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Graduate Theses and Dissertations Graduate School

November 2019

Multimodal Treatment and Neoadjuvant Chemotherapy Trends, Multimodal Treatment and Neoadjuvant Chemotherapy Trends,

Utilization and Survival Effects in Intrahepatic Utilization and Survival Effects in Intrahepatic

Cholangiocarcinoma – a Propensity Score Analysis Cholangiocarcinoma – a Propensity Score Analysis

Ovie Utuama University of South Florida

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Multimodal Treatment and Neoadjuvant Chemotherapy Trends, Utilization and Survival

Effects in Intrahepatic Cholangiocarcinoma – a Propensity Score Analysis

by

Ovie Utuama

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy with a concentration in Epidemiology

College of Public Health

University of South Florida

Co-Major Professor: Aurora Sanchez-Anguiano, Ph.D Co-Major Professor: Jennifer Permuth, Ph.D

Getachew Dagne, Ph.D Amy Alman, Ph.D Daniel Anaya, MD

Date of Approval: November 1, 2019

Keywords: curative-intent surgery, pre-operative therapy, biliary cancer, elderly

Copyright © 2019, Ovie Utuama

DEDICATION

It takes a village to raise a child and as such this body of work is dedicated to the first and most

profound of my teachers -- my parents, Prof. Amos and Dr. Nelly Utuama -- who sowed the

seeds of curiosity and set me off on my life-long pursuit of knowledge. This endeavor would not

have been possible without your example and support.

To my precious wife, Ona, for embarking on this journey with me. Now that I’m all grown up, I

promise to be a responsible adult.

To Rovu, Rume and Ome, daddy loves you. May this work serve as a testament to striving to be

your best selves and leaving an indelible mark on the world, for the good of all humanity.

ACKNOWLEDGEMENTS

Though my instructors are many, all of whom important in shaping me, it is to my committee I

must now turn in gratitude. Dr. Sanchez-Anguiano, my longest collaborator, for life and

professional advice that helped center me during times of apparent stagnation. Dr. Permuth, for

demystifying the Ph.D., taking me by the hand, operationalizing and optimizing the process.

Your energy and can-do attitude are simply infectious! Drs. Dagne and Alman, for insight and

conversations that brought much clarity and encouragement. Dr. Anaya, for pushing the

boundaries of an idea, a question, a hypothesis. You have made me a better researcher.

I would be remiss to not mention Dozie and Bashir, who generously provided vital real estate in

Tampa, at a time when I couldn’t afford it, that enabled meaningful progress after many months

of vacillation. Fahad Mansuri, thank you for being the first guest at my dissertation defense. Dr.

Schwartz, you are a kindred spirit with whom I have enjoyed our many musings. Thank you for

always believing in me! To Jane, Donna, Chassity and Diana, for all the quiet but essential work

that you do that keep the cogs of the College and Gastrointestinal Oncology Department at

Moffitt turning – a warm and heartfelt thank you!

i

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... iii

LIST OF FIGURES....................................................................................................................... iv

ABSTRACT .................................................................................................................................. vi

CHAPTER 1: STATEMENT OF PROBLEM AND RATIONALE ..................................................... 1

CHAPTER 2: CURATIVE-INTENT SURGERY FOR ELDERLY PATIENTS WITH NON- METASTATIC INTRAHEPATIC CHOLANGIOCARCINOMA ................................................... 4

Abstract ........................................................................................................................... 4 Introduction ...................................................................................................................... 5 Methods .......................................................................................................................... 6

Study population and study design ....................................................................... 6 Inclusion/exclusion criteria ................................................................................... 6 Intervention/exposure........................................................................................... 7 Outcomes ............................................................................................................7 Covariates ............................................................................................................8 Propensity score development .............................................................................8 Statistical analysis ................................................................................................9

Results .......................................................................................................................... 10 Trends and patterns of treatment over time ........................................................ 10 Predictors of curative -- intent surgical treatment ................................................ 10 Survival analysis ................................................................................................ 11

Discussion ..................................................................................................................... 12 Supplemental Results .................................................................................................... 29

CHAPTER 3: NEOADJUVANT CHEMOTHERAPY FOR PATIENTS WITH INTRAHEPATIC CHOLANGIOCARCINOMA ........................................................................ 33

Abstract ......................................................................................................................... 33 Introduction .................................................................................................................... 34 Methods ........................................................................................................................ 35

Study design ...................................................................................................... 35 Inclusion/exclusion criteria ................................................................................. 35 Intervention – neoadjuvant chemotherapy .......................................................... 36 Outcomes .......................................................................................................... 36 Covariates .......................................................................................................... 36 Propensity score development ........................................................................... 37 Statistical analysis .............................................................................................. 38

ii

Results ..........................................................................................................................39 Neoadjuvant chemotherapy utilization – trends and predictors ...........................39 Survival analysis ................................................................................................40

Discussion .....................................................................................................................41

Supplemental Results ....................................................................................................58

CHAPTER 4: CONCLUSION AND RECOMMENDATIONS ......................................................62

REFERENCES .........................................................................................................................65

iii

LIST OF TABLES

TABLE 2.1 Sociodemographic, hospital, tumor and treatment characteristics of ICC

patients, n=3,653 ...............................................................................................17

TABLE 2.2 Predictors of surgical treatment ..........................................................................24

TABLE 2.3 Crude and propensity score (PS) stratified-adjusted Cox models for

overall survival- age and treatment effects examined .........................................28

TABLE S2.1 Standardized mean differences by propensity score strata and treatment

group..................................................................................................................30

TABLE 3.1 Baseline sociodemographic, hospital, clinical and tumor

characteristics of patients with non-metastatic intrahepatic

cholangiocarcinoma, treated with surgery, by treatment strategy

(n=881) ..............................................................................................................46

TABLE 3.2 Multivariable logistic regression analysis examining predictors of

neoadjuvant chemotherapy utilization (n=881) ...................................................52

TABLE 3.3 Results from Cox regression models examining the effect of neoadjuvant chemotherapy on survival ..............................................................57

TABLE S3.1 Baseline characteristic of patients in the neoadjuvant and no- neoadjuvant groups, from the propensity score matching – adequacy of matching expressed in p values and standardized differences .......................59

iv

LIST OF FIGURES

FIGURE 2.1 Treatment trend for stages 1-3 ICC patients, n=3,653 ........................................ 20

FIGURE 2.2A Treatment utilization for stages 1-3 ICC patients, n=3,653 .................................. 21

FIGURE 2.2B Treatment utilization for stages 1-3 ICC in patients < 70 years, n=2,337 ............ 22

FIGURE 2.2C Treatment utilization for stages 1-3 ICC in patients ≥ 70 years, n=1,316 ............ 23

FIGURE 2.3A KM plots for stages 1-3 ICC patients, n=3,651 ................................................... 25

FIGURE 2.3B KM plots for stages 1-3 ICC patients less than 70 years, n=2,337 ..................... 26

FIGURE 2.3C KM plots for stages 1-3 ICC patients at least 70 years, n=1,315 ........................ 27

FIGURE S2.1 Inclusion and exclusion criteria of study participants, NCDB (2004-2014) .......... 31

FIGURE S2.2 Treatment trend for stages 1-4 ICC patients, n=21,377 ..................................... 32

FIGURE 3.1A Annual proportion of patients with intrahepatic cholangiocarcinoma receiving neoadjuvant chemotherapy, in participating NCDB hospitals (n=881) ...............................................................................................50

FIGURE 3.1B Trend over time for neoadjuvant chemotherapy utilization among 26 hospitals with consistent patient reporting through study period (n=340) ............ 51

FIGURE 3.2A Unadjusted Kaplan-Meier curves depicting overall survival estimates for all patients with non-metastatic, intrahepatic cholangiocarcinoma by treatment strategy (n=881) ............................................................................54

FIGURE 3.2B Unadjusted Kaplan-Meier curves depicting overall survival estimates for patients with locally advanced stages (Stages II-III), intrahepatic cholangiocarcinoma - by treatment strategy (n=414) .......................................... 55

FIGURE 3.2C Unadjusted Kaplan-Meier curves depicting overall survival estimates for patients with early stage (Stages I), intrahepatic cholangiocarcinoma by treatment strategy (n=465) ............................................56

v

FIGURE S3.1 Study flow chart for selection criteria of patients with intrahepatic cholangiocarcinoma – NCDB (2006-2014) .........................................................58

FIGURE S3.2 Histogram illustrating the distribution of patients in each of the propensity score strata, by treatment group (n=881) .....................................................................60

FIGURE S3.3 Utilization of neoadjuvant chemotherapy among all ICC patients, n=21,018 ......61

vi

ABSTRACT

Intrahepatic Cholangiocarcinomas (ICC) are fatal malignancies common among the elderly.

Patients are diagnosed late and often relapse, even after curative-intent surgery (CIS). In this

context, additional systemic chemotherapy (multimodal treatment) is recommended for most

patients but reported survival benefits are minimal and are limited to small single institutional

studies. For this reason, using a national cancer registry, we sought to characterize multimodal

treatment trends and utilization in general and neoadjuvant chemotherapy, specifically, as well

as evaluate their survival effects among the larger population. We hypothesized that, 1). Elderly

ICC patients would have survival benefits equivalent to younger patients, 2). Neoadjuvant

chemotherapy (NC) provides improved survival over adjuvant chemotherapy or surgery alone.

Study participants were selected from the National Cancer Database (NCDB), a hospital-based

cancer registry which accounts for 70% of newly diagnosed cancer cases in the United States

annually. We examined trends from 2004 through 2014 using Cochran-Armitage trend tests,

identified independent predictors of multimodal treatment using logistic regression models,

evaluated survival using univariate KM plots with log-rank tests and adjusted for confounders

using propensity-score stratified and matched multivariable Cox regression models. Survival

benefit in elderly versus young patients was no different for CIS (HR 1.14 [0.92-1.41]) and for

CIS-multimodality treatment (1.35 [0.91-2.01]). NC utilization was associated with improved OS

(HR: 0.78 [95%CI 0.54-1.11]. Elderly patients were less likely to receive CIS than younger ones

but had an equivalent survival when treated. This study also demonstrated that patients with

more advanced disease may benefit from a multimodal approach using NC.

1

CHAPTER 1: STATEMENT OF PROBLEM AND RATIONALE

The prognosis of intrahepatic cholangiocarcinoma (ICC) has remained unchanged for more than

40 years. 1,2 Although a rare cancer with less than 10,000 cases annually in the United States, 3

together with hepatocellular carcinoma they represent the fourth largest cause of cancer

mortality with an ongoing and largely unexplained rise in incidence.4,5 Late disease presentation,

non-specific symptoms, non-specific disease markers and an absence of sensitive screening

tools have contributed to the fatality of the condition.6-8 Extensive surgery is the mainstay for

potential cure. 9,10

There is mounting evidence that surgery in combination with chemo- or radio-therapy, so-called

multimodal therapy, may be beneficial in late stage, non-metastatic ICC.11-13 However, until

recently only single patient case reviews and small observational studies from single institutions

have supported this benefit.14-16 Observational studies have been particularly subject to

selection biases in which early stage ICC is commonly treated with surgery alone while later

stage disease with multimodal therapy, making comparisons of the survival effects of

multimodal therapy and surgery alone confounded by disease stage. Additionally, the treatment

experience of elderly ICC patients, who make up more than half of all those diagnosed with the

disease, is unclear. 17-19 Against the backdrop of older cancer patients receiving substandard

care compared to younger ones, there is controversy about whether the former respond to

recommended treatment as well as young patients. Furthermore, the timing of the

administration of systemic chemotherapy in relation to surgery increasingly suggests that

neoadjuvant chemotherapy (chemotherapy before surgery) may be more beneficial than the

currently recommended adjuvant chemotherapy (chemotherapy after surgery).14,20

2

Therefore, the aims and objectives of the present dissertation are as follows.

Aim 1: To characterize the use and survival effects of multimodal therapy among elderly

ICC patients. We hypothesize that multimodal therapy has the same survival effect on older

patients as younger patients.

Aim 1 objectives were to, a) describe the utility and trends of multimodal therapy, b) elucidate

the predictors of multimodal therapy, and c) evaluate the survival effects of multimodal therapy

among the elderly in relation to younger patients.

Aim 2: To characterize the use and survival effects of neoadjuvant chemotherapy among

ICC patients. We hypothesize that the neoadjuvant chemotherapy approach has greater

survival effect than the adjuvant approach. Aim 2 objectives, therefore, were to, a) describe the

utility and trends of neoadjuvant chemotherapy, b) elucidate the predictors of neoadjuvant

chemotherapy, and c) evaluate the survival effects of neoadjuvant chemotherapy in relation to

non-neoadjuvant therapy.

To evaluate our hypotheses and objectives, we used the National Cancer Database (NCDB) as

a data source. The NCDB is a hospital-based cancer registry instituted in 1989 by the American

College of Surgeons and the American Cancer Society which tracks cancer patients, their

treatment and outcomes and now represents more than 70 percent of newly diagnosed cancer

cases annually, sourced from more than 1500 hospitals nationwide. 21 From 2004 through 2014,

the NCDB accumulated 23,273 cases of intrahepatic biliary malignancies which constitute the

source population for the retrospective cohort study design used throughout the dissertation.

This research is significant because it is the first to aggregate large numbers of incident ICC-

only patients with the aims of examining the association of multimodal therapy among the

elderly and neoadjuvant chemotherapy use and survival.

3

It is innovative because it has extensively employed causal inference techniques to adjust for

selection biases common in cancer treatment studies. This research hopes to present a

rigorous characterization of multimodal therapy (of which neoadjuvant chemotherapy is a

special case) and valid estimates of its survival effects that will advance our understanding of

best treatment practices for ICC, as an important step towards improving survival outcomes of

this lethal malignancy.

4

CHAPTER 2: CURATIVE-INTENT SURGERY FOR ELDERLY PATIENTS WITH NON-

METASTATIC INTRAHEPATIC CHOLANGIOCARCINOMA

Abstract [335 words]

Introduction: The incidence of intrahepatic cholangiocarcinoma (ICC) is increasing with most

patients (>50%) presenting over the age of 70 years. The use of curative-intent surgery

(hepatectomy - CIS) in elderly patients with ICC is currently unknown, and the survival benefit of

CIS for this population is unclear.

Methods: A retrospective cohort study of patients in the National Cancer Data Base with a

diagnosis of ICC was performed (2004-2014). A landmark approach was used to define the final

study cohort. Patients were categorized by age into young (<70) and elderly (≥70). Trends over

time for utilization of different treatment approaches were evaluated (CIS, CIS-multimodality,

other, no treatment). Using propensity score stratification and cox regression analysis, we

examined the association between treatment strategy and overall survival (OS), including

interaction for age and treatment type.

Results: Among the 3,653 patients in the study cohort, 1,316 (36%) were elderly. CIS was

performed in only 19% of patients, though its use increased over time for the whole population

(trend test, P=0.007). In the elderly group, there was a significant decrease over time in the “no-

treatment” group, primarily driven by an increase in the “other” treatment category (P<0.001).

On multivariable logistic regression, age≥70 was a predictor of not receiving CIS (OR 0.68

[0.56-0.81]; P<0.001). Median OS was significantly higher for those receiving CIS and CIS-

multimodality treatments as compared to those in the other and no-treatment groups (median

OS 46.5, 47.9, 25.3, and 16.3 months, respectively; logrank, P<0.001). Following propensity

5

score stratification, relative to no treatment, CIS and CIS-multimodality were each associated

with lower risk of death for the whole cohort (HR 0.28 [0.25-0.32] and 0.32 [0.27-0.38],

respectively), and for the elderly population (0.33 [0.27-0.40] and 0.42 [0.29-0.60], respectively).

Further, the survival benefit in elderly versus young patients was no different for CIS (HR 1.14

[0.92-1.41]) and for CIS-multimodality treatment (1.35 [0.91-2.01]).

Conclusion: Patients over the age of 70 years are less likely to receive surgical treatment for

ICC, despite having a significant survival benefit from curative-intent surgery (hepatectomy),

equivalent to that observed by younger patients.

Introduction

Intrahepatic cholangiocarcinomas (ICC) are typically adenocarcinomas arising from the biliary

ducts and ductules within the liver. ICC accounts for 10-15% biliary/hepatic malignancies and

represents the second commonest form of liver cancer. 5,22 In the United States, incidence is

approximately 1-2 per 100,000 persons, with a male preponderance of new cases. 22 About

5000-8000 Americans are diagnosed annually with a peak age between the fifth to seventh

decades of life. 5 Hispanic and Asian populations are reported to have the highest rate of

incidence (up to 3.3 cases per 100,000 persons) while non-Hispanic whites and African-

Americans have the lowest rates, both estimated at 2.1 cases per 100,000 persons. 23 Mortality

rates are highest among American Indian and Alaskan Natives (1.3-1.4 per 100,000) and lowest

among African Americans (0.7 per 100,000). 23,24 Despite its relative rarity, ICC remains of public

health significance because it is one of a few cancers with a five-year survival rate below 10%

and whose incidence is on the rise in many western countries, often without known risk

factors.25

Historically, there is abundant evidence that the elderly have not benefitted as much from

advances in cancer treatment and that they receive more substandard care than the young. 28,29

6

The reasons for this may be related to more comorbidities, reduced organ functional capacity,

perception of increased toxicities to systemic therapies among the elderly, and fewer treatment

guidelines for them. 30,31 More recently, several studies among patients with hepatobiliary

cancers have demonstrated that the elderly can achieve similar survival benefits as young

patients when stage-appropriate and patient-specific treatment is instituted. 32,33 There are only

a few studies investigating the treatment experience of the elderly patients with ICC. 17 We

therefore sought to characterize ICC treatment in the United States and investigate its survival

benefits among elderly patients.

Methods

Study population and study design

Using a retrospective cohort design, we abstracted data corresponding to ICC patients

diagnosed between January 1, 2004 through December 31, 2014 from the National Cancer

Database (NCDB). The NCDB is a hospital-based cancer registry that was created in 1989 and

maintained by the American College of Surgeons and the American Cancer Society. About

1500 Commission on Cancer (CoC) approved hospitals contribute to the registry annually,

representing more than 70% of all incident cancers reported in the United States. Case finding

and data collection items and procedures are detailed elsewhere. 34 The NCDB aggregates

anonymized patient information on tumor characteristics, disease stage and treatment with

additional masking of patient residential zip codes and hospital names. The current study did

not involve attempts to contact patients, identify them or link the database, or portions thereof,

with other data sources. This study was approved by the Moffitt Cancer Center and University of

South Florida IRB committee.

Inclusion/exclusion criteria

ICC cases were identified using the third edition of the WHO manual on the International

Classification of Diseases for Oncology, ICD-O-3, topographic (C22.1) and morphologic codes

7

(8140, 8160, 8255, 8260, 8453, 8480, 8481, 8503). Only patients who were diagnosed with

primary ICC, seen in one CoC hospital, and were worked up for treatment by the reporting

hospital were included in the analysis. We excluded stage 4 patients, patients with carcinoma-

in-situ and those without survival information, clinical stage or malignant tumor behavior.

Importantly, to limit the effects of survival bias among those who received treatment, we

additionally excluded all those who died within 90 days of diagnosis - landmark approach

(Supplemental Figure 2.1).

Intervention/exposure

Four intervention categories were defined among patients and served as the main exposures.

There were patients who received potential curative surgery alone; curative surgery with at least

chemotherapy or radiation of some form; other treatments, primarily chemotherapy with or

without external beam radiation, among others; and no treatment (no surgery, chemotherapy or

radiation therapy reported). Curative surgery was defined as liver resection with or without bile

duct excision in a patient who did not receive palliative care. No distinction was made among

patients who underwent surgery and received neoadjuvant or adjuvant chemotherapy.

Whenever the treatment groups required dichotomization, both surgical-based categories were

compared against those in whom patients did not receive any of the major interventions or

received other non-surgical treatment.

Outcomes

Overall survival was the primary outcome of interest and was measured in months from

diagnosis until death or date of last follow-up. Secondary outcomes included treatment

utilization using the categories listed above. Trends and patterns of treatment were

examined over time, and predictors of curative intent surgery were evaluated with logistic

regression.

8

Covariates

Age was an effect modifier and defined as a binary variable, less than 70 and equal to or

greater than 70 years of age. The year of diagnosis ranged from 2004 and 2014. Age was

dichotomized at the median age distribution of ICC for the ease of interpretation of results.

Patients were also categorized by race (white, black, others), comorbidity index (scores 0, 1, 2,

≥ 3) and primary type of insurance (no insurance, private, government). Other patient

characteristics, which were aggregated at the zip code level, included percent of residents with

a high school diploma (< 14%, 14-19.99%, 20-28.9%, ≥29%), median household income (<

$30,000, $30,000-34,999, $35,000-45,999, ≥$46,000 residency classification (urban, rural)

distance from residential zip code centroid to reporting hospital (miles). Hospitals were classified

by type (community cancer program, academic/research program, integrated network program)

and region (northeast, south, midwest, west) in which they were located.

Tumor characteristics were defined by grade and clinical stage. During the study period, the

sixth and seventh editions of the American Joint Committee on Cancer (AJCC) staging

conventions were used, the latter being adopted from 2010 onwards. To minimize bias from

differential staging, all staging information available was modified according to a system

proposed by Meng and colleagues. 35

Briefly, stages 1 and 2 were left unmodified; stages 3 and 3a of the sixth edition were recoded

as 3a while 3b remained unmodified; stages 4a of the seventh edition was recoded as 3b, with

4b remaining as stage 4; stage 4 of the sixth edition remained unmodified in so far as

metastasis was additionally documented.

Propensity score development

Propensity score stratification of all study participants was undertaken because there was a

need to preserve as many patients within the four treatment categories as possible. The choice

9

of baseline variables that were regressed on the odds of undergoing curative surgery were

guided in part by their level of prediction of surgical treatment and their identification in the

literature as confounders of the treatment-survival association, as well as being documented

predictors of survival in the absence of a relationship to treatment. We chose region, hospital

type, year of diagnosis, primary insurance, median household income, residency, Charlson

comorbidity index and clinical stage as baseline covariates for the generation of propensity

scores. The quality of the propensity score model was judged based on graphical demonstration

of the common support region between treatment groups, absolute standardized mean

differences of 0.25, and their associated p-values.

Statistical analysis

Sociodemographic, tumor and treatment characteristics were stratified by age and significance

testing with Chi-Square tests performed. Overall and age-group trend and treatment type

utilization over the study period were graphically displayed and tested for significance using the

Cochran-Armitage trend and Chi-Square tests, respectively. Logistic regressions models were

used to identify independent predictors of surgical treatment and to generate propensity scores

(probabilities) associated with receiving surgical treatment. Kaplan Meier survival plots stratified

by treatment categories were performed for the overall study sample, and for patients by age

group. Log rank tests were used to assess for differences in survival estimates across treatment

categories. A propensity score-stratified Cox model of treatment categories, age-groups and an

age-treatment interaction term was performed to evaluate hazard ratios (HRs) and estimate

95% confidence intervals (CIs) across levels of treatment and age. Only pooled HRs over the

five propensity-score strata were reported. All Cox models accounted for clustering of survival

among patients with the same facility by estimation of robust variance estimators; all analysis

was performed using SAS version 9.4 (SAS Institute, Cary, NC) and a type 1 error rate of

0.05% was specified a-priori.

10

Results

A total of 3,653 study participants were available for analysis, of which 1,316 (36%) were at

least 70 years of age (the elderly). The elderly were more likely than younger patients to seek

care in a community cancer program (33.9% elderly vs. 25.6% younger, p-value=<0.0001), to

present with earlier stage disease (56% elderly with stages 1 and 2 disease vs. 50% younger

with stages 1 and 2 disease, p-value=<0.0001), to not receive treatment (34.1% elderly vs

19.5% younger, p-value <0.0001) and to report some form of government insurance as the

primary source of payment (86.2% elderly vs. 37.0% younger). When elderly patients received

treatment, surgery-based multimodal treatment (with systemic chemo or radiation) was less

likely (3.9% elderly vs. 8.4% younger, p <0.0001) (Table 2.1).

Trends and patterns of treatment over time

Overall trends of surgery-based treatment demonstrated an increase from 2004 to 2010 (20% to

35% utilization), followed by a gradual decline (Figure 2.2a). When utilization was categorized

by the four treatment types and followed over time, other treatment use, such as chemotherapy

alone or in combination, increased from 42% in 2004 to 55% in 2014, while the level of patients

who did not receive treatment during the same period fell from 39% to 23%. The elderly

compared to younger patients experienced the largest drop in proportions of not treated from

the start of the study (56% old vs. 27.5% young) to study’s end (30% old vs. 17% young),

despite a third of them not receiving any of the major types of treatment. Conversely, over the

study duration, receipt of other treatment by the elderly went up from 30% to 45% while among

the younger patients from 49% to 59% (Figures 2.2b and 2.2c).

Predictors of curative-intent surgical treatment

After controlling for all other variables in a multivariable logistic regression, the elderly remained

less likely to receive any surgery-based form of treatment (OR=0.68, 95%CI: 0.56-0.81; p-value

11

<0.0001) as did patients diagnosed with later stages when compared to stage 1 disease

(OR=0.53, 0.43-0.64 stage 2; OR=0.27, 0.21-0.35 stage 3A; OR=0.17, 0.14-0.22 stage 3B; p-

value < 0.0001). Additionally, hospitals affiliated with academic/research and integrated cancer

programs were more likely to perform surgery-based treatment than community cancer

programs (OR=2.54, 2.09-3.08, OR=1.76, 1.30-2.40, respectively; p<0.0001) (Table 2.2).

Survival analysis

Overall median follow-up and survival of all study participants was 48.2 and 15.8 months,

respectively. There was a statistically significant difference in overall survival between treatment

groups (median OS: surgery alone 37.8 months, surgery/multimodality 33 months, other 14.4

months, and no treatment 9.2 months; p<0.01 – Figure 2.3a); this association persisted when

stratified by age group (Figures 2.3b-c).

When survival was examined by age-group using the propensity-score stratified model, the

hazard ratios among the younger treated age-group were similar among the corresponding

elderly treated when compared to their respective untreated peers (HR=0.36 [95%CI 0.31-0.43]

younger vs. HR= 0.33 [0.27-0.40] elderly for curative surgery; HR=0.39 [0.31-0.48] vs. HR=0.42

[0.29-0.60] for surgery-based multimodal therapy; HR=0.76 [0.68-0.87] vs. HR=0.75 [0.65-0.87]

for other treatment). When survival was further examined within treatment categories, no

significant survival difference was observed between the elderly and younger patients among

those who received surgery alone (HR=1.14 [0.92-1.41]) and among those who received

surgery-multimodality treatment (HR=1.35 [0.92-2.01]) (Table 2.3).

Propensity scores were generated for 3,431 (93.9%) patients, of which 941 patients underwent

surgery with or without additional chemotherapy or radiation (the treated, for propensity-score

generation purposes).

Among the treated, propensity scores were computed for 887 (94.2%) patients. Propensity

scores were grouped into non-overlapping quintiles, each of which contained approximately 177

12

treated patients and a variable number of control patients. Except for the hospital type variable

(absolute standardized mean difference=0.279, p-value=0.00) and stage 1 patients (p-

value=0.03) in stratum 1, treated and control groups were balanced within the standardized

mean difference and p-value boundaries for negligible differences (Supplemental Table 2.1).

Discussion

Using a large hospital-based cancer registry and employing a quasi-experimental design for the

purposes of causal inference, we examined treatment trends and evaluated average treatment

effects of various therapeutic modalities among ICC patients. During the study period, we

demonstrated a reduction in the proportion of patients who did not receive ICC-directed

treatment, especially among the elderly. In an age-stratified analysis, we observed that all

treatment types conferred significant survival benefits and there were no differences in overall

survival in a side by side comparison of elderly and younger patients, a result that underscores

the importance of instituting treatment in the elderly. Crucially, when we directly compared

younger patients to elderly ones stratified by treatment, we found neither clinically meaningful

nor statistically significant differences in overall survival among those who underwent curative

surgery alone. This finding suggests that the elderly with early-stage ICC disease should be

offered aggressive curative intention surgery based on already established criteria routinely

instituted in younger patients.

In assessing the role of multimodal treatment among the elderly, we observed a 35% increase in

the hazard rate among those who had received surgery in combination with another treatment

type, when compared to younger patients. This effect was larger than either that of elderly

patients receiving no treatment or receiving chemotherapy-based multimodal therapy, even as

the latter treatment is typically associated with later stage disease.

13

While the reason for this is not exactly known, it appeared to be related to the nature of the

disease treated by surgery-based multimodal therapy and not the treatment itself, as older and

younger ICC patients, respectively, experienced substantial benefit from it when compared to

their peers who did not receive any treatment. As potentially curative surgery is combined with

systemic chemotherapy in early stage disease with invasive features, this variant of ICC may

represent an unusually aggressive phenotype to which the elderly with limited functional organ-

system reserves are naturally more susceptible.

Unfortunately, the current results also corroborate many other studies that have reported

substandard care among elderly cancer patients. 32,33,36,37 We identified several indications of

this. First, despite an overall decline in the proportion of untreated elderly patients during the

study period, as many as a third remained untreated. Second, although elderly ICC patients

were diagnosed at an earlier stage than younger patients, there was no attendant increase in

the use of early stage surgery-based treatment. Instead, a large increase in the receipt of

chemotherapy-based treatment was found. Third, the older patients were more likely to seek

care in hospitals associated with community cancer programs, which we demonstrated were

less likely to offer surgical treatment. These findings were consistent with those of a large

population-based study among elderly patients with rectal cancer. 36 That study observed that

older rectal cancer patients were less likely to receive aggressive radical surgery and when

surgery was performed, it often led to local tumor excision without the recommended removal

of regional lymph nodes. In another large study of elderly patients with hepatocellular

carcinoma, a similar finding of reduced likelihood of surgical resection and increased receipt of

transarterial chemoembolization for unresectable cancer was reported, despite the elderly

having smaller and fewer multiple tumors. 38 Lastly, in one of the few large ICC studies, older

patients who had undergone surgery were less likely to receive adjuvant chemotherapy or

radiotherapy. 17

14

The decision to treat the elderly cancer patient is complicated. Several provider and patient

factors play an important role in the decision-making process. Limited treatment guidelines for

the elderly exist due to underrepresentation in clinical trials, the presence of comorbidities, and

pharmacist and physician perception of increased treatment toxicity among the elderly. Other

factors that may impede optimal care are poor life expectancy, time constraints, the focus of

multidisciplinary teams on cancer pathology only, the lack of physician experience with geriatric

care and/or lack of capacity for referral to relevant specialists. 30,39,40 Specifically, physician

expertise and clinicians in academic institutions have been identified as more likely to offer

personalized, multimodal treatment to cancer patients, observations that may explain, in part,

lower receipt of surgery-based treatment in hospitals affiliated with community cancer programs.

41 Much less studied patient-level factors that may also affect treatment decisions include trust in

physicians, the level and quality of communication with physicians, presence of cognitive

impairment, health literacy and numeracy. However, we were unable to explore reasons for

non-treatment in our study beyond the explanation that a specific form of therapy was not part of

the first-line of treatment.

The NCDB has several limitations. First, it reports only all-cause deaths and we were therefore

unable to demonstrate ICC-specific survival. The elderly are more likely to die than the young at

any given time in the larger population, and among our study participants, we estimated that the

elderly had an instantaneous background death rate 20-25% times faster than the young at any

given time point, irrespective of treatment. The implications of this are that our treatment-

stratified survival estimates would be biased in favor of younger patients; the reported HRs may

therefore represent an upper limit of disease-specific mortality rate ratios. We attempted to limit

this bias to that which would occur in a healthy aging population by accounting for the severity of

comorbid conditions among study participants by including the Charlson Comorbidity Index in

15

the propensity score model. Second, the NCDB may not be representative of patients who

reside in rural areas and are diagnosed with early stage disease in physician offices, as CoC

hospitals tend to be clustered in metro and urban areas.

Additionally, propensity score techniques have their own limitations. Most notably, they are

unable to adjust for unmeasured variables and our results are therefore subject to bias from

potential confounders not included in the propensity score model. Furthermore, we were unable

to include 6% of those who received surgical treatment in our results as propensity scores were

not generated for them. While the effect of this on the validity of our results is unknown, we

believe it is negligible.

On the other hand, the present study leverages the strengths of the NCDB in a way that few

others examining cholangiocarcinoma (CC) have. We have aggregated one of the largest

patient cohorts of ICC and excluded extrahepatic cholangiocarcinomas (ECC). Other studies

have investigated CC as a single entity, therefore obscuring prognostic insights into the

heterogenous group of diseases. 42 The well-established observation of the rising incidence of

ICC in developed countries and the unassociated decline of ECC incidence suggest, at a

minimum, both groups of CC have different etiologic drivers. 43 We have been careful to

minimize several potential biases common to retrospective cohort studies assessing cancer

treatment. First, we deduplicated ICC cases by restricting analysis to patients who sought care

in one CoC hospital. Second, we excluded patients with multiple primary tumors preventing

confounding by use of different treatment regimen. Third, by excluding patients who died within

90 days of diagnosis we minimized survival bias. Fourth, by adopting a standard staging

definition across the entire study duration, we mitigated the differential exclusion of stage 3

patients with lymph node invasion that would have been reclassified as stage 4 in the seventh

AJCC edition from 2010 onwards. Had the seventh edition AJCC staging remained unmodified,

this would have artificially biased reported HRs towards the null. Fifth, by including stage in

16

generating our propensity scores, we mitigated the effects of confounding by indication as

stage is both a proxy for treatment and survival outcomes.

In conclusion, we demonstrated that a substantial proportion of elderly ICC patients either

received no treatment or were undertreated. When they did receive treatment, after adjusting for

stage, year of diagnosis, comorbidities, and other potential confounders using propensity score

stratification for the purpose of causal inference, they experienced treatment benefits equivalent

to younger patients for all treatment types. Surgery-based treatment, alone or in combination,

offer the best survival benefit for the elderly with non-metastatic disease. However, there remain

institutional barriers to optimal treatment of elderly ICC patients that need to be overcome.

17

Table 2.1. Sociodemographic, hospital, tumor and treatment characteristics of ICC

patients, n=3,653.

CHARACTERISTICS All

N=3,653 < 70 years

n=2,337 ≥ 70 years

n=1,316

*P

n (%)

Region 0.0001

Northeast 914 (25) 531 (22.7) 383 (29.1)

South 1271 (34.8) 820 (35.1) 451 (34.3)

Midwest 900 (24.6) 606 (25.9) 294 (22.3)

West 568 (15.5) 380 (16.3) 188 (14.3) Hospital Type <.0001

Community cancer program

1044 (28.6)

598 (25.6)

446 (33.9)

Academic/research program 2269 (62.1) 1531 (65.5) 738 (56.1)

Integrated network program 340 (9.3) 208 (8.9) 132 (10.0) Year of Diagnosis 0.6493

2004

119 (3.3)

73 (3.1)

46 (3.5)

2005 128 (3.5) 84 (3.6) 44 (3.3)

2006 141 (3.9) 103 (4.4) 38 (2.9)

2007 212 (5.8) 131 (5.6) 81 (6.2)

2008 279 (7.6) 178 (7.6) 101 (7.7)

2009 327 (9.0) 207 (8.9) 120 (9.1)

2010 436 (11.9) 275 (11.8) 161 (12.2)

2011 430 (11.8) 275 (11.8) 155 (11.8)

2012 469 (12.8) 289 (12.4) 180 (13.7)

2013 502 (13.7) 327 (14.0) 175 (13.3)

2014 610 (16.7) 395 (16.9) 215 (16.3) Sex 0.0025 Male 1754

(48.0) 1166

(49.9) 588

(44.7)

Female 1899 (52.0)

1171 (50.1)

728 (55.3)

Race <.0001

White 3045 (83.4)

1899 (81.3)

1146 (87.1)

Black 293 (8.0)

226 (9.7)

67 (5.1)

Others 315

(8.6) 212

(9.1) 103

(7.8)

18

Table 2.1. (Continued).

Primary Insurance <.0001

No Insurance 247 (6.8)

205 (8.8)

42 (3.2)

Private 1408 (38.5)

1268 (54.3)

140 (10.6)

Government Insurance 1998

(54.7) 864

(37.0) 1134

(86.2) % without HSD 0.1104

Missing 144 (3.9)

101 (4.3)

43 (3.3)

>=29% 622

(17.0) 411

(17.6) 211

(16.0) 20-28.9% 778

(21.3) 511

(21.9) 267

(20.3) 14-19.9% 787

(21.5) 503

(21.5) 284

(21.6) < 14% 1322

(36.2) 811

(34.7) 511

(38.8) Median Household Income 0.0863

Missing 143 (3.9)

100 (4.3)

43 (3.3)

< $30,000 484 (13.2)

320 (13.7)

164 (12.5)

$30,000 - $34,999 607

(16.6) 407

(17.4) 200

(15.2) $35,000 - $45,999 919

(25.2) 564

(24.1) 355

(27.0) $46,000 + 1500

(41.1) 946

(40.5) 554

(42.1) Residency Type 0.1052

Missing 144 (3.9)

91 (3.9)

53 (4.0)

Metro 2928

(80.2) 1857

(79.5) 1071

(81.4) Urban/Rural 581

(15.9) 389

(16.6) 192

(14.6) Charlson Comorbidity Index 0.0002

0 2557 (70.0)

1683 (72.0)

874 (66.4)

1 755 (20.7)

441 (18.9)

314 (23.9)

2 205 (5.6)

118 (5.0)

87 (6.6)

≥3

136 (3.7)

95 (4.1)

41 (3.1)

Tumor Grade 0.0009

Missing 1892 (51.8)

1162 (49.7)

730 (55.5)

Well Differentiated, Differentiated, NOS 204 (5.6)

125 (5.3)

79 (6.0)

Moderately Differentiated, Moderately Well Differentiated, Intermediate Differentiation

874 (23.9)

604 (25.8)

270 (20.5)

Poor/Differentiated 683 (18.7)

446 (19.1)

237 (18.0)

19

Table 2.1. (Continued).

Clinical Stage <.0001

1 1049 (28.7)

607

(26.0)

442 (33.6)

2 856 (23.4)

560 (24.0)

296 (22.5)

3A 515 (14.1)

324 (13.9)

191 (14.5)

3B 1233 (33.8)

846 (36.2)

387 (29.4)

Treatment Type <.0001

No Surgery, No Chemo, No Radiation 905

(24.8) 456

(19.5) 449

(34.1) Curative Surgery Alone 694

(19.0) 445

(19.0) 249

(18.9) Curative Surgery +/- Chemo 247

(6.8) 196

(8.4) 51

(3.9) Other (Chemo Alone, External Beam Radiotherapy +/- Chemo, Etc)

1807 (49.5)

1240 (53.1)

567 (43.1)

*Chi-Square test p-value

20

Figure 2.1. Treatment trend for stages 1-3 ICC patients, n=3,653. Cochran-Armitage: p

= 0.0072

21

Figure 2.2a. Treatment utilization for stages 1-3 ICC patients, n=3,653. Chi-square: p<0.001

22

Figure 2.2b Treatment utilization for stages 1-3 ICC in patients < 70 years, n=2,337.

Chi- square: p<0.001

23

Figure 2.2c. Treatment utilization for stages 1-3 ICC in patients ≥ 70 years, n=1,316.

Chi- square: p=0.0001

24

Table 2.2. Predictors of surgical treatment.

Unadjusted Adjusted*

Odds Ratio

95% CI

Odds Ratio

95%CI

**P

Age ≥ 70 years

0.67

0.58

0.77

0.68

0.56

0.81

<.0001

Region: midwest vs west 1.69 1.32 2.16 1.51 1.14 1.98 0.00

northeast vs west 1.65 1.29 2.12 1.69 1.28 2.22

south vs west 1.56 1.23 1.97 1.53 1.18 2.00

Hospital Type: academic/research program vs community cancer program

2.63 2.20 3.14 2.54 2.09 3.08 <.0001

integrated network program vs community cancer program

1.89 1.43 2.49 1.76 1.30 2.40

Year of Diagnosis: 2005 vs 2004 1.06 0.58 1.91 0.99 0.52 1.90 <.0001

2006 vs 2004 0.73 0.39 1.36 0.68 0.34 1.34

2007 vs 2004 1.20 0.70 2.04 1.15 0.64 2.07

2008 vs 2004 1.81 1.10 2.98 1.81 1.05 3.13

2009 vs 2004 1.41 0.86 2.31 1.21 0.70 2.09

2010 vs 2004 2.60 1.63 4.15 2.31 1.38 3.88

2011 vs 2004 2.04 1.27 3.27 1.76 1.05 2.97

2012 vs 2004 1.85 1.16 2.97 1.81 1.07 3.04

2013 vs 2004 1.71 1.07 2.74 1.43 0.85 2.41

2014 vs 2004 1.58 0.99 2.52 1.39 0.83 2.32

Sex: Female vs Male 1.13 0.99 1.30 1.19 1.01 1.39 0.03

Race: black vs white 0.79 0.60 1.03 0.73 0.54 0.98 0.08

others vs white 0.90 0.69 1.16 0.86 0.64 1.15

Insurance Status: government insurance vs no insurance

1.06 0.78 1.44 1.27 0.90 1.80 0.00

private vs no insurance 1.68 1.23 2.29 1.76 1.25 2.48

% without HSD 1.07 1.00 1.14 0.97 0.88 1.07 0.49

Median Household Income 1.11 1.04 1.19 1.17 1.05 1.30 0.00

Residency Type 1.03 0.99 1.07 1.06 1.01 1.11 0.01

Charlson Comorbidity Index 0.97 0.89 1.06 0.95 0.86 1.04 0.24

Clinical Stage: 2 vs 1 0.56 0.47 0.67 0.53 0.43 0.64 <.0001

3A vs 1 0.28 0.22 0.36 0.27 0.21 0.35

3B vs 1 0.19 0.16 0.24 0.17 0.14 0.22

*adjusted for all other variables **Wald Chi-Square test p-value

25

Figure 2.3a. KM plots for stages 1-3 ICC patients, n=3,651.

26

Figure 2.3b. KM plots for stages 1-3 ICC patients less than 70 years, n=2,337.

27

Figure 2.3c. KM plots for stages 1-3 ICC patients at least 70 years, n=1,315.

28

Table 2.3. Crude and propensity score (PS) stratified-adjusted Cox models for overall survival- age and treatment effects examined.

HR LCL UCL HR LCL UCL

Unadjusted model

Curative surgery vs no treatment 0.28 0.25 0.32 Surgery +/- chemo or RT vs no

treatment

0.32

0.27

0.38

Other treatment vs no treatment 0.70 0.64 0.76

PS stratified model – by age group PS stratified model – by treatment

< 70 years no treatment

Curative surgery vs no treatment 0.36 0.31 0.43 ≥ 70 vs < 70 years 1.26 1.09 1.46 Surgery +/- chemo or RT vs no

treatment

0.39

0.31

0.48

Other treatment vs no treatment 0.76 0.68 0.87 curative surgery

≥ 70 vs < 70 years 1.14 0.92 1.41

≥ 70 years

Curative surgery vs no treatment 0.33 0.27 0.40 surgery +/- chemo or RT Surgery +/- chemo or RT vs no

treatment

0.42

0.29

0.60

≥ 70 vs < 70 years

1.35

0.91

2.01

Other treatment vs no treatment 0.75 0.65 0.86

other treatment

≥ 70 vs < 70 years 1.23 1.10 1.38

Abbreviations: HR – hazard ratio, LCL –95% lower confidence limit, UCL – 95% upper confidence limit

29

Supplemental results

Table S2.1. Standardized mean differences by propensity score strata and treatment group.

Treated (curative surgery)

Control (no curative surgery)

Treated - Control

Stratum Index

N Mean N Mean Standardized Mean

**p- value

Difference

Propensity score 1 177 0.14 1358 0.12 0.133 <0.01

2 178 0.25 483 0.24 0.021 0.21

3 177 0.35 335 0.35 0.011 0.51

4 178 0.46 257 0.46 0.008 0.64

5 177 0.58 111 0.57 0.098 0.02

Region 1 177 4.82 1358 4.84 -0.008 0.93

2 178 4.52 483 4.38 0.058 0.41

3 177 4.52 335 4.32 0.083 0.39

4 178 4.05 257 4.20 -0.063 0.48

5 177 3.85 111 4.01 -0.067 0.57

Hospital type 1 177 2.77 1358 2.60 0.279 0.00

2 178 2.82 483 2.77 0.089 0.62

3 177 2.90 335 2.88 0.039 0.89

4 178 2.99 257 3.05 -0.102 0.11

5 177 3.04 111 3.01 0.050 0.36

Year of diagnosis 1 177 2010.63 1358 2010.20 0.157 0.10

2 178 2010.53 483 2010.64 -0.038 0.64

3 177 2010.61 335 2010.90 -0.108 0.12

4 178 2010.99 257 2011.03 -0.014 0.90

5 177 2011.02 111 2011.07 -0.018 0.97

Primary insurance 1 177 2.41 1358 2.41 0.001 0.41

2 178 2.39 483 2.12 0.168 0.32

3 177 2.19 335 2.34 -0.099 0.43

4 178 2.14 257 2.21 -0.042 0.95

5 177 1.73 111 1.79 -0.037 0.34

Median Household Income

1 177 3.02 1358 2.88 0.134 0.10

2 178 3.12 483 3.04 0.083 0.28

3 177

2.81 335 2.93 -0.110 0.31

30

Table S2.1. (Continued).

* mean represents proportion of patients not of a given stage within each stratum

**Z-score test p-value

4 178 2.97 257 3.09 -0.112 0.26

5 177 3.26 111 3.30 -0.035 0.63

Residency type 1 177 1.97 1358 2.02 -0.031 0.31

2 178 1.90 483 2.19 -0.150 0.18

3 177 2.38 335 2.17 0.108 0.45

4 178 2.49 257 2.31 0.094 0.95

5 177 2.80 111 2.61 0.100 0.55

Charlson Comorbidity Index

1 177 0.46 1358 0.39 0.096 0.22

2 178 0.44 483 0.42 0.029 0.60

3 177 0.46 335 0.46 0.005 0.75

4 178 0.47 257 0.52 -0.066 0.51

5 177 0.49 111 0.59 -0.122 0.52

Stage 1* 1 177 0.95 1358 0.98 -0.066 0.03

2 178 0.78 483 0.83 -0.111 0.14

3 177 0.56 335 0.53 0.054 0.59

4 178 0.26 257 0.27 -0.001 0.99

5 177 0.09 111 0.07 0.028 0.70

Stage 2* 1 177 0.86 1358 0.89 -0.060 0.31

2 178 0.62 483 0.59 0.055 0.58

3 177 0.51 335 0.55 -0.081 0.45

4 178 0.74 257 0.74 0.001 0.99

5 177 0.92 111 0.93 -0.029 0.70

Stage 3A* 1 177 0.80 1358 0.79 0.013 0.90

2 178 0.81 483 0.80 0.030 0.78

3 177 0.94 335 0.94 0.002 0.98

4 178 1.00 257 1.00 0.000

5 177 1.00 111 1.00 0.000

Stage 3B* 1 177 0.39 1358 0.34 0.119 0.17

2 178 0.80 483 0.78 0.040 0.63

3 177 0.99 335 0.98 0.022 0.43

4 178 1.00 257 1.00 0.000

5 177 1.00 111 1.00 0.000

31

[Type a quote from the

document or the summary of an interesting

point. You can position the text box

Intrahepatic biliary malignancies in NCDB

n=23,273

ICD-O-3 morphologies not defined as intrahepatic

cholangiocarcinoma

n=1,896

Intrahepatic cholangiocarcinoma (ICC) n=21,377

Other patient exclusion criteria: ▪ Patients seen for

diagnostic purposes only: 2,675

▪ multiple primary site tumors: n=3,544

▪ single primary tumors seen in more than 1 CoC facility: n=2,153

▪ borderline malignant behavior: n=11

▪ carcinoma-in-situ: n=8 ▪ unknown vital status or

follow-up time: 1,906 ▪ missing or unknown

clinical stage: 3,124 ▪ patient deaths within 90

days of diagnosis: n=1,110

ICC patients seen in 1 CoC facility for treatment and included in analysis

n=3,653

Patients with stage IV disease

excluded: n=3,194

Figure S2.1. Inclusion and exclusion criteria of study participants, NCDB (2004-2014).

32

Figure S2.2. Treatment trend for stages 1-4 ICC patients, n=21,377. Cochran-Armitage: p

=0.702

33

CHAPTER 3: NEOADJUVANT CHEMOTHERAPY FOR PATIENTS WITH INTRAHEPATIC

CHOLANGIOCARCINOMA

Abstract [276 words]

Introduction: Although liver resection provides the only potentially-curative approach for

intrahepatic cholangiocarcinoma (ICC), locoregional and systemic recurrence remain common.

Despite recent studies supporting the use of multimodality therapy for resectable ICC, the

survival benefit in the adjuvant setting is small. Neoadjuvant chemotherapy (NC) is a potential

alternative, though its role for resectable disease is controversial and has not been well

characterized.

Methods: We performed a retrospective cohort study of ICC patients in the National Cancer

Data Base who were treated with curative-intent surgery (2006-2014). NC utilization over time

was evaluated across participating hospitals, and predictors of NC use were identified using

multivariable logistic regression. The effect of NC on overall survival (OS) was examined using

propensity-score matched Cox regression models.

Results: A total of 881 patients met inclusion criteria for the study cohort. NC was used in 8.3 %

of the population. On multivariable analysis, increasing stage (p<0.001) and year of diagnosis

(p=0.03) were independent predictors of NC utilization. In the unadjusted model, there was no

difference in OS between the NC versus non-NC groups (median OS of 51.8 months versus

35.6 months, respectively; p=0.51), however there was a trend towards improved survival in the

NC group for the high-risk population (stages 2-3) (median OS 35.7 versus 26.4 months,

respectively; p=0.1). After adjusting using the propensity-score 1:4 matched cohort, NC

utilization was associated with a trend towards improved OS (HR 0.78 [95%CI 0.54-1.11];

p=0.16).

34

Conclusion: Overall NC utilization for resectable ICC is low, although its use has increased over

time and for those with more advanced disease. Despite no clear survival benefit, this study

shows that patients with more advanced disease may benefit from a multimodal approach using

preoperative chemotherapy.

Introduction The prognosis of intrahepatic cholangiocarcinoma (ICC) is dismal, with median survival of less

than three years and five-year survival of less than 10%. 44,45 Complete resection of small single

tumors without evidence of lymph node, neural, or blood vessel involvement remains the best

option for cure among the 15% of patients eligible for surgery.46-48 Additionally, the National

Comprehensive Cancer Network (NCCN) suggests the use of adjuvant chemotherapy among

patients with early stage ICC in whom lymph node metastasis are present or negative resection

margins cannot be achieved. 49,50 Although neoadjuvant chemotherapy is the lesser examined

of the systemic chemotherapy approaches, there is recent evidence that it may offer better

survival than the current standard of care among patients with locally invasive disease. 51,52

Theoretically, its advantages over adjuvant chemotherapy include better compliance, improved

delivery of drugs to a better vascularized presurgical tumor bed and sterilization of micro-

metastasis, potential tumor downstaging and preselection of patients who may benefit from

surgery and further chemotherapy. 51

Only a few studies have quantified the effect of neoadjuvant chemotherapy in ICC patients and

these have been based on small samples from single institutions. 53-55 Only now are some

randomized clinical trials such as ACTICCA, PRODIGE-12 and BILCAP suggesting survival

benefits of adjuvant chemotherapy over post-operative observation. 56,57 There are no published

neoadjuvant chemotherapy clinical trials that we are aware of. Furthermore, there is an inherent

selection bias of patients in observational studies as those who receive neoadjuvant

chemotherapy tend to be younger albeit diagnosed with later stage disease. 58 As a result,

35

causal inferences of the neoadjuvant chemotherapy effect on ICC survival cannot often be

made.

Based on this context, the aims of the current study are to characterize neoadjuvant

chemotherapy use across a hospital-based cancer registry and evaluate its treatment effect on

survival among patients with ICC having surgical treatment.

Methods Study Design We carried out a retrospective cohort design among ICC patients diagnosed between January

1, 2006 through December 31, 2014 in the National Cancer Database (NCDB). The NCDB is a

hospital-based cancer registry jointly established in 1989 and maintained by the Commission on

Cancer (CoC) of the American College of Surgeons and the American Cancer Society.

Annually, more than 1500 CoC hospitals contribute anonymized patient records to the

database, which currently represents more than 70% of newly diagnosed cancers,

accumulating 34 million cancer cases across the United States. 21The NCDB collects data on

patient characteristics, staging information, tumor histology, first-line treatment and outcomes

from participating hospitals using nationally standardized data item and coding definitions

specified in CoC oncology registry data standards. 59,60 The current study was approved by the

Moffitt Cancer Center and University of South Florida IRB committee.

Inclusion/exclusion criteria

We extracted data on patients who met our criteria for ICC diagnosis during the study period.

Using the third edition of the International Classification of Diseases for Oncology (ICD-O-3), we

defined ICC as topographic code C22.1 and morphological codes 8160, 8140, 8255, 8260,

8453, 8480, 8481 and 8503. We excluded patients with multiple primary tumors, non-malignant

tumors, stage IV disease and for which clinical staging information was missing, unknown or

non-applicable. To minimize duplication of records, we also excluded patients with single

36

primary ICC tumors seen in more than one CoC hospital. Our analysis was restricted to patients

who had potentially curative surgical procedures in a CoC hospital. Curative surgery was

defined as a biliary tract-specific procedure involving a hepatectomy and/or biliary tract

resection in a patient who did not undergo palliative treatment, a tumor destructive procedure or

liver transplant (Supplemental Figure 3.1).

Intervention - neoadjuvant chemotherapy Neoadjuvant chemotherapy was our main exposure and was defined as any receipt of systemic

chemotherapy preceding a curative surgical procedure, irrespective of the presence of a record

of chemotherapy after surgery. Two mutually exclusive non-neoadjuvant chemotherapy

interventions, or unexposed groups, were also defined: adjuvant chemotherapy and surgery

alone. Adjuvant chemotherapy was defined as a receipt date of systemic chemotherapy

following that of curative surgery, while surgery alone was the absence of chemotherapy as part

of the surgical treatment. For analytic purposes, patient treatment in which chemotherapy was

administered between two surgical procedures were neither defined nor included in the present

study. Except for the descriptive analysis of the study sample, adjuvant chemotherapy and

surgery alone interventions were considered under a single referent non-neoadjuvant category.

Outcomes

Overall survival was our primary outcome of interest and was measured from time of diagnosis

until death or date of last follow-up. The secondary outcome was neoadjuvant chemotherapy

utilization rates at the hospital level. A trend analysis was performed to examine changes over

time within the NCDB registry and within consistent participating hospitals during the study

period, and multivariable logistic regression was performed to identify predictors of neoadjuvant

chemotherapy utilization.

Covariates Covariates for assessing independent predictors of neoadjuvant chemotherapy use were

broadly grouped into hospital-, patient- and tumor/treatment-based. The hospital-based

37

covariates included hospital location by region (Northeast, South, Midwest, West) and cancer

program type of hospital (community, academic/research, integrated network). Patient-based

covariates included year of cancer diagnosis, age (measured as both continuous and binary:

< 70 & ≥ 70 years), sex, race (white, black, other), primary insurance type (uninsured, private

insurance, government insurance), Charlson comorbid severity index (0, 1, 2, ≥ 3 point-

score) and distance of residence from the hospital (miles). To enhance the anonymity of

small easily identifiable patient subgroups, the following socioeconomic variables were

collected at the zip code level, which was itself masked: residence type (metro, urban/rural);

percent of residents without high school diploma (<14%, 14-19.9%, 20-28.9%, ≥ 29%);

median household income (<$30,000, ≥$30,000-34,999, >$34,999-45,999, >$45,999).

Finally, tumor grade (well differentiated, moderately differentiated, undifferentiated, poorly

differentiated) and stage (I, II, IIIA, IIIB) comprised the tumor-based covariates. Because of the

small numbers of poorly differentiated ICC, this category was collapsed into that of the

undifferentiated during analysis. Crucially, two editions of the American Joint Committee on

Cancer (AJCC) staging were used across the study time span: the sixth edition from 2006 to

2009 and the seventh from 2010 onwards; to minimize misclassification bias from differential

diagnostic criteria, we recoded all clinical staging information to reflect the current eighth edition

AJCC using a framework proposed by Meng and colleagues.35 In summary, stages 1 and 2

were left unmodified across the study period; stages 3 and 3A of the sixth AJCC edition were

recoded as stage 3A, while stages 3B and 3C were recoded as 3B; stage 4A of the seventh

edition was recoded as 3B while stage 4 remained unmodified.

Propensity score development

To mitigate the effects of selection biases inherent in treatment decisions, we used logistic

regression models to calculate probabilities/propensity scores (PS) of receiving neoadjuvant

chemotherapy among all study participants under the assumption that grouping similarly-scored

38

patients with different treatment would provide a counterfactual for the estimation of an average

treatment effect among treated ICC patients. Baseline variable selection for PS generation was

guided by significant predictors of neoadjuvant chemotherapy use in our study that were also

well-established determinants of survival. However, because of a small event size and the

further loss of neoadjuvant chemotherapy patients when we attempted to include all covariates

in our logistic models, we selected only the following variables for PS generation: hospital type,

patient age, comorbidity index, clinical stage of disease and year of diagnosis. Several PS

techniques were subsequently applied to quantify neoadjuvant chemotherapy effects on

survival, including matching and stratification. We additionally ensured that all patients treated

with neoadjuvant chemotherapy were used in the PS techniques by identifying and linking

uniquely anonymized hospital IDs associated with missing observations of hospital type to non-

missing ones.

Statistical analysis Baseline hospital, patient socioeconomic and tumor characteristics were stratified by treatment

categories, with differences in the distribution of proportions and medians of these

characteristics assessed by chi-square and Kruskal-Wallis tests, respectively. Trends of

neoadjuvant chemotherapy utilization overtime was examined at the hospital-level using the

Cochran-Armitage trend test for assessment of statistical significance, while logistic regression

with odds ratios (ORs) and 95% confidence intervals (CIs) were constructed to identify

independent predictors of neoadjuvant chemotherapy use. Kaplan-Meier (KM) plots were used

to describe survival probabilities among the entire sample, and subset populations stratified by

stage. Several Cox models were used to estimate hazard ratios (HRs) and 95% CIs associated

with neoadjuvant chemotherapy use on survival. The first of the Cox models was an unadjusted

estimate of the neoadjuvant chemotherapy effect while the others incorporated propensity score

techniques. The second Cox model PS-matched neoadjuvant to non-neoadjuvant recipients in a

fixed 1:1 ratio within a 0.25 caliper and specifying the shortest Malahanobis distance of the age

39

and year of diagnosis variables between exposed and unexposed groups. The third Cox model

PS-matched recipients in a variable 1:4 ratio while specifying a matching region defined by all

neoadjuvant chemotherapy patients using an optimal algorithm. The fourth Cox model

estimated a pooled neoadjuvant chemotherapy effect across five propensity score strata, into

which study participants were grouped. All Cox models additionally accounted for potential

clustering of survival effects within unique hospitals by specifying robust estimates of the

potential correlation of such effects within hospitals. Other than attempts to identify missing

hospital types, the results presented here were analyzed by complete case analysis. A 0.05

level of significance was used and SAS version 9.4 (Cary, North Carolina) was used for all

analyses.

Results A total of 881 ICC patients with known clinical staging information who had undergone

potentially curative surgery in a CoC-accredited hospital met inclusion criteria and represented

our study sample (2006 – 2014). Most of these patients presented with stage I disease (52.8%),

were white (85.0%), female (55.1%), less than 70 years of age (65.7%) and were treated in an

academic/research setting (66.6%) with surgery alone (66.1%). Overall, only 8.3% of patients

received neoadjuvant chemotherapy (NC). Patients who received NC tended to be younger

(79.7% less than 70 years vs 65.7% in overall sample, p < 0.01), sought treatment farther away

from residence (median 38 miles vs 25 in overall sample, p < 0.01) and presented with more

stage III disease (26.7% vs 15.1% in overall sample, p < 0.01) (Table 3.1).

Neoadjuvant chemotherapy utilization – trends and predictors

NC use was reported by a total of 266 unique hospitals in every year except 2007, but rarely

accounted for more than 10% of the treatment strategy utilized annually. The overall pattern of

use over time was not statistically significant. The annual number of reporting hospitals ranged

from 25 in 2006 to 101 in 2014. When we restricted the trend analysis to hospitals that

consistently reported patients throughout the study period, NC use approached 20% of the

40

annual treatment mix and the trend moved towards statistical significance without reaching it.

Of 27 such hospitals, 26 (96.2%) were affiliated with academic/research programs (Figures

3.1a & 3.1b). On multivariable logistic regression, region, year of diagnosis and stage

independently predicted NC use (Table 3.2). The mid-western region was least likely than the

others to use NC (OR=0.31, 95% CI=0.09-1.01; p=0.04). NC use increased with advancing

year of diagnosis (OR=1.20, 1.01-1.43; p=0.03) and patients with stage 3A disease were more

likely to receive NC (OR=4.33, 1.44-13.04; p < 0.001).

Survival analysis The median follow-up time and overall survival for the whole population was 50.9 months and

36.4 months, respectively. On unadjusted analysis, although there was no statistically significant

difference between the NC and non-NC groups, there was a trend towards improved survival for

the intervention group (median OS 50.8 vs. 35.6 months, respectively; p=0.5). When stratifying

by stage of disease, the trend favoring neoadjuvant chemotherapy utilization was stronger for

those with advanced disease (stages 2 and 3) with median survival of 47.6 vs. 25.9 months

(p=0.1), while it disappeared for those with early stage presentation (stage 1) (Figures 3.2a-c).

Table 3.2 lists the results of the Cox regression models. On univariate analysis, neoadjuvant

chemotherapy was associated with a non-significant trend of improved survival (HR 0.92 [95%CI

0.64-1.31]; p=0.66). Each of the propensity score models included all 74 patients who received

NC. For both the matched (1:1 and 1:4) and the propensity stratified models, the trend persisted,

though it was not statistically significant. Notably, the performance of the matched cohorts was

excellent; using the 1:1 PS matched cohort, all standardized mean differences between NC and

non-NC patients associated with propensity-adjusted variables were below 0.1, except for the

academic category of the hospital type variable (Supplemental Table 3.1). Similarly, for the

stratified model, there were approximately 15 NC patients in each of the five strata. The

frequency distribution of NC and non-NC patients in each of the five strata for the PS stratified

model is show in Supplemental Figure 3.2.

41

Discussion

In this large retrospective study of ICC patients that received potentially curative surgery, we

observed that there was a small but increasing trend towards NC use and, on average, its use

appeared to have a small association-trend to improved survival over the receipt of surgery

alone or the use of adjuvant chemotherapy. NC use was primarily driven by hospitals

associated with academic/research cancer programs, a finding that reflects the complexity of

the current recommendation that NC be considered in initially unresectable disease. 50 Our

robust findings are compatible with the notion that some patients, who seek care in non-

academic/research settings, may benefit from NC use and the improved outcomes observed in

some hospitals may be related, in part, to it. Furthermore, we observed what appears to be a

delayed survival benefit for NC patients who survived at least two years post-diagnosis, the

veracity and the exact nature of which are unknown and require further investigation.

Until recently, large study evidence of the survival benefits of NC has come from its use in other

gastrointestinal malignancies. In a 2016 study of 12,857 pancreatic cancer patients, 58 of which

12% of those who were eligible for surgical resection used NC, a stage-dependent advantage of

NC over adjuvant chemotherapy (AC) use was observed. Stage III patients had a median

overall survival (OS) of 22.9 vs 17.3 months for the AC group, while stages II and I patients

receiving NC experienced longer median OS (26.2 vs. 25.7 months AC use and 23.3 vs. 23.0

months AC use, respectively). Similarly, in evaluating two approaches of NC use among 10,086

patients with esophageal cancer, when compared to that to which watchful waiting was

employed post-operatively, a 2017 study demonstrated the group that continued chemotherapy

after surgery had a 21% lower eight-year risk of death. 61 Smaller, older studies involving

neoadjuvant chemoradiation in extrahepatic cholangiocarcinoma (ECC) have also reported

advantages. 62,63 In one of such studies, 12 (26%) of 45 patients were treated with neoadjuvant

therapy with fluoropyrimidine-based chemotherapy and radiotherapy. The reported five-year

42

survival rates were 53% and 23% for the NC and AC patients, respectively, despite the former

initially having unresectable disease. 63

Our main finding cannot be directly compared to prior studies. To our knowledge, the present

study is the first to aggregate only ICC patients and attempt to evaluate chemotherapeutic

treatment effects using causal inference methods. A study involving all types of

cholangiocarcinoma was recently undertaken in which NC use was compared directly to AC

use, while applying a one-to-many propensity score matching technique. 42 Despite the inclusion

of a non-baseline radiotherapy use variable in generating propensity scores and not

distinguishing between ICC and other types of cholangiocarcinoma, they estimated an average

NC treatment hazard ratio of 0.78 (95% CI=0.64-0.94), a finding compatible with those of the

present study. Specifically, our one-to-many matching estimate is nearly identical to their result,

an observation that suggests NC may have the same effect on different types of CC, albeit we

argue this estimate may be biased. 64 Nonetheless, that study was able to achieve better

precision in its confidence limits due to its larger sample size. The Malahanobis distance-

adjusted one-to-one propensity score match, however, may have offered better validity in

sacrifice of precision by ensuring instead only the best matches for the NC group were used.

When compared to ICC-only studies from western centers, our other findings appear to fall

outside the range of reported results. In a small French study of 74 surgical ICC patients with

locally advanced disease, of which 39 (53%) were treated with NC, it was found that the NC

group had a median survival of 24.1 vs. 25.7 months among those who received surgery

alone.58 Among 45 ICC patients who underwent surgical resection in a single German

institution, 5 (11.1%) were given NC while 23 received AC. Although survival was not stratified

by chemotherapy sequence in relation to surgery, among those who had a resection with

negative tumor margins, chemotherapy use was associated with a median survival of only 15

43

months. 65 It is pertinent to report the recent and first-ever findings of phase III clinical trials of

AC use among patients with advanced biliary cancers (ABC trials). 66 A total of 109 ICC

patients from the United Kingdom ABC-01, -02 and -03 trials who received first line treatment

with Cisplatin and Gemcitabine, of whom 52 had liver-only disease, were retrospectively

reviewed and observed to have a median OS of 15.4 months after a median follow-up of 12.2

months from a larger cohort of biliary cancer patients. Importantly, of the 52 patients, 44% had

non- metastatic disease while 26.9% received prior treatment including surgery. We, however,

demonstrated higher median OS with NC use in general and among patients with stage 3

ICC, which would correspond to locally invasive non-distant metastatic disease. This

departure from other western centers is likely a result of the use of NC in earlier stages of

disease and more aggressive treatment with surgery among all patients with later stage

disease in our study.

Additionally, we cannot rule out the survival effects of hospital-related structural and process of

care factors, such as the presence of multidisciplinary specialists and monitoring of operating

times and blood loss, which have been shown to vary widely in the treatment of pancreatic

cancer and may further explain systematic patient survival variation in closely related ICC. 67

The current findings should be interpreted with caution. It is worth noting that the NCDB is a

hospital-based cancer registry. As such, it is likely to aggregate more severe ICC cases and

possibly underestimate its survival in the larger population. Also, CoC hospitals which contribute

cancer cases to NCDB have been identified as being more urban, larger and providing more

cancer services than non-CoC hospitals.68 This suggests that patients treated in non-CoC

hospitals may have poorer outcomes than those treated in CoC hospitals, possibly resulting in

less of a survival underestimation by NCDB. Not all patients who used NC are captured in the

present study, namely, those who may have received NC prior to liver transplantation. These

patients have limited functional liver reserves to begin with, the survival effect of NC in this

44

setting has not been comprehensively evaluated and as such our findings cannot be

generalized to them. Furthermore, the results presented here are subject to bias from

confounders not included in the propensity-score generating model.

However, this study has several key advantages. NCDB accounts for some 70% of incident

cancer cases in the United States, making it one of the most representative cancer registries

available. We have been careful to account for one of the most important biases in ICC

treatment studies, i.e. treatment selection of patients by disease stage, which itself is a predictor

of survival. By applying propensity score techniques, we were able to compare patients

diagnosed with the same clinical stage but receiving different surgery-based therapies.

Crucially, we used all patients who received NC in the setting of potentially curative surgery in

our propensity score matching, and therefore minimized bias associated with partial matching.

In conclusion, in this propensity score-adjusted study using NCDB registry data, we

demonstrated a trend towards a survival advantage of NC use among ICC patients over the

more prevalent practices of surgery alone and in combination with AC. The use of systemic

chemotherapy in the treatment of ICC, irrespective of its sequence in relation to surgery,

presupposes disease with poor prognostic features such as invasion of nearby major blood or

lymphatic vessels. For many patients with advanced non-metastatic disease, NC use may offer

distinct advantages over AC by improving drug compliance and delivery which are often

hampered by post-operative complications in the AC setting, and by providing an opportunity for

pre-operative tumor downstaging and subsequent R0 resections among patients with initially

unresectable disease. Despite the use of large data, we were unable to aggregate a large

sample of patients who received NC. As the same drugs with the same safety profile are used in

the NC and AC approaches, it seems reasonable to offer more patients NC in situations where

contraindications are not present, especially in a randomized clinical trial setting. It is likely that

NC affects prognosis by working in concert with institutional best practice and patient factors.

45

There is a need to identify these factors and quantify their survival effects. In particular, the

tumor microenvironment and its many cellular and molecular interactions remain mostly

mysterious.

46 9

Table 3.1. Baseline sociodemographic, hospital, clinical and tumor characteristics of patients with non-metastatic intrahepatic

cholangiocarcinoma, treated with surgery, by treatment strategy (n=881).

Treatment Strategy

All

(n=881)

Surgery

alone

(n=583)

Surgery +

adjuvant

therapy

(n=224)

Neoadjuvan

t therapy +

Surgery

(n=74)

N (%) N (%) N (%) N (%) *p-

value

Region 0.01

Missing 28 (3.2) 10 (1.7) 12 (5.4) 6 (8.1)

Northeast 219 (24.9) 126 (21.6) 71 (31.7) 22 (29.7)

South 312 (35.4) 226 (38.8) 68 (30.4) 18 (24.3)

Midwest 219 (24.9) 155 (26.6) 47 (21.0) 17 (23.0)

West 103 (11.7) 66 (11.3) 26 (11.6) 11 (14.9)

Hospital type 0.25

Missing 28 (3.2) 10 (1.7) 12 (5.4) 6 (8.1)

Community cancer program 181 (20.5) 119 (20.4) 51 (22.8) 11 (14.9)

Academic/research program 587 (66.6) 400 (68.6) 135 (60.3) 52 (70.3)

Integrated network program 85 (9.6) 54 (9.3) 26 (11.6) 5 (6.8)

Year of diagnosis 0.08

2006 28 (3.2) 13 (2.2) 12 (5.4) 3 (4.1)

2007 46 (5.2) 32 (5.5) 14 (6.3) 0 0.0

2008 83 (9.4) 55 (9.4) 24 (10.7) 4 (5.4)

2009 80 (9.1) 48 (8.2) 23 (10.3) 9 (12.2)

47 9

Table 3.1. (Continued).

2010 144 (16.3) 94 (16.1) 41 (18.3) 9 (12.2)

2011 119 (13.5) 77 (13.2) 32 (14.3) 10 (13.5)

2012 121 (13.7) 81 (13.9) 23 (10.3) 17 (23.0)

2013 114 (12.9) 82 (14.1) 21 (9.4) 11 (14.9)

2014 146 (16.6) 101 (17.3) 34 (15.2) 11 (14.9)

Median age at diagnosis (25%, 75%) 64(57,72)

66(59, 73) 62(54, 69) 62 (53, 67) <0.01

Median age at diagnosis - groups <0.01

< 70 years 579 (65.7) 347 (59.5) 173 (77.2) 59 (79.7)

70 years + 302 (34.3) 236 (40.5) 51 (22.8) 15 (20.3)

Gender 0.02

Male 396 (44.9) 281 (48.2) 88 (39.3) 27 (36.5)

Female 485 (55.1) 302 (51.8) 136 (60.7) 47 (63.5)

Race 0.20

White 749 (85.0) 495 (84.9) 187 (83.5) 67 (90.5)

Black 60 (6.8) 44 (7.5) 12 (5.4) 4 (5.4)

other 72 (8.2) 44 (7.5) 25 (11.2) 3 (4.1)

Primary insurance <0.01

Uninsured 54 (6.1) 44 (7.5) 6 (2.7) 4 (5.4)

Private insurance 366 (41.5) 202 (34.6) 127 (56.7) 37 (50.0)

Government insurance 461 (52.3) 337 (57.8) 91 (40.6) 33 (44.6)

% without high-school education 0.13

Missing 33 (3.7) 20 (3.4) 12 (5.4) 1 (1.4)

>=29% 147 (16.7) 112 (19.2) 27 (12.1) 8 (10.8)

48

Table 3.1. (Continued).

20-28.9% 178 (20.2) 120 (20.6) 40 (17.9) 18 (24.3)

14-19.9% 198 (22.5) 131 (22.5) 51 (22.8) 16 (21.6)

< 14% 325 (36.9) 200 (34.3) 94 (42.0) 31 (41.9)

Median household income 0.30 Missing 33 (3.7) 20 (3.4) 12 (5.4) 1 (1.4)

< $30,000 111 (12.6) 81 (13.9) 22 (9.8) 8 (10.8)

$30,000 - $34,999 138 (15.7) 94 (16.1) 32 (14.3) 12 (16.2)

$35,000 - $45,999 219 (24.9) 151 (25.9) 46 (20.5) 22 (29.7)

$46,000 + 380 (43.1) 237 (40.7) 112 (50.0) 31 (41.9)

Residency location 0.36

Missing 31 (3.5) 21 (3.6) 8 (3.6) 2 (2.7)

Metro 678 (77.0) 446 (76.5) 178 (79.5) 54 (73.0)

Urban/rural 172 (19.5) 116 (19.9) 38 (17.0) 18 (24.3)

Median distance to treating hospital,

in miles (25%, 75%) <0.01

25(9,73) 26(9,82) 18(8,48) 38(18,85)

Charlson comorbidity index 0.15

0 599 (68.0) 379 (65.0) 168 (75.0) 52 (70.3)

1 183 (20.8) 129 (22.1) 37 (16.5) 17 (23.0)

2 65 (7.4) 49 (8.4) 13 (5.8) 3 (4.1)

≥3 34 (3.9) 26 (4.5) 6 (2.7) 2 (2.7)

Tumor Grade <0.01

Missing 139 (15.8) 97 (16.6) 22 (9.8) 20 (27.0)

Well differentiated, differentiated, NOS 92 (10.4) 67 (11.5) 19 (8.5) 6 (8.1)

49

Table 3.1. (Continued).

Moderately differentiated, moderately

well differentiated, intermediate

differentiation

425 (48.2) 287 (49.2) 108 (48.2) 30 (40.5)

Poorly differentiated 213 (24.2) 123 (21.1) 73 (32.6) 17 (23.0)

Undifferentiated, anaplastic 12 (1.4) 9 (1.5) 2 (0.9) 1 (1.4)

Clinical stage <0.01

0 2 (0.2) 1 (0.2) 1 (0.4) 0 0.0

1 465 (52.8) 346 (59.3) 94 (42.0) 25 (33.8)

2 281 (31.9) 161 (27.6) 90 (40.2) 30 (40.5)

3A 93 (10.6) 50 (8.6) 28 (12.5) 15 (21.3)

3B 40 (4.5) 25 (4.3) 11 (4.9) 4 (5.4)

Lymph node resection 0.01

No 416 (47.2) 295 (50.6) 88 (39.2) 33 (44.5)

Yes 465 (52.8) 288 (49.4) 136 (60.7) 41 (55.5)

Surgical margins

R0 655 (74.3) 468 (80.3) 139 (62.1) 48 (64.9)

R1/R2 176 (20.0) 86 (14.8) 72 (32.1) 18 (24.3)

margin status unknown 50 (5.7) 29 (5.0) 13 (5.8) 8 (10.8)

Radiation treatment <0.01

None 751 (85.2) 570 (97.8) 125 (55.8) 56 (75.7)

External beam 114 (12.9) 7 (1.2) 93 (41.5) 14 (18.9)

Other 16 (1.8) 6 (1.0) 6 (2.7) 4 (5.4)

*Chi-Square test p-values for categorical variables and Kruskal-Wallis test p-values for continuous variables

50

Figure 3.1a. Annual proportion of patients with intrahepatic cholangiocarcinoma

receiving neoadjuvant chemotherapy, in participating NCDB hospitals (n=881). Cochran-

Armitage p- value=0.17

51

Figure 3.1b. Trend over time for neoadjuvant chemotherapy utilization among 26 hospitals with

consistent patient reporting through study period (n=340). Cochran-Armitage p-value= 0.1

52

Table 3.2. Multivariable logistic regression analysis examining predictors of neoadjuvant chemotherapy utilization (n=881).

Crude Odds Ratio

95% Confidence

Interval

*Adjusted Odds Ratio

95% Confidence Interval

**p-value adjusted

Odds Ratio Region 0.04

Midwest vs west 0.46 0.16, 1.32 0.31 0.09, 1.01

Northeast vs west 0.93 0.36, 2.37 0.82 0.28, 2.38 South vs west 0.55 0.21, 1.44 0.51 0.17, 1.53

Hospital type 0.12 Academic vs community

1.62 0.70, 3.73 1.95 0.76, 4.97

Integrated vs community

-- --, -- -- --, --

Year of diagnosis 1.10 0.95, 1.27 1.20 1.01, 1.43 0.03

Age 70+ vs <70

0.59 0.29, 1.19 0.53 0.24, 1.19 0.07

Sex

Female vs male 1.34 0.72, 2.52 1.61 0.79, 3.28 0.14

Race 0.24 B vs. W 0.88 0.26, 2.97 0.87 0.22, 3.34

Other vs. W 0.23 0.03, 1.73 0.14 0.01, 1.17 Primary Insurance

0.36

Government vs no insurance

1.57 0.20, 12.18 1.41 0.16, 12.13

Private vs no insurance

1.88 0.24, 14.69 1.29 0.15, 11.02

Charlson Comorbidity Index

0.64

0 vs 3 1.82 0.23, 13.92 1.22 0.14, 10.29

1 vs 3 2.67 0.33, 21.35 2.11 0.24, 18.45 2 vs 3 1.80 0.17, 18.18 1.22 0.10, 13.08

Residency Metro vs.

urban/rural

0.95 0.44, 2.04 1.06 0.41, 2.71 0.70

Distance to hospital

1.00 0.99, 1.00 1.00 0.99, 1.00 0.63

Grade Moderate vs well

differentiated 1.16 0.43, 3.11 0.84 0.28, 2.51

Poor vs well differentiated

1.17 0.40, 3.41 0.94 0.29, 3.05

Undifferentiated vs well differentiated

-- -- , -- -- --, --

Stage 0.00 2 vs 1 2.34 1.18, 4.63 2.72 1.27, 5.82

3A vs 1 3.00 1.17, 7.65 4.33 1.44, 13.04

53

Table 3.2. (Continued).

3B vs 1 0.85 0.10, 6.73 2.70 0.26, 27.44

No high school diploma

0.59

<14% vs >=29 1.45 0.51, 4.12 1.40 0.35, 5.50 14%-19.9 vs >=29 1.99 0.68, 5.81 1.80 0.49, 6.51 20%-28.9 vs >=29 2.55 0.88, 7.39 2.17 0.63, 7.40 Median household income

0.56

$30,000-34,9999 vs <30,000

1.48 0.43, 5.09 1.13 0.28, 4.59

$35,000-45,999 vs <30,000

2.02 0.65, 6.31 2.20 0.53, 9.04

$46,000 + vs <30,000

1.27 0.41, 3.89 1.08 0.23, 4.98

*Adjusted for all other variables **Wald Chi-Square test p-value

54

Figure 3.2a. Unadjusted Kaplan-Meier curves depicting overall survival estimates for all patients with non-metastatic, intrahepatic cholangiocarcinoma - by treatment strategy (n=881).

55

Figure 3.2b. Unadjusted Kaplan-Meier curves depicting overall survival estimates for patients with locally advanced stages (Stages II-III), intrahepatic cholangiocarcinoma - by treatment strategy (n=414).

56

Figure 3.2c. Unadjusted Kaplan-Meier curves depicting overall survival estimates for patients with early stage (Stages I), intrahepatic cholangiocarcinoma - by treatment strategy (n=465).

57

Table 3.3. Results from Cox regression models examining the effect of neoadjuvant chemotherapy on survival.

Model Description

HR

95% CI

**p-value

Unadjusted with facility as cluster variable

0.92

0.64, 1.31

0.66

Adjusted using 1:1 propensity score matching*

0.85

0.58, 1.25

0.42

Adjusted using 1:4 propensity score matching†

0.78

0.54, 1.11

0.16

Adjusted using propensity score stratification‡

0.82

0.60, 1.13

0.24

Abbreviations: CI - confidence interval, HR - hazard ratio **Wald Chi-Square test p-value *1:1 matching using Mahalanobis distance matching of age, year of diagnosis with facility as cluster variable (n=128)

†1:4 optimal variable matching with facility as cluster variable (n=259) ‡Pooled estimate of stratified matched propensity scores (strata=5) with facility as cluster variable (n=878)

58

Supplemental results

Figure S3.1. Study flow chart for selection criteria of patients with intrahepatic cholangiocarcinoma – NCDB (2006-2014).

ICD-O-3 morphologies not defined as intrahepatic

cholangiocarcinoma n=1,896

▪ no surgical

procedure:

n=2,519

▪ destructive tumor/non-

specific surgical

procedures: n=130

▪ transplant procedures:

n=48

Other patient exclusion criteria: ▪ multiple primary site

tumors: n=2,825 ▪ single primary tumors

seen in more than 1 CoC facility: n=1,520

▪ borderline malignant behavior: n=12

▪ missing a clinical stage: 4,272

▪ absent or unknown surgery-systemic treatment information: n=415

▪ intraoperative systemic therapy n=5

▪ unknown vital status or

follow-up time: 600

Intrahepatic biliary malignancies in NCDB

n=23,273

Intrahepatic cholangiocarcinoma (ICC)

n=21,377

Exclusion of

8,150 stage

IV patients

ICC patients seen in 1 CoC facility for diagnosis and/or treatment and

included in analysis n=3,578

ICC patients seen in 1 CoC facility for surgical treatment and included for propensity score matching analysis

n=1,059

ICC patients seen in 1 CoC facility

who underwent curative surgery:

n=881

59

Table S3.1. Baseline characteristic of patients in the neoadjuvant and no-neoadjuvant groups, from the propensity score matching – adequacy of matching expressed in p values and standardized differences.

No neoadjuvant chemotherapy

(n= 74)

Neoadjuvant

chemotherapy (n=74)

*p-value

Standardized difference

Age

1.0

Age: <70 yrs 59(79.7) 59(79.7) 0.00000

Age: ≥70 yrs 15(20.3) 15(20.3) 0.00000

Hospital type

0.95

Community program 10 (13.5) 12(16.2) -0.06924 Academic program 61(82.4) 57(77.0) 0.12193

Integrated network program 3(4.0) 5(6.7) -0.09753

Stage

0.93

Stage 0/I 25(33.7) 25(33.7) 0.00000 Stage II 30(40.5) 30(40.5) 0.00000 Stage III 19(25.6) 19(25.6) 0.00000

Charlson comorbidity index

0.88

0 56(75.6) 52(70.2) -0.03507 1 14(18.9) 17(22.9) 0.03274 2 4(5.4) 3(4.0) -0.01152

≥3 0(0.0) 2(2.7) 0.03010

Year of diagnosis

1.0

2006 2(2.7) 3(4.0) -0.07336 2007 1(1.3) 0(0.0) -- 2008 4(5.4) 4(5.4) 0.00000 2009 9(12.1) 9(12.1) 0.00000 2010 10(13.5) 9(12.1) 0.03849 2011 9(12.1) 10(13.5) -0.03950 2012 17(22.9) 17(22.9) 0.00000 2013 10(13.5) 11(14.8) -0.03915 2014 12(16.2) 11(14.8) 0.03709

*Z-score test p-value

60

Figure S3.2. Histogram illustrating the distribution of patients in each of the propensity score strata, by treatment group (n=881).

61

Figure S3.3 Utilization of neoadjuvant chemotherapy among all ICC patients, n=21,018

62

CHAPTER 4: CONCLUSION AND RECOMMENDATIONS

In summary, most patients will require multimodal treatment for non-metastatic ICC because of

the late presentation of disease. In this scenario, we have demonstrated that potentially curative

surgery should be an integral part of treatment for best survival outcomes. Specifically,

neoadjuvant chemotherapy offers several advantages over the more prevalent adjuvant

chemotherapy, namely, it promotes better drug compliance and pre-selects patients who have

chemo-responsive tumors. Elderly patients experience a treatment response equivalent to

younger ones. However, against the backdrop of a naturally reduced life expectancy and

perception of increased toxicity to chemotherapy, when compared to younger ones, older

patients are less likely to receive stage-appropriate treatment. The receipt of substandard care

among the elderly is an important and often overlooked driver of poor ICC outcomes.

It is important to note that the present dissertation does not measure or account for quality of life

post treatment, as no surrogates were available in NCDB at the time of analysis. Survival does

not necessarily equate to quality of life. Quality of life is a subjective and multidimensional

concept that is difficult to quantify. Although as much as 5-7% of patients in small studies have

experienced a post-surgical complication which may contribute to substantial disability and

reduced quality of life, there appears to be the perception of improved overall health despite

reported worsening of digestive and liver-related symptoms beyond three months after stage-

appropriate treatment.69,70 There is a need for large prospective study evaluation of quality of life

among ICC patients who have undergone treatment.

In the United States, it is estimated that the 2000 CENSUS population of persons 65 years and

older would double by 2050. 71 As ICC risk increases with age, it is therefore expected that ICC

63

incidence and its prevalence will likely increase in the future. There is a need to address several

current shortcomings in order to meet this challenge.

Participation in clinical trials: Low enrollment of older clinical trial participants is one of the most

consistently cited reasons for substandard care in practice among this population. As a result,

there are few treatment guidelines for the elderly among whom comorbidity and polypharmacy

further complicate chemotherapy use. It has been suggested that incentivizing the relaxation of

stringent trial inclusion criteria may be helpful. 31,72

Chemotherapy and pharmaco- dynamic and kinetic studies: Healthy aging is associated with a

host of system-wide changes including reduced liver volume and renal function as measured

by glomerular filtration rate, bone marrow function and general muscle loss. 73 These changes

potentially affect the dose, dosing interval and choice of chemotherapeutic agents or

supportive care that elder ICC patients may require. 74 There is a continuing need to update the

effects of new chemotherapeutic agents on the aging body as well as the older body’s ability to

absorb, distribute and eliminate these agents. More importantly, these physiologic changes

occur at different rates for different individuals and older age should not necessarily imply

substantial organ impairment. 75

Standardized geriatric assessment: With few guidelines for elderly patients, there is a need to

standardize and validate ICC-specific geriatric assessments that comprehensively assess risk of

adverse effects of therapeutic chemotherapy and complications of surgery, especially in settings

where a multidisciplinary team is not available. While a few geriatric assessments for cancer

patients exist, they are yet to be routinely used as part of patient work-up. 30

64

Integration and coordination of care: Not only will more of a multidisciplinary approach to

managing older cancer patients be required, but primary care providers will also need to

effectively compile and transmit complex health information to and from specialists and patients

alike, in a manner that encourages active participation and decision making. 76

65

REFERENCES

1. Shaib Y, El-Serag HB. The epidemiology of cholangiocarcinoma. Paper presented

at: Seminars in liver disease 2004.

2. Khan SA, Thomas HC, Davidson BR, Taylor-Robinson SD. Cholangiocarcinoma.

The Lancet. 2005;366(9493):1303-1314.

3. Maithel SK, Gamblin TC, Kamel I, Corona‐Villalobos CP, Thomas M, Pawlik

TM. Multidisciplinary approaches to intrahepatic cholangiocarcinoma. Cancer.

2013;119(22):3929-3942.

4. NCI. National Cancer Institute/Surveillance, Epidemiology, End Results: Cancer Stat

Facts: Liver and Intrahepatic Bile Duct Cancer:

https://seer.cancer.gov/statfacts/html/livibd.html , accessed from the Internet on

October 22, 2019

5. Gupta A, Dixon E. Epidemiology and risk factors: intrahepatic cholangiocarcinoma.

Hepatobiliary Surg Nutr. 2017;6(2):101-104. 6. Razumilava N, Gores GJ. Cholangiocarcinoma. The Lancet. 2014;383(9935):2168-2179.

7. Goodman ZD. Neoplasms of the liver. Modern pathology: an official journal of the

United States and Canadian Academy of Pathology, Inc. 2007;20 Suppl 1:S49-

60.

8. Patel AH, Harnois DM, Klee GG, LaRusso NF, Gores GJ. The utility of CA 19-9 in the

diagnoses of cholangiocarcinoma in patients without primary sclerosing cholangitis.

The American journal of gastroenterology. 2000;95(1):204-207.

9. Bridgewater J, Galle PR, Khan SA, et al. Guidelines for the diagnosis and management

66

of intrahepatic cholangiocarcinoma. Journal of hepatology. 2014;60(6):1268-1289.

10. Mavros MN, Economopoulos KP, Alexiou VG, Pawlik TM. Treatment and prognosis for

patients with intrahepatic cholangiocarcinoma: systematic review and meta-analysis.

JAMA surgery. 2014;149(6):565-574.

11. Ghidini M, Tomasello G, Botticelli A, et al. Adjuvant chemotherapy for resected biliary

tract cancers: a systematic review and meta-analysis. HPB. 2017;19(9):741-748.

12. Simo KA, Halpin LE, McBrier NM, et al. Multimodality treatment of intrahepatic

cholangiocarcinoma: a review. Journal of surgical oncology. 2016;113(1):62-83.

13. Morise Z, Sugioka A, Tokoro T, et al. Surgery and chemotherapy for intrahepatic

cholangiocarcinoma. World J Hepatol. 2010;2(2):58-64.

14. Tran TB, Bal CK, Schaberg K, Longacre TA, Chatrath BS, Poultsides GA. Locally

Advanced Intrahepatic Cholangiocarcinoma: Complete Pathologic Response to

Neoadjuvant Chemotherapy Followed by Left Hepatic Trisectionectomy and Caudate

Lobectomy. Digestive Diseases and Sciences. 2015;60(11):3226-3229.

15. Morise Z, Sugioka A, Hoshimoto S, et al. Patient with advanced intrahepatic

cholangiocarcinoma with long‐term survival successfully treated with a combination of

surgery and chemotherapy. Journal of Hepato‐Biliary‐Pancreatic Surgery.

2008;15(5):545-548.

16. Howell M, Valle JW. The role of adjuvant chemotherapy and radiotherapy for

cholangiocarcinoma. Best Practice & Research Clinical Gastroenterology.

2015;29(2):333-343.

17. Vitale A, Spolverato G, Bagante F, et al. A multi‐institutional analysis of elderly patients

undergoing a liver resection for intrahepatic cholangiocarcinoma. Journal of surgical

oncology. 2016;113(4):420-426.

18. Yeh C-N, Jan Y-Y, Chen M-F. Hepatectomy for Peripheral Cholangiocarcinoma in

67

Elderly Patients. Annals of Surgical Oncology. 2006;13(12):1553-1559.

19. De La Fuente SG, Bennett KM, Scarborough JE. Functional status determines

postoperative outcomes in elderly patients undergoing hepatic resections. Journal

of surgical oncology. 2013;107(8):865-870.

20. Grendar J, Grendarova P, Sinha R, Dixon E. Neoadjuvant therapy for downstaging of

locally advanced hilar cholangiocarcinoma: a systematic review. HPB.

2014;16(4):297- 303.

21. NCDB. About the National Cancer Database: https://www.facs.org/quality-

programs/cancer/ncdb/about, accessed from the Internet on July 30, 2019.

22. Massarweh NN, El-Serag HB. Epidemiology of hepatocellular carcinoma and

intrahepatic cholangiocarcinoma. Cancer Control,

2017;24(3):1073274817729245.

23. Everhart JE, Ruhl CE. Burden of digestive diseases in the United States Part III:

Liver, biliary tract, and pancreas. Gastroenterology 2009;136(4):1134-1144.

24. McLean L, Patel T. Racial and ethnic variations in the epidemiology of intrahepatic

cholangiocarcinoma in the United States. Liver International, 2006;26(9):1047-

1053.

25. Khan SA, Thomas HC, Davidson BR, Taylor-Robinson SD. Cholangiocarcinoma.

The Lancet 2005;366(9493):1303-1314.

26. Tyson GL, El‐Serag HB. Risk factors for cholangiocarcinoma.

Hepatology, 2011;54(1):173-184.

27. Simo KA, Halpin LE, McBrier NM, et al. Multimodality treatment of

intrahepatic cholangiocarcinoma: a review. Journal of surgical oncology

2016;113(1):62-83.

28. Bellury LM, Ellington L, Beck SL, Stein K, Pett M, Clark J. Elderly cancer survivorship:

69

an integrative review and conceptual framework. European Journal of Oncology

Nursing. 2011;15(3):233-242.

29. Berger NA, Savvides P, Koroukian SM, et al. Cancer in the elderly. Trans Am Clin

Climatol Assoc. 2006;117:147-156.

30. Mohile SG, Dale W, Somerfield MR, et al. Practical assessment and management of

vulnerabilities in older patients receiving chemotherapy: American Society of Clinical

Oncology Guideline for Geriatric Oncology. Journal of clinical oncology: official journal

of the American Society of Clinical Oncology. 2018;36(22):2326.

31. Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and

its treatment. CA: a cancer journal for clinicians. 2016;66(4):337-350.

32. DePeralta DK, Frakes J, Mahipal A, et al. Multidisciplinary Management of Liver,

Pancreatic, and Gastric Malignancies in Older Adults. Geriatric Oncology 2019:1-

28.

33. Hung AK, Guy J. Hepatocellular carcinoma in the elderly: Meta-analysis and

systematic literature review. World J Gastroenterol. 2015;21(42):12197-12210.

34. NCDB. Facility Oncology Registry Data Standards: https://www.facs.org/-

/media/files/quality-programs/cancer/ncdb/fords-2016.ashx?la=en, accessed from the

internet on: July 30th, 2019.

35. Meng Z-W, Pan W, Hong H-J, Chen J-Z, Chen Y-L. Modified staging classification

for intrahepatic cholangiocarcinoma based on the sixth and seventh editions of the

AJCC/UICC TNM staging systems. Medicine (Baltimore). 2017;96(34):e7891-e7891.

36. Chang GJ, Skibber JM, Feig BW, Rodriguez-Bigas M. Are we undertreating

rectal cancer in the elderly? An epidemiologic study. Ann Surg. 2007;246(2):215-

221.

70

37. Tranvåg EJ, Norheim OF, Ottersen TJBc. Clinical decision making in cancer care:

a review of current and future roles of patient age. 2018;18(1):546.

38. Liu P-H, Hsu C-Y, Lee Y-H, et al. Uncompromised treatment efficacy in elderly

patients with hepatocellular carcinoma: a propensity score analysis. Medicine

(Baltimore). 2014;93(28):e264-e264.

39. Sarfati D, Gurney J, Stanley J, Koea J. A retrospective cohort study of patients with

stomach and liver cancers: the impact of comorbidity and ethnicity on cancer care

and outcomes. J BMC cancer. 2014;14(1):821.

40. Bridges J, Hughes J, Farrington N, Richardson A. Cancer treatment decision-

making processes for older patients with complex needs: a qualitative study. BMJ

open. 2015;5(12):e009674.

41. Tariman JD, Berry DL, Cochrane B, Doorenbos A, Schepp KG. Physician, patient, and

contextual factors affecting treatment decisions in older adults with cancer and models

of decision making: a literature review. Oncol Nurs Forum. 2012;39(1):E70-E83.

42. Yadav S, Xie H, Bin-Riaz I, et al. Neoadjuvant vs. adjuvant chemotherapy for

cholangiocarcinoma: A propensity score matched analysis. European Journal of

Surgical Oncology. 2019.

43. Cardinale V, Semeraro R, Torrice A, et al. Intra-hepatic and extra-hepatic

cholangiocarcinoma: New insight into epidemiology and risk factors. World

J Gastrointest Oncol. 2010;2(11):407-416.

44. Mavros MN, Economopoulos KP, Alexiou VG, Pawlik TM. Treatment and Prognosis

for Patients With Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-

analysisTreatment of Intrahepatic CholangiocarcinomaTreatment of Intrahepatic

Cholangiocarcinoma. JAMA Surgery. 2014;149(6):565-574

71

45. Buettner S, van Vugt JLA, Ijzermans JN, Groot Koerkamp B. Intrahepatic

cholangiocarcinoma: current perspectives. Onco Targets Ther. 2017;10:1131-

1142.

46. Maithel SK, Gamblin TC, Kamel I, Corona-Villalobos CP, Thomas M, Pawlik

TM. Multidisciplinary approaches to intrahepatic cholangiocarcinoma. Cancer.

2013;119(22):3929-3942.

47. Wang K, Zhang H, Xia Y, Liu J, Shen F. Surgical options for

intrahepatic cholangiocarcinoma. Hepatobiliary Surg Nutr.

2017;6(2):79-90.

48. Bagante F, Spolverato G, Weiss M, et al. Defining Long-Term Survivors Following

Resection of Intrahepatic Cholangiocarcinoma. Journal of Gastrointestinal Surgery.

2017;21(11):1888-1897.

49. Ramírez-Merino N, Aix SP, Cortés-Funes H. Chemotherapy for cholangiocarcinoma:

An update. World J Gastrointest Oncol. 2013;5(7):171-176.

50. Bagante F, Gani F, Beal EW, et al. Prognosis and adherence with the National

Comprehensive Cancer Network Guidelines of patients with biliary tract cancers:

an analysis of the National Cancer Database. Journal of Gastrointestinal Surgery.

2019;23(3):518-528.

51. Cantrell CK, White J. Successful Management of an "Unresectable" Intrahepatic

Cholangiocarcinoma with Neoadjuvant Systemic Therapy, Chemoembolization, and

Extended Hepatectomy with Portal Vein Reconstruction. Cureus.

2018;10(5):e2696- e2696.

72

52. Kato A, Shimizu H, Ohtsuka M, et al. Downsizing Chemotherapy for Initially

Unresectable Locally Advanced Biliary Tract Cancer Patients Treated with

Gemcitabine Plus Cisplatin Combination Therapy Followed by Radical Surgery. Annals

of Surgical Oncology. 2015;22(3):1093-1099.

53. Le Roy B, Gelli M, Pittau G, et al. Neoadjuvant chemotherapy for initially

unresectable intrahepatic cholangiocarcinoma. BJS. 2018;105(7):839-847.

54. Kato A, Shimizu H, Ohtsuka M, et al. Surgical resection after downsizing

chemotherapy for initially unresectable locally advanced biliary tract cancer: a

retrospective single- center study. Annals of surgical oncology. 2013;20(1):318-324.

55. Lunsford KE, Javle M, Heyne K, et al. Liver transplantation for locally advanced

intrahepatic cholangiocarcinoma treated with neoadjuvant therapy: a prospective

case- series. The lancet Gastroenterology & hepatology. 2018;3(5):337-348.

56. Cai Y, Cheng N, Ye H, Li F, Song P, Tang W. The current management of

cholangiocarcinoma: A comparison of current guidelines. Bioscience trends. 2016.

57. Shroff RT, Kennedy EB, Bachini M, et al. Adjuvant therapy for resected biliary tract

cancer: ASCO clinical practice guideline. Journal of Clinical Oncology. 2019:

JCO1802178.

58. Le Roy B, Gelli M, Pittau G, et al. Neoadjuvant chemotherapy for initially unresectable

intrahepatic cholangiocarcinoma. British Journal of Surgery. 2018;105(7):839-847.

59. NCDB. Facility Oncology Registry Data Standards: https://www.facs.org/-

/media/files/quality-programs/cancer/ncdb/fords-2016.ashx?la=en, accessed from the

internet on July 30, 2019.

73

60. Bilimoria KY, Stewart AK, Winchester DP, Ko CY. The National Cancer Data Base: a

powerful initiative to improve cancer care in the United States. Ann Surg Oncol.

2008;15(3):683-690.

61. Mokdad AA, Yopp AC, Polanco PM, et al. Adjuvant Chemotherapy vs Postoperative

Observation Following Preoperative Chemoradiotherapy and Resection in

Gastroesophageal Cancer: A Propensity Score–Matched Analysis. JAMA oncology.

2018;4(1):31-38.

62. McMasters KM, Tuttle TM, Leach SD, et al. Neoadjuvant chemoradiation for

extrahepatic cholangiocarcinoma. The American journal of surgery. 1997;174(6):605-

609.

63. Nelson JW, Ghafoori AP, Willett CG, et al. Concurrent chemoradiotherapy in resected

extrahepatic cholangiocarcinoma. International Journal of Radiation Oncology* Biology*

Physics. 2009;73(1):148-153.

64. Rassen JA, Shelat AA, Myers J, Glynn RJ, Rothman KJ, Schneeweiss S. One‐to‐many

propensity score matching in cohort studies. Pharmacoepidemiology and drug safety.

2012;21:69-80.

65. Yedibela S, Demir R, Zhang W, Meyer T, Hohenberger W, Schönleben F. Surgical

treatment of mass-forming intrahepatic cholangiocarcinoma: an 11-year Western

single- center experience in 107 patients. Annals of Surgical Oncology.

2009;16(2):404.

66. Lamarca A, Ross P, Wasan HS, et al. Advanced intrahepatic cholangiocarcinoma:

post- hoc analysis of the ABC-01,-02 and-03 clinical trials. JNCI: Journal of the

National Cancer Institute. 2019.

74

67. Bilimoria KY, Bentrem DJ, Lillemoe KD, Talamonti MS, Ko CY, on behalf of the

American College of Surgeons' Pancreatic Cancer Quality Indicator Development

Expert

P. Assessment of Pancreatic Cancer Care in the United States Based on

Formally Developed Quality Indicators. JNCI: Journal of the National Cancer

Institute. 2009;101(12):848-859.

68. Bilimoria KY, Bentrem DJ, Stewart AK, Winchester DP, Ko CYJJoCO. Comparison

of Commission on Cancer–approved and–nonapproved hospitals in the United

States: implications for studies that use the National Cancer Data Base.

2009;27(25):4177- 4181.

69. Ma KW, Cheung TT, She WH, et al. Major postoperative complications

compromise oncological outcomes of patients with intrahepatic

cholangiocarcinoma after curative resection – A 13-year cohort in a tertiary center.

Asian Journal of Surgery. 2019;42(1):164-171.

70. Raoof M, Lewis A, Goldstein L, et al. Timing and severity of post-discharge

morbidity after hepatectomy. HPB. 2017;19(4):371-377.

71. Cinar D, Tas D. Cancer in the elderly. North Clin Istanb. 2015;2(1):73-80. 72. Lewis JH, Kilgore ML, Goldman DP, et al. Participation of patients 65 years of age

or older in cancer clinical trials. Journal of clinical oncology. 2003;21(7):1383-1389.

73. Mangoni AA, Jackson SHD. Age-related changes in pharmacokinetics and

pharmacodynamics: basic principles and practical applications. British Journal of

Clinical Pharmacology. 2004;57(1):6-14.

75

74. Lichtman SM, Wildiers H, Launay-Vacher V, Steer C, Chatelut E, Aapro M.

International Society of Geriatric Oncology (SIOG) recommendations for the

adjustment of dosing in elderly cancer patients with renal insufficiency. European

Journal of Cancer. 2007;43(1):14-34.

75. Crombag BSM-R, Joerger M, Thürlimann B, Schellens HMJ, Beijnen HJ, Huitema

DRA. Pharmacokinetics of Selected Anticancer Drugs in Elderly Cancer Patients:

Focus on Breast Cancer. Cancers. 2016;8(1).

76. Walsh J, Harrison JD, Young JM, Butow PN, Solomon MJ, Masya L. What are the

current barriers to effective cancer care coordination? A qualitative study. BMC

Health Services Research. 2010;10(1):132.