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Follow-up Care Strategies After Treatment for Breast Cancer

Caprice C. Greenberg, MD, MPH; Heather B. Neuman, MD, MS; Jessica R. Schumacher, PhD; Menggang Yu, PhD; David Vanness, PhD

All investigators are affiliated with the University of Wisconsin, Madison.

Institution Receiving the PCORI award: Alliance for Clinical Trials in Oncology Original Project Title: Post-treatment Surveillance in Breast Cancer: Addressing an Urgent Need for Evidence PCORI Award Number: CE-1304-6543 HSRProj ID: HSRP20143518 ClinicalTrials.gov: NCT02171078

_______________________________ To cite this document, please use: Greenberg CC, Neuman HB, Schumacher JR, Yu M, Vanness D. (2019). Follow-up Care Strategies After Treatment for Breast Cancer. Washington, DC: Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/03.2020.CE.13046543

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Table of Contents

ABSTRACT ............................................................................................................................. 4

BACKGROUND ....................................................................................................................... 6

Overview ......................................................................................................................................... 6

Post-treatment Surveillance in Breast Cancer ................................................................................ 7

Table 1. National Guideline Recommendations for Surveillance to Detect Recurrence Following Active Treatment for Breast Cancer ..............................................................................................................7

PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS IN THE DESIGN AND CONDUCT OF RESEARCH AND DISSEMINATION OF FINDINGS .................................................................... 10

Types and Numbers of Stakeholders Involved ............................................................................. 10

Balance of Stakeholder Perspectives ............................................................................................ 10

Methods Used to Identify and Recruit Stakeholder Partners ...................................................... 11

Figure 1. Stakeholder topic identification process for the DEcIDE Cancer Consortium ......................... 12

Perceived or Measured Impact of Engagement ........................................................................... 12

METHODS ........................................................................................................................... 14

Study Overview ............................................................................................................................. 14

Table 2. Tumor Biology Groups: Types of Breast Cancer as Defined by Receptor Status ...................... 14

Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities ............... 15

Table 3. Modern-Era Adjuvant Therapies Used in Alliance Trials ........................................................ 16

Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging ...... 18

Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials ..................................................................................................... 24

Changes to the Original Study Protocol ........................................................................................ 26

RESULTS .............................................................................................................................. 27

Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities ............... 27

Table 4. Characteristics of Patients Enrolled in Alliance Trials ............................................................ 28

Figure 2. Annual hazards of breast cancer first recurrence by stage at diagnosis ................................ 29

Table 5. Cumulative Probability of Recurrence at 5 Years From Diagnosis .......................................... 29

Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence ...................................................................................................................................... 23

Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging ...... 25

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Table 7. Characteristics of Stage II to III Breast Cancer Patients With Distant Recurrence by How Recurrence Was Detected (Sign/Symptom vs Asymptomatic Imaging Detected), Overall Population (Unweighted) ................................................................................................................................... 26

Table 8. Unweighted and Weighted Association Between Asymptomatic vs Symptom Detected Distant Recurrences and Time to Death by Molecular Subtype Risk Group for Women Diagnosed With Stage II to III Breast Cancera .......................................................................................................................... 25

Table 9. Percentage of Patients Surviving Until Years 3 to 4 and Median Survival for Patients With Triple Negative and HER2+ Stage II to III Breast Cancer, Propensity Weighted Based on Receipt of Surveillance Within 3 Years Of Diagnosis .......................................................................................... 25

Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials ..................................................................................................... 26

Figure 3. Alliance Breast Committee support for clinical trial ............................................................. 27

Figure 4. Stakeholder-derived decision support tool design considerations ........................................ 28

Figure 5. Base inputs and outputs for decision support tool .............................................................. 29

Figure 6. Initial page of data entry for clinicians in the decision support tool ..................................... 31

Figure 7. Output page in the decision support tool ............................................................................ 32

DISCUSSION ........................................................................................................................ 33

Context for Study Results ............................................................................................................. 33

Generalizability of Findings ........................................................................................................... 34

Implementation of Study Results ................................................................................................. 34

Subpopulation Considerations ...................................................................................................... 35

Study Limitations .......................................................................................................................... 35

Future Research ............................................................................................................................ 36

CONCLUSIONS ..................................................................................................................... 38

REFERENCES ........................................................................................................................ 39

ACKNOWLEDGMENTS .......................................................................................................... 43

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ABSTRACT Background: Breast cancer is a heterogeneous disease, with side effects, recurrence, and survival varying based on biological characteristics such as receptor status (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]—each positive [+] or negative [–]), stage at presentation, and type of treatment received. Yet, clinical guidelines for surveillance are not tailored to account for this variation in risk. As a result, the clinical application of surveillance strategies varies widely following active treatment for breast cancer.

Objectives and Aims: This project had the following 2 main objectives: (1) Develop an approach to personalize the timing of surveillance visits, including a decision support tool and evidence to inform guideline development; and (2) inform the design of future prospective randomized trials in breast cancer surveillance by identifying the highest-value subpopulations and surveillance tests with the greatest potential to improve outcomes. To do this, we proposed 3 specific aims: (1) Use data from legacy clinical trials to evaluate how recurrence varies by patient and cancer characteristics; (2) use existing and abstracted data from institutions accredited by the Commission on Cancer (CoC; these hospitals provide care for approximately 70% of all newly diagnosed cancers in the United States) to evaluate the effectiveness of surveillance imaging for distant recurrence; and (3) engage cancer survivors, providers, and researchers in developing an improved patient-centered approach to guide post-treatment care and in designing high-priority prospective trials.

Methods: To optimize surveillance strategies, the team examined cancer recurrence and survival for patients enrolled in 17 Alliance for Clinical Trials in Oncology trials. We also collected imaging and recurrence information from more than 10 000 randomly selected patients from 1200 CoC-accredited facilities across the United States and compared survival for patients whose recurrence was identified on imaging with that of patients whose cancer was found when they developed symptoms.

Results: Recurrence rates were lowest for ER+ or PR+ cancers (7.7%) and HER2+ cancers (15.2%) after 5 years for stage II and III, respectively, and highest for triple negative cancers (ER– and PR–: 11% and HER2: 38%; p < 0.001). The utilization of surveillance systemic imaging in our cohort was lower than that reported in previous publications, whereas the underutilization of recommended breast imaging was consistent with that of previous reports. Recurrence detected on surveillance imaging rather than by signs and symptoms was not associated with a survival advantage for ER+ or PR+, HER2– disease, which represents most breast cancers; however, recurrence detected on surveillance imaging was associated with a statistically significant improvement in survival for 2 other subtypes of cancer (ER– and PR–, HER2– as well as HER2+), which represent higher-risk disease.

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Conclusions: Using primary data collection, we found that the use of surveillance imaging for systemic disease was not as high as previous studies suggest; however, our observation of a survival advantage for certain subgroups of breast cancer when metastatic cancer is detected on surveillance imaging supports the need for a prospective trial. In the meantime, our results have informed development of a clinician-facing decision support tool that can facilitate shared decision-making regarding an individualized surveillance strategy by providing estimates of the expected risk and timing of recurrence and mortality, based on known patient risk factors. This tool will be tested in future prospective studies.

Study Limitations: Study limitations include those related to available data and medical records. For example, we were unable to assess family history, including genetic predispositions. In addition, patients enrolled in clinical trials may not reflect the general population in terms of race and competing risks. In our assessment of the role of surveillance imaging on survival, we used a data set of patients diagnosed in 2006-2007 to allow for the collection of 5-year follow-up information. HER2 status was not recorded consistently, and trastuzumab (Herceptin), the targeted therapy given to patients who have HER2+ tumors, was not routinely administered to them during these years. The magnitude of the survival advantage observed for this subgroup is, therefore, likely overestimated. Last, this observational study could not completely control for differences between asymptomatic and symptom-detected recurrences and could not consider biological differences that may confer differences in survival after different types of recurrences. However, findings were robust in sensitivity analyses, including models restricted by site of recurrence and to patients who received treatment for their recurrence.

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BACKGROUND

Overview

More than 15 million Americans have completed a course of treatment for cancer and

require surveillance for recurrence.1 With continued advances in cancer treatments and

increasing life expectancy, this number is expected to rise, reaching more than 20 million by the

year 2026.1 At the same time, health care costs are growing at unsustainable rates,2 leading to

recent calls for a re-evaluation of common medical tests and treatments that are expensive but

that lack high-quality data regarding risks and benefits for patients.3 As a result, advanced

radiographic imaging studies to monitor for cancer recurrence, such as computerized

tomography (CT) or positive emission tomography (PET), have come under scrutiny.4

Clinicians follow patients after active treatment for cancer for many reasons. These

include (1) detecting locoregional recurrence (cancer returning in the breast or adjacent lymph

nodes) or a second primary breast cancer, (2) detecting distant metastases, (3) monitoring for

treatment toxicities, (4) managing patient anxiety and fear of recurrence, and (5) assuring

continuation of primary care and other health services. However, the optimal approach to such

post-treatment surveillance is unknown.5 Although the American Society of Clinical Oncology

(ASCO) and National Comprehensive Cancer Network (NCCN) publish clinical practice

guidelines, these are based on limited data and largely reflect expert opinion. Additionally,

current guidelines do not consider individual patient variation, such as risk of recurrence or

treatment toxicity.

This issue is especially critical for breast cancer. With 5-year survival rates for

nonmetastatic breast cancer now exceeding 85% (> 98% for node-negative cancer), almost 3

million women require post-treatment surveillance annually in the United States.6

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Post-treatment Surveillance in Breast Cancer

Routine surveillance following active treatment for nonmetastatic breast cancer

includes interval history and physical examination as well as diagnostic imaging. Surveillance

can target the recurrence of breast and lymph node disease or distant metastases. Numerous

clinical practice guidelines for surveillance have been generated, most notably by ASCO and

NCCN (Table 1).7-9

Table 1. National Guideline Recommendations for Surveillance to Detect Recurrence Following Active Treatment for Breast Cancer

American Society of Clinical Oncology

National Comprehensive Cancer Network

• History and physical every 3 to 6 months for 3 years, then every 6 to 12 months for next 2 years, then annually

• Mammography no earlier than 6 months after definitive radiation, with subsequent mammograms every 6 to 12 months

• The use of laboratory testing, chest radiographs, liver ultrasounds, pelvic ultrasounds, computerized axial tomography, positron emission tomography, magnetic resonance imaging, and/or tumor markers is not recommended for routine follow-up in an otherwise asymptomatic patient with no specific findings on clinical examination.

• History and physical 1 to 4 times per year for 5 years, then annually

• Mammography every 12 months • In the absence of signs and

symptoms, there is no indication for laboratory or imaging studies for metastases screening.

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These follow-up recommendations have remained unchanged for the past 20 years,

owing mainly to a lack of new data. Evidence supporting current guidelines for nonmetastatic

breast cancer follow-up is based largely on clinical trials conducted in the 1980s, which predate

improvements in systemic imaging (CT scans, bone scans, PET scans, or magnetic resonance

imaging [MRI]) and modern systemic therapies that target specific receptors (estrogen receptor

[ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]). After

a biopsy, tumor cells are determined to be receptor positive or receptor negative, depending

on whether testing confirms the presence of ER, PR, or HER2 receptors in the cancer cells.

When hormones (estrogen or progesterone) attach to receptors or HER2 growth-promoting

proteins are present, tumor growth can be accelerated. Modern era therapies specifically target

these receptors and proteins and are consistent with appropriate clinical practice guidelines for

the management of women diagnosed with nonmetastatic breast cancer.

A 2005 Cochrane review of surveillance following treatment for early-stage breast

cancer identified 4 well-designed randomized trials that found no difference in either overall or

disease-free survival among 3055 women followed by intensive testing with liver ultrasound

and chest X-ray, compared with annual physician visits and mammography.10-14 Quality of life

was also similar for those trials in which it was measured. A 2016 updated Cochrane review

came to the same conclusion, but for the first time the authors noted that “results should be

interpreted with caution, bearing in mind that these studies were conducted almost two

decades ago and that additional trials incorporating new biological knowledge and improved

imaging technologies are needed.”15

As a result, no strong evidence supports or refutes current recommendations against

routine CT, PET, or MRI. The effectiveness of more aggressive surveillance, especially in specific

clinical situations, is unknown, as imaging techniques and targeted therapeutics have advanced

since the original trials were performed. In addition, currently no published data exist on the

frequency and duration of oncology clinic visits for history and physical examination.9 To inform

the frequency and duration of follow-up based on patient-specific risk and patterns of

detection, understanding the likelihood and timing of breast cancer recurrence is critical.

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In part because of this uncertainty, wide variation in the use of cancer follow-up care

services has been reported in practice, including both underuse and overuse of surveillance

services relative to the current guideline recommendations. Overall patient utilization of health

services is high following active treatment, with up to 30 episodes of health services per

survivor during the first year following active treatment for stage I or II breast cancer.16 Studies

suggest that only 60% to 70% of patients receive mammograms within the first year of

completing treatment (underuse), whereas upward of 60% to 80% of patients receive imaging

to survey for metastatic disease (overuse).17-20 Such variation may reflect a lack of definitive

data and the age of the studies that do exist, and underscores the significance of our proposal

and the potential for major impact on clinical practice and policy. Higher concordance with

guidelines is observed when strong scientific evidence supports the recommendation.21-23

The main goal of this study was to provide critical evidence necessary to inform the

development of a risk-stratified tailored approach to surveillance following active treatment for

breast cancer. We sought to inform a strategy that is more patient centered and effective than

the current untailored approach and that accounts for recent advances in imaging and

treatment. In particular, we had the following 2 main objectives: (1) Develop an approach to

personalize the timing of surveillance visits; and (2) inform design of future prospective

randomized trials in breast cancer surveillance by identifying the highest-value populations and

surveillance tests. To do this, we proposed 3 specific aims:

Specific aim 1. Determine the risk and patterns of recurrence according to tumor

characteristics, treatment modalities, and other patient characteristics in order to inform the

design of a patient-centered, risk-stratified, tailored approach to post-treatment surveillance.

Specific aim 2. Evaluate current utilization and comparative effectiveness of routine

surveillance breast MRI and advanced body imaging to detect recurrence and improve survival

following active treatment for stage II or III breast cancer.

Specific aim 3.Engage stakeholders to develop a patient-centered, risk-based tailored

approach to post-treatment surveillance and identify high-priority, feasible comparators to be

tested in prospective randomized trials.

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PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS IN THE DESIGN AND CONDUCT OF RESEARCH AND DISSEMINATION OF FINDINGS

Types and Numbers of Stakeholders Involved

Cancer survivors and patient advocates were engaged throughout the study through the

Alliance for Clinical Trials in Oncology Research Network (hereafter Alliance) Patient Advocate

Committee (PAC; N = 19). Three members of the PAC were also part of the core research team;

2 are breast cancer survivors. In addition, multidisciplinary breast cancer clinicians (medical,

surgical, and radiation oncologists) were engaged through the Alliance Breast Committee.

For our third component, we formed a multi-stakeholder advisory group to integrate

into our stakeholder engagement process. This group (N = 12) included patient advocates,

many of whom work with underrepresented communities, including Native American, Latina,

and other women of color. It also included oncology specialty providers (radiation oncologists,

radiologists, surgical oncologists, medical oncologists) from both community and academic

settings. This group was formed because patients and clinicians routinely described the

importance of understanding others’ perspectives regarding the content and communication of

follow-up strategies. Bringing these 2 sets of stakeholders together was critical for working

through key design considerations and interpreting and communicating results.

Balance of Stakeholder Perspectives

Feedback from team members indicated that we achieved an appropriate balance of

stakeholder perspectives. We achieved this balance partly because of semiannual meetings but

mostly because of the care with which the study team assembled the stakeholder group, as

described above. Meetings emphasized open and dynamic communication that was designed

to fully engage the diverse set of perspectives represented by the multidisciplinary stakeholder

group. Agendas were well defined, allowing all participants an opportunity to share their

perspectives. After discussing research findings, the project team often presented specific

questions to stakeholders that prompted conversation as well as solicited general feedback,

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comments, and suggestions. The team presented findings clearly and in an accessible manner,

encouraging participation from all stakeholders to make sure that everyone felt that his or her

contribution was valuable.

Methods Used to Identify and Recruit Stakeholder Partners

The process of stakeholder engagement for this study began in 2009 as a part of the

DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) Cancer Consortium

funded by the Agency for Healthcare Research and Quality. Dr. Caprice Greenberg led the

stakeholders, a multidisciplinary group of patients, providers, researchers, payers, and

policymakers, in engagement activities to identify, prioritize, and operationalize topics for

comparative effectiveness research in cancer.24 Dr. Greenberg and her team identified and

recruited these stakeholders through existing relationships with professional organizations and

federal partners. The goal was to identify the topic with the greatest potential to improve the

care of cancer patients.

DEcIDE stakeholders identified post-treatment surveillance as the top priority, based on

the level of uncertainty about the optimal approach and the potential to change practice

significantly. Post-treatment surveillance also cuts across cancer types and affects survivorship,

an underrepresented component of the care continuum among the cancer comparative

effectiveness portfolio.

Figure 1 depicts the stakeholder process that led to identification of the study topic.

Toward the end of the DEcIDE engagement activities, the team recognized the need to engage a

broader group of clinical and patient stakeholders. This led investigators to partner with the

Alliance, an approach that continued throughout the design and execution of this PCORI grant,

in light of the team’s success during DEcIDE.

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Figure 1. Stakeholder topic identification process for the DEcIDE Cancer Consortium

Perceived or Measured Impact of Engagement

The following examples illustrate the impact of our stakeholder engagement; all were a

direct result of the study team’s engagement sessions:

1. Topic identification and study design: Before submission, we engaged multiple

stakeholders, as described as above, in identifying the high-priority topic. In addition,

stakeholders played a key role in determining that a randomized controlled trial was not

feasible at this time and provided key input for the design of our observational study.

Furthermore, they were essential in identifying patient-centered outcomes for inclusion

in the study and helping with the response to reviewers for our resubmission.

2. Operationalizing the study design: Based on feedback from stakeholders, we changed

the study design for aim 2 to stratified random sampling by stage rather than random

sampling across facilities to ensure robust estimates by stage at diagnosis.

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3. Designing the web-based decision support tool: We solicited and achieved stakeholder

consensus in selecting inputs, outputs, and the look and feel of the tool and in

identifying the primary audience (provider-facing to facilitate shared decision- making

rather than direct to patients). Stakeholders were adamant that the tool be flexible so

that outputs could be tailored to display only the outcomes that an individual patient

was interested in considering.

4. Finalizing variables and analytic methods: Medical and surgical oncologist stakeholders

were engaged in defining and classifying adverse events for the Alliance legacy trials in

aim 1, designing a rigorous approach for abstracting data on recurrence and defining the

intent of imaging for medical record abstraction as a part of aim 2, and selecting analytic

methods to ensure that clinicians can readily interpret findings and translate results to

the care of their patients.

5. Specifying high-priority comparators for future pragmatic trial(s) through a

combination of surveys and audience response units

6. Interpreting findings, including the implications of model results, and ensuring that

information is clearly communicated via the decision support tool

During our last stakeholder engagement session, one patient advocate who was

engaged continuously from the start to the end of the study had this to say about her

involvement:

It has been a great experience seeing the project from conception to completion. The project team was so open to our input, I always felt like a valued member of the team. The project addressed a need in the breast cancer community and addressed it in a way that took the patient perspective into consideration. It was a lot of work, and I appreciate the complexities of databases and mining data for meaningful information that matters to patients. . . . The team was always accessible to all of the advocates. . . . Providing all [stakeholders] information twice a year and asking for specific input at each meeting made us feel valued, useful and ensured that each meeting was productive.

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METHODS

Study Overview

This study has 3 specific aims, which will be considered in sequence. All 3 aims examine

breast cancer according to its biology, which is defined based on 3 receptors: ER, PR, and HER2.

Each receptor is classified as either positive or negative. Table 2 depicts the major types of

breast cancer that are included in this analysis.

Table 2. Tumor Biology Groups: Types of Breast Cancer as Defined by Receptor Status

Typea ER PR HER2 Abbreviation

Triple negative Negative Negative Negative ER/PR– HER2–

Triple positive Positivea Positivea Positive ER/PR+ HER2+

Hormone negative, HER2+

Negative Negative Positive ER/PR– HER2+

Hormone positive, HER2–

Positivea Positivea Negative ER/PR+ HER2–

HER2+ Positive or negative

Positive or negative

Positive ER/PR+ HER2+ ER/PR– HER2+

Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. aOnly ER or PR, not necessarily both, need be positive for a cancer to be considered hormone positive. When numbers were small, HER2+ cancers were evaluated together regardless of ER/PR status.

In aim 1, we combined data from 17 trials previously conducted by the Alliance to

evaluate the risk and patterns of recurrence. The aim was to generate data to inform the

tailoring of frequency and duration of follow-up clinical visits for history and physical exams,

based on empirically derived risk estimates. This unique data set contains the most

comprehensive, detailed prospective data on locoregional breast cancer in existence.

For aim 2, cancer registrars at 1296 CoC-accredited institutions across the United States

performed primary data collection on more than 11 000 patients regarding the use of imaging

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after active breast cancer treatment. With these detailed data on the intent with which a scan

was ordered, we were able to assess whether imaging use was consistent with national

guideline recommendations. Furthermore, detailed and accurate data on recurrence allowed us

to compare survival for patients whose recurrence was detected by scan with survival for

patients whose recurrence was detected by the development of symptoms.

Finally, in aim 3, in close collaboration with our multiple stakeholders, we combined the

results from the first 2 aims to design a high-value prospective randomized trial. Data from aim

1 were also used to develop a web-based decision support tool that can inform shared decision-

making regarding surveillance strategies.

Our stakeholders informed us that a prospective randomized trial was not advisable in

this instance without a prior large-scale observational study to guide development, including

the definition of inclusion/exclusion criteria and selection of comparators, and to ensure

efficient and appropriate investment of resources. We therefore performed a retrospective

cohort analysis to investigate current practice, determine the likelihood and timing of

recurrence, and examine the effectiveness of routine advanced imaging in 2 unique national

data sets. Each aim is described in detail below.

Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities

Data Sources

We combined data from 17 legacy Alliance clinical trials that had previously enrolled

women diagnosed with stages I, II, or III breast cancer. We further restricted the sample to

women who received surgery and modern-era adjuvant therapies (Table 3) and had complete

staging, receptor status, and recurrence information (n = 10 357). This ensured that the risk and

patterns of recurrence and treatment toxicities would be assessed in a patient population

treated with modern era therapies, in which treatments are personalized based on tumor

biology.

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Table 3. Modern-Era Adjuvant Therapies Used in Alliance Trials

Receptor Status Group

Modern-era Adjuvant Therapies

Triple negative Cytotoxic chemotherapy

ER/PR+, HER2–

Endocrine therapy +/– Cytotoxic chemotherapy

HER2+ Trastuzumab-based therapy Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

Study Setting

Aim 1 utilized data from previous clinical trials performed by the Cancer and Leukemia

Group B, North Central Cancer Treatment Group, and American College of Surgeons Oncology

Group. The study team identified all trials that enrolled patients with locoregional (stages I, II,

and III) breast cancer (N = 17). Data from each of these studies were standardized,

concatenated, and quality checked to allow for comparisons across trials. We chose to create

this unique aggregate data set because no existing data source has reliable recurrence or

survival information for an appropriately diverse and robust set of patients. These data will

foster greater understanding of the disease course to inform appropriate surveillance

recommendations.

Comparators

In this aim, we compared outcomes across types of breast cancer. The variables that

were available and consistent across trials included the following: age at diagnosis; ER status;

PR status; HER2 status; clinical and pathologic tumor stage (categorized as 0-2 cm, 2-5 cm, 5+

cm); and clinical and pathologic nodal stage (categorized as negative and positive). The

systematic collection of clinical and pathologic T (tumor size) and N (nodal) stage allows reliable

calculation of cancer stage at diagnosis. We used the categorization depicted in Table 2 for

making comparisons.

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Study Outcomes

The primary outcome is first recurrence as a binary variable. Secondary outcomes

include time to recurrence and overall and disease-specific survival. Each outcome is measured

as time from trial registration (as a proxy for diagnosis date).

Time Frame for the Study

Patients included in this study were diagnosed between 1997 and 2013. We chose to

include all data that were available rather than arbitrarily exclude trials based on year of

enrollment. We did exclude any patients who did not receive modern-era adjuvant treatment

regimens as defined in Table 3 and current standard surgical care to ensure that results would

reflect recurrence risk for the population of patients currently treated.

Analytical and Statistical Approaches

We estimated models predicting recurrence risk according to the tumor biology groups

described in Table 2 (ER/PR+, HER2–; triple negative; triple positive; ER/PR–, HER2+).25 We

assessed the relationship between receptor status and stage with time to first recurrence using

a stratified multivariable Cox proportional hazards model. The primary variable of interest was

tumor biology group, stratified by cancer stage (cancer staging for patients diagnosed with

early-stage breast cancer comprises tumor size and nodal status26). We also controlled for age

in the model. Data were censored at death, loss to follow-up, or at 5 years, whichever occurred

first.

Based on these models, we calculated cumulative probabilities of recurrence at 3 and 5

years, with associated confidence intervals, based on the hazard function for all combinations

of age, tumor biology groups, tumor size, and nodal status. These time frames correspond to

stakeholder consensus about time frames most relevant to determining risk during follow-up.

In addition, we plotted smoothed estimates of the hazards of recurrence at 1-year intervals

from the time of trial registration through the time of first recurrence. We used a log-rank test

to assess the difference in distribution of recurrence time by tumor biology group.

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Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body

Imaging

Data Sources

We used data from a national cancer registry, the National Cancer Database (NCDB),27,28

augmented with primary data collection via medical record abstraction. The NCDB is a joint

program of the American College of Surgeons, the CoC, and the American Cancer Society; it

captures 70% of newly diagnosed cancers in the United States.27,29

Facilities that are accredited by the CoC must attempt to follow all patients and report

data to the NCDB on a minimum of 90% of patients diagnosed and/or treated at their facilities,

each year from diagnosis until death, regardless of where they receive follow-up care. Trained

registrars at each facility collect information on diagnosis, first-course treatment factors, and

survival using the Facility Oncology Registry Data Standards manual.30 The CoC can use this data

collection platform to obtain additional data elements as necessary under its Special Study

mechanism, which involves the collection of specific data with the goal of improving cancer

care delivery.

Study Setting

This study utilized data collected from CoC-accredited hospitals through the NCDB. The

1231 CoC-accredited hospitals included in this study account for 70% of newly diagnosed

cancers in the United States. By randomly sampling a small number of patients at each hospital,

we were able to ensure generalizability to the entire population of patients being treated for

cancer in the United States.

Participants

The CoC identified a random sample of patients aged 18 and older with stage II and III

breast cancer at each CoC-accredited hospital. Patients with stage I breast cancer were

excluded because of their low rate of recurrence and low rate of surveillance imaging. Ten

patients (7 stage II, 3 stage III) were selected from each of 1231 facilities accredited by the CoC

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in 2006-2007 (N = 11 478). We designed the sampling strategy to achieve a similar ratio of stage

II to III patients in our sample as that observed nationally (observed ratio in 2006-2007: 73.9%

stage II, 26.1% stage III). Of facilities, 99% (N = 1217) participated and 11 360 patient records

were submitted. All but 17 facilities diagnosed and treated at least one stage III patient. We

maximized generalizability by randomly selecting patients from the census of CoC facilities

across the United States.

We excluded any patients who had delayed surgery (> 1 year from diagnosis; n = 17) or

had evidence of recurrence, developed a new primary cancer, died, or were otherwise lost to

follow-up before the end of active treatment (approximated by 10 months from diagnosis; n =

490). This left a sample size of 10 853 patients, whose medical records were abstracted by

trained registrars at each facility to assess surveillance imaging and recurrence.

Comparators

The primary explanatory variable was mode of distant recurrence detection, categorized

as (1) asymptomatic imaging either for cancer follow-up (routine imaging) or as an incidental

finding on unrelated imaging for other reason, vs (2) patient-detected sign/symptom that

prompted nonroutine doctor visit, physician-detected sign/symptom during routine visit, or

detection as part of work-up for a local/regional recurrence or new primary. Registrars were

instructed to code how the first documented distant cancer recurrence was initially detected

and not the diagnostic procedures or imaging that might have followed. Registrars were also

instructed to consult high-yield locations in the medical record (operative reports, pathology

reports, radiology/imaging reports, and notes from clinic and consult visits). We also provided

examples of symptoms with which a patient might present that could lead to a diagnosis of

distant recurrence and showed how surveillance-detected recurrences might be documented in

the medical record. We assessed recurrences within the first 5 years of diagnosis.

We considered systemic imaging scans (CT or MRI of the chest, abdomen, pelvis, and/or

head; bone scan; PET/CT) to be for surveillance when registrars classified them as “surveillance

imaging in the absence of new signs or symptoms (asymptomatic).” Imaging determined not to

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be for the purpose of surveillance included imaging performed as “follow-up for a new

sign/symptom,” “follow-up for a suspicious finding on other imaging, or “imaging performed in

response to a newly detected malignancy.” Registrars went to specific high-yield locations in

medical records inside and outside their facilities for this information.

We summarized use of surveillance systemic imaging as a dichotomous variable (receipt

of surveillance imaging within first 3 years: yes/no). We used this variable as the outcome

variable in a propensity model to account for differences between patients who received and

did not receive surveillance systemic imaging during follow-up.

Models controlled for appropriate factors, such as sociodemographics, age, coexisting

conditions, and treatment factors. Other factors included race, Hispanic ethnicity, zip code,

level of education, household income, insurance status, and rural/urban residence. Each

patient’s Charlson/Deyo clinical comorbidity score,31,32 categorized as 0 or 1+, was also

available. First-course treatment factors included surgery type, receipt of radiation, and

systemic therapies recorded as 3 separate yes/no dichotomous variables (chemotherapy,

hormone, and HER2 targeted therapy). Facility type was also included (community cancer

program/other, comprehensive community cancer program, academic/research program).

We stratified the analysis by type of breast cancer. We combined ER, PR, and HER2

status to create 3 tumor biology groups as described in Table 2: ER/PR+ HER2–; triple negative;

and HER2+.25 We further stratified the tumor biology type by stage II and stage III.

Study Outcomes

The primary outcome variable was the number of days from the initial breast cancer

diagnosis to death (as opposed to the time of recurrence to death), to reduce the potential for

lead-time bias.

Time Frame for the Study

Women diagnosed with stage II or III breast cancer who received an operation at a CoC-

accredited institution in 2006-2007 were included. We chose these 2 years to allow 5 years of

21

follow-up through the end of 2013, the most recent data available at the beginning of the

analysis.

Data Collection and Sources

This analysis used existing data from the NCDB as described previously. Additional data

elements, including indications for and results of systemic imaging as well as recurrences and

how they were first detected, were abstracted by trained registrars at each facility as part of a

CoC Special Study initiative to determine current surveillance practice and recurrence outcomes

for colorectal, breast, and lung cancers. Trained registrars are on staff at each facility to enter

the required data elements for the NCDB. For our study, these same registrars reviewed patient

records from their own and any other institutions or practice settings where a patient received

treatment or imaging studies, beginning at the end of active treatment and continuing until the

end of the fifth year after diagnosis or death, whichever occurred first.

Registrars recorded complete follow-up care information, including systemic imaging

studies conducted (chest CT, abdomen/pelvis CT/MRI, head CT/MRI, bone scan, PET/CT), the

intent of the scans (asymptomatic surveillance imaging: yes/no), recurrences and how they

were detected (asymptomatic imaging vs patient- or provider-detected signs and/or

symptoms), and death. Registrars used a standardized abstraction manual developed as a part

of this PCORI-funded study, and they entered data via the same secure web platform used for

the standard NCDB data collection.

Cancer registrars abstracted recurrence because recurrence information is unreliable

and missing for 18.2% of patients diagnosed with breast cancer.33 HER2 status was not routinely

collected in 2006-2007, and so we included it as a data element. The registrars confirmed key

fields (eg, date of death) at the time of chart abstraction. Data elements collected during the

study were merged with a patient’s corresponding existing record in the NCDB. The registrars

completely deidentified all data before providing them to the investigators.

22

Pilot Study and Reliability Analysis

To ensure the quality of the data, we performed both a pilot study and a reliability

study. For the pilot study, we piloted full study procedures at 18 CoC-accredited facilities with

180 randomly sampled patients (10 per facility) who met inclusion criteria. To assess feasible

and valid abstraction of the data fields related to recurrence, we ensured that at least one

randomly selected patient from each of the pilot facilities had a documented recurrence.

We conducted a reliability study to ensure that the cancer registrars could abstract

intent of imaging and recurrence from medical records. Specifically, we selected a random 5%

sample of patients who received their diagnosis and/or elements of first-course treatment at

multiple facilities (n = 537). Registrars at the second outside facility were assigned the same

patient to assess reliability of abstraction, particularly intent of scan. This was possible because

registrars at CoC facilities are required to track patients who receive follow-up care at multiple

facilities in order to retain accreditation.27 The primary facility was the one where the patient

received most of her cancer-related care; registrars at these sites had direct access to the most

complete follow-up information through that facility’s medical records. The registrar at the

second facility had to request a greater number of records. Records abstracted at the second

facility were used only for this reliability study.

From the 537 pilot patients, cancer registrars abstracted 1240 scans. Of these scans, 418

were systemic imaging scans. The observed percentage agreement for surveillance vs symptom

or follow-up advanced imaging was 79.4% (expected = 51.0%); the kappa was 0.6 (Z = 11.9; p <

0.001), indicating moderate to high agreement. These reliability estimates likely represent an

underestimate of reliability given that primary facilities by definition had direct access to more

complete medical information.

Analytical and Statistical Approaches

In our preliminary data analyses, we noticed potential bias in who received

asymptomatic imaging and, who therefore, was more likely to have her recurrence detected on

asymptomatic imaging. For example, women with a distant recurrence detected by symptoms

23

as compared with asymptomatic imaging were more likely to have had their index cancer be

lobular, treated with mastectomy, and treated in community cancer centers as compared with

academic facilities. To adjust for this bias, we constructed 2 propensity score weights. First, we

constructed propensity score models based on a patient’s receipt of asymptomatic systemic

imaging in the first 3 years following diagnosis, under the assumption that accounting for

differences between patients who do vs do not receive asymptomatic systemic imaging best

reflects the randomization point that would be used in a clinical trial. “Any imaging” was

modeled because only 12% of patients received 2 or more imaging studies of the same type

over the follow-up period.34 This approach is also consistent with best practices for constructing

propensity score models that include only pretreatment factors.35,36 We fit separate models for

each of the tumor subtype groups. We constructed a second set of propensity score weights

based on how a patient’s distant recurrence was first detected (asymptomatic imaging vs

patient-/provider-detected signs/symptoms) to ensure consistent findings. Propensity score

models included the full set of sociodemographic and tumor/treatment factors described above

and were constructed using the approach outlined by Xu et al (2010) to obtain stabilized

inverse propensity score weights.37 Patients were censored at the time they were lost to follow-

up or died.

The study team then calculated the number and proportion of patients who received

surveillance systemic imaging in the first 3 years from diagnosis. We specifically compared

sociodemographic, diagnosis, and treatment factors between the asymptomatic surveillance

imaging and sign-/symptom-detected recurrence groups, using t tests for continuous variables

and chi-square tests for categorical variables. We estimated this descriptive analysis

unweighted, then weighted by the propensity scores, to ensure comparable groups within each

of the tumor subtype groups. We then assessed the relationship between mode of recurrence

detection and days from initial cancer diagnosis to death, using propensity-weighted

multivariable Cox proportional hazards regression, stratified by tumor subtype group, given the

marked recurrence risk differences observed by these groups.25 Based on this model, we

estimated median survival within 5 years of diagnosis. Patients were censored at the time they

were lost to follow-up or died.

24

Sensitivity analyses assessed the potential for length bias, which is the concern that

faster-growing tumors and aggressive cancers may be detected as interval cancers by

signs/symptoms as opposed to asymptomatic imaging detecting slowly progressing cases. The

presence of length bias would result in an overestimated survival benefit for patients with

surveillance imaging-detected recurrences. As the potential for this bias would be greatest for

the group of patients with the most aggressive disease (triple negative), we re-estimated

models, excluding the following groups from this cohort: (1) patients who recurred with brain

metastases; (2) patients not treated with systemic therapy at the time of distant recurrence;

and (3) patients who recurred within 4 months of the start of surveillance. The study team

conducted analyses using SAS v9 (https://www.sas.com/en_us/software/sas9.html).

Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Posttreatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials

The 2 primary deliverables associated with aim 3 are the following: (1) identification of

priority comparator strategies for prospective randomized trials, and (2) creation of a decision

support tool to facilitate clinician communication with patients regarding risks during follow-up.

Identification of High-Priority Comparators

Over the course of the 3-year study, the research team engaged patients, clinicians, and

the multi-stakeholder group to finalize study methods and to help with interpreting results. In

addition to holding engagement sessions, we conducted an anonymous survey of Alliance

Breast Committee members (N = 27) to assess support for a prospective trial of surveillance

imaging. The survey asked providers about (1) their willingness to enroll patients in a clinical

trial of systemic imaging, and (2) the priority level of a clinical trial assessing the impact of

systemic imaging. In addition, the study team used an audience response system after the

presentation of study findings at the Alliance PAC meeting to assess feasibility. The audience

response system allowed presenters to poll attendees regarding the following topics: (1)

willingness to enroll in a trial and (2) whether the attendee considered the research to be of

high priority.

25

Creation of Decision Support Tool

The study team engaged multiple stakeholder groups (patient, clinicians, multi-

stakeholder group) and achieved consensus that, whereas survivorship care plans exist,

currently no decision support tool helps providers assess a patient’s longer-term risk of

recurrence and death. The single tool identified by the team was a patient management and

follow-up planning flow sheet endorsed by ASCO.38 This generic template, however, cautions

that patient variation is not taken into consideration; moreover, it provides no guidance on how

to tailor follow-up based on recurrence or toxicity risks that patients face given their unique

diagnosis and treatment characteristics.39

Stakeholders were engaged during the 3-year project to reach consensus about the

goals of the decision support tool; its key design requirements and features; and its content,

including inputs (patient, tumor, and treatment characteristics) and outcome measures. These

engagement sessions yielded consensus about the elements for the development of the initial

tool. To obtain this feedback, throughout the 3-year period the study team conducted separate

engagement sessions with the Alliance PAC and the multi-stakeholder group of clinicians and

patients.

The study programming team developed the style and layout template using Bootstrap

(https://getbootstrap.com/), an open-source, front-end framework (library) that offers a cost-

effective approach to designing websites. The framework includes HTML, CSS, and JavaScript

code to help developers build web applications for both desktop and mobile environments. The

back-end, graphic information relies on Highcharts (https://www.highcharts.com/). Free for

academic and nonprofit use, Highcharts is a charting tool that allows for graphical

representation of risk information. We developed a web-based platform to facilitate integration

of the decision support tool into the workflow of oncology specialty care and medical

encounters.

26

Changes to the Original Study Protocol

The study team made 2 minor, but necessary, changes to the analysis plan for 2 reasons:

(1) lower-than-anticipated surveillance imaging rates than had been previously reported in the

literature and (2) robust findings that did not lend themselves to a value of information (VOI)

analysis. First, in the original study protocol, we designed the analytic plan to assess the

relationship between receipt of asymptomatic imaging over time and the detection of

recurrence and survival. However, we found that 30% of women received 1 or more

asymptomatic systemic imaging scans for the purpose of surveillance during follow-up, and only

12% received 2 or more asymptomatic surveillance systemic scans of the same type over

time.40 This low proportion required a shift in the analytic plan, to assess the relationship

between detection of distant recurrences by asymptomatic imaging vs patient-/provider-

detected signs/symptoms and survival, as described in aim 2.

Second, our team originally proposed a VOI analysis in aim 3 as a tool for prioritizing the

need for future prospective comparative effectiveness trials. However, robust study results in

aim 2 identified 2 subgroups of patients who may potentially benefit from surveillance systemic

imaging—namely, patients with triple negative cancers and patients with HER2+ cancers.

Stakeholders, including cancer specialists and patients, broadly endorsed these groups and

comparators, rendering a VOI analysis unnecessary.

27

RESULTS

The results are presented by aim. The Alliance trials used for aim 1 included all

locoregional breast cancers—namely, stages I, II, and III. Because aim 2 examines the use of

systemic imaging to detect distant recurrence, we restricted the analysis to stages II and III.

Stage I patients were excluded because their distant recurrence rate and use of imaging are too

low to analyze with any meaningful power.

Specific Aim 1: Determine Risk and Patterns of Recurrence and Treatment Toxicities

Characteristics for the population of patients with receptor status and recurrence are

documented in Table 4 (n = 10 046). Approximately 55% of the patient population was ER/PR+,

HER2–, the mean age was 54.1 years, and 84.8% of the population was white.

Annual hazards of first recurrence by cancer stage are presented in Figure 2. Both stage

(p < 0.0001) and tumor biology type (p < 0.0001) influenced likelihood of recurrence within 5

years. The timing of recurrence varied by tumor biology type (p < 0.0001). Of recurrences, 75%

occurred by 3.3 years for ER/PR+, HER2–; 1.8 years for triple negative; 5.0 years for triple

positive; and 3.1 years for ER/PR–, HER2+. For stage III patients, triple negative tumors recurred

earlier and more often (5-year probability of recurrence was 43.1%) than did triple positive

tumors (5-year probability of recurrence was 16%), which were distributed over a longer time

frame (Table 5).

28

Table 4. Characteristics of Patients Enrolled in Alliance Trials

Patient Characteristics N = 10 046 %

Age Mean (SD)

54.1 (11.6 yrs)

Race White 8520 84.8%

Black 564 5.6%

Asian/Native Pacific Islander 619 6.2%

Other/unknown 345 3.4%

Tumor size < 2 cm 4658 46.4%

2-5 cm 4504 44.8%

> 5 cm or diffuse/ inflammatory

885 8.8%

Nodal status

Negative 5427 54.1%

1-3 positive nodes 3216 32.1%

≥ 4 positive nodes 1389 13.8%

Receptor type

ER or PR+, HER2– 5572 55.4%

ER and PR–, HER2– 1693 16.8%

ER or PR+, HER2+ 1560 15.5%

ER and PR–, HER2+ 1223 12.2% Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

Table 6 summarizes the model and personalized risk estimates for measures with

minimal missingness that explained the greatest proportion of variance in recurrence (tumor

biology group, cancer stage, age at diagnosis). We used these models to provide estimates for

the decision support tool developed as a part of specific aim 3.

29

Figure 2. Annual hazards of breast cancer first recurrence by stage at diagnosis

Abbreviations: ER, estrogen receptor; HER2neu, human epidermal growth factor receptor 2; PR, progesterone receptor.

Table 5. Cumulative Probability of Recurrence at 5 Years From Diagnosis

Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

Receptor Type Group Stage I Stage II Stage III

ER or PR+, HER2– 5.1% 9.1% 28.8%

ER and PR–, HER2– 7.0% 13.4% 43.1%

ER or PR+, HER2+ 0.9% 7.9% 15.9%

ER and PR–, HER2+ 3.1% 10.2% 26.5%

23

Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence

ER+ or PR+, HER2– ER– and PR–, HER2–

Patient/Tumor Characteristic

Coeff Standard Error

(Coeff)

Hazard Ratio

Z P Value Coeff Standard Error

(Coeff)

Hazard Ratio

Z P Value

Nodal status

Negative nodes REF -- -- -- -- REF -- -- -- --

Positive node 1.12 0.10 3.08 11.32 <0.0001 1.54 0.16 4.67 9.45 <0.0001

Tumor size

0-2 cm REF -- -- -- -- REF -- -- -- --

2-5 cm 0.27 0.10 1.31 2.61 0.009 0.45 0.16 1.58 2.86 0.0043

5+ cm 1.08 0.14 2.94 7.73 <0.0001 1.23 0.25 3.41 4.86 <0.0001

Age group

18-44 REF -- -- -- -- REF -- -- -- --

45-64 –0.46 0.11 0.63 –4.11 <0.0001 –0.42 0.16 0.66 –2.59 0.0097

65+ –0.28 0.13 0.76 –2.06 0.04 –0.62 0.25 0.54 –2.53 0.0115

24

Table 6. Relation Between Molecular Subtype, Tumor Size, Nodal Status, and Age on Time to First Recurrence, continued

ER– and PR–, HER2+ ER+ or PR+, HER2+

Patient/Tumor Characteristic

Coeff Standard Error

(Coeff)

Hazard Ratio

Z P Value Coeff Standard Error

(Coeff)

Hazard Ratio

Z P Value

Nodal status

Negative nodes REF -- -- -- -- REF -- -- -- --

Positive node 1.49 0.30 4.43 4.89 <0.0001

1.11 0.24 3.02 4.69 <0.0001

Tumor size

0-2 cm REF -- -- -- -- REF -- -- -- --

2-5 cm –1.67 0.72 0.19 –2.31 0.0207 –0.90 0.59 0.41 –1.51 0.13

5+ cm 1.14 0.43 3.14 2.68 0.0074 0.65 1.01 1.91 0.64 0.52

Age group

18-44 REF -- -- -- --

45-64 –0.08 0.15 0.92 –0.55 0.5824 –0.05 0.16 0.95 –0.30 0.76

65+ 0.33 0.27 1.40 1.21 0.2247 0.10 0.26 1.11 0.38 0.7 Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

25

Specific Aim 2: Evaluate Routine Surveillance Breast Imaging and Advanced Body Imaging

Approximately 12% of the 10 076 women in the cohort (n = 1220) with known receptor

status had a distant recurrence detected within the first 5 years of diagnosis. Five-year distant

recurrence was 21.9% for triple negative; 13.9% for HER2+; and 10.1% for ER/PR+, HER2–

patients.

Of the 1220 women with a distant recurrence with known ER, PR, and HER2 status, 78%

were younger than 70 at diagnosis, 45% had government-provided insurance, and 17% had 1 or

more chronic conditions (Table 7). Further, 50% of patients with a distant recurrence were

ER/PR+, HER2neu –; 28% were triple negative; and 22%, HER2+. Most women were treated at

community and comprehensive community cancer program facilities (81%). Women with a

distant recurrence detected by symptoms were more likely than women with a distant

recurrence detected by asymptomatic imaging to have had their index cancer be lobular,

treated with mastectomy, and treated in community cancer program facilities rather than

academic facilities. These differences were no longer statistically significant after propensity

score weighting.

26

Table 7. Characteristics of Stage II to III Breast Cancer Patients With Distant Recurrence by How Recurrence Was Detected (Sign/Symptom vs Asymptomatic Imaging Detected), Overall Population (Unweighted)

Patient Characteristics N Overall (N = 1220)

%

Symptom Detected (n = 936)

%

Asymptomatic Imaging

Detected (n = 284)

%

P Value

Sociodemographic Characteristics

Age 0.82

< 50 401 32.9 33.2 31.7

50-69 552 45.3 44.8 46.8

≥ 70 267 21.9 22.0 21.5

Race 0.08

White 976 80.0 81.0 76.8

Black 191 15.7 14.4 19.7

Other 53 4.3 4.6 3.5

Hispanic ethnicity 0.59

No 1041 85.3 85.9 83.5

Yes 61 5.0 4.8 5.6

Unknown 118 9.7 9.3 10.9

Mean percentage in patient zip code with less than high school degree

0.37

29% or more 237 19.4 18.5 22.5

20%-28.9% 272 22.3 22.7 21.1

14%-19.9% 271 22.2 22.5 21.1

Less than 14% 387 31.7 31.5 32.4

Unknown 53 4.3 4.8 2.8

Median household income in patient zip code 0.47

<$30 000 193 15.8 15.7 16.2

$30 000-$34 999 192 15.7 15.1 18.0

27

Patient Characteristics N Overall (N = 1220)

%

Symptom Detected (n = 936)

%

Asymptomatic Imaging

Detected (n = 284)

%

P Value

$35 000-$45 999 373 30.6 31.1 28.9

$46 000+ 409 33.5 33.3 34.2

Unknown 53 4.3 4.8 2.8

Insurance status 0.41

Private insurance/ managed care

624 54.0 54.4 52.8

Medicaid 137 11.9 12.5 9.9

Medicare and other government

379 32.8 32.0 35.5

Uninsured/self-pay/ insurance status unknown

15 1.3 1.1 1.8

Urban/rural 0.68

Rural 34 2.9 2.8 3.3

Urban 1140 97.1 97.2 96.7

Clinical Characteristics

Charlson/Deyo score (SD) 0.86

0 1014 83.1 83.0 83.5

1+ 206 16.9 17.0 16.6

Tumor Characteristics

Tumor size 0.46

< 2 cm 206 16.9 16.1 19.4

2-5 cm 715 58.6 58.7 58.5

≥ 5 cm 272 22.3 23.1 19.7

Missing 27 2.2 2.1 2.5

Nodal status 0.40

Negative 258 21.2 21.2 21.1

28

Patient Characteristics N Overall (N = 1220)

%

Symptom Detected (n = 936)

%

Asymptomatic Imaging

Detected (n = 284)

%

P Value

1-3 positive 382 31.3 32.6 27.1

4-9 positive 310 25.4 24.5 28.5

+9 positive 246 20.2 20.0 20.8

Uncertain/unsampled/ unknown

24 2.0 1.8 2.5

Histology 0.06

Ductal 1034 84.8 83.4 89.1

Lobular 109 8.9 9.5 7.0

Other 77 6.3 7.1 3.9

ER, PR, HER2 risk group 0.65

ER or PR+, HER2– 610 50.0 50.1 49.7

ER and PR–, HER2– 338 27.7 27.1 29.6

Chemotherapy 0.47

No 210 17.3 17.8 15.9

Yes 1002 82.7 82.2 84.1

Unknown

HER2 targeted therapy 0.40

No 1074 89.4 88.9 90.7

Yes 128 10.7 11.1 9.3

Endocrine therapy 0.71

No 518 43.4 43.6 42.4

Yes 677 56.7 56.4 57.6

Surgery type and radiation therapy 0.03

Breast-conserving surgery alone

346 28.6 30.0 23.8

29

Patient Characteristics N Overall (N = 1220)

%

Symptom Detected (n = 936)

%

Asymptomatic Imaging

Detected (n = 284)

%

P Value

Breast-conserving surgery + radiation

43 3.6 3.8 2.9

Mastectomy alone 538 44.4 42.2 52.0

Mastectomy + radiation 284 23.5 24.1 21.4

Facility type 0.04

Community cancer program/other

331 27.1 29.1 20.8

Comprehensive community cancer program

655 53.7 52.2 58.5

Academic/research program

232 19.0 18.6 20.4

Other 2 0.2 0.1 0.4 Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

We compared women whose recurrences were detected by asymptomatic imaging with

women whose recurrences were detected by signs or symptoms. After propensity weighting

based on receipt of surveillance systemic imaging within 3 years of diagnosis, women with triple

negative and HER2+ cancers had a reduced risk of death in 5 years (hazard ratio [HR] = 0.66,

95% CI, 0.48-0.91 and HR = 0.40, 95% CI, 0.24-0.68, respectively) if their recurrence had been

detected by asymptomatic imaging compared with if it had been detected by signs and

symptoms. By contrast, the 5-year mortality rate did not differ significantly based on whether

the distant recurrence was detected by asymptomatic imaging or by symptoms for patients

with ER/PR+ HER2– (HR = 1.2; 95% CI, 0.93-1.60) cancer (Table 8).

25

Table 8. Unweighted and Weighted Association Between Asymptomatic vs Symptom Detected Distant Recurrences and Time to Death by Molecular Subtype Risk Group for Women Diagnosed With Stage II to III Breast Cancera

Propensity Weight Based on Receipt of Surveillance

Systemic Imaging Within 3 Years of Diagnosis b

Propensity Weight Based on How Recurrences Detected (Asymptomatic Imaging vs

Signs/Symptoms)c

Tumor Subtype Risk Group

Unweighted Unweighted, Covariates

Weighted, No Covariates

Weighted, Covariates

Weighted, No Covariates

Weighted, Covariates

N HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI)

ER or PR+, HER2–

610 1.22 1.17 1.21 1.22 1.03 1.14

(0.96-1.55) (0.89-1.53) (0.93-1.56) (0.93-1.60) (0.80-1.33) (0.87-1.48)

ER and PR –, HER2–

338 0.68 0.64 0.74 0.66 0.72 0.64

(0.51-0.89) (0.46-0.89) (0.55-0.98) (0.48-0.91) (0.53-0.97) (0.47-0.88)

HER2+ 272 0.64 0.42 0.63 0.40 0.69 0.56

(0.43-0.94) (0.25-0.69) (0.41-0.98) (0.24-0.68) (0.46-1.04) (0.36-0.87) Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. aBolded values indicate significant differences in significant hazard ratios (HRs); p < 0.05. bPropensity weight constructed using full cohort of 10 076 patients. Adjusted for propensity to receive surveillance systemic imaging within 3 years of diagnosis. cPropensity weight constructed using cohort of patients with distant recurrence within 5 years of diagnosis (n = 1220). Adjusted for propensity to have recurrence detected by asymptomatic systemic imaging vs by signs/symptoms.

25

These estimates translated to a between-group difference in weighted median survival

of 5 months for triple negative (median of 39 months for patients diagnosed on asymptomatic

imaging as compared with 34 months for patients diagnosed on signs and symptoms) and 13

months for HER2-amplified patients (median of 64 months for patients diagnosed on

asymptomatic imaging as compared with 51 months for patients diagnosed on signs and

symptoms; Table 9).

Table 9. Percentage of Patients Surviving Until Years 3 to 4 and Median Survival for Patients With Triple Negative and HER2+ Stage II to III Breast Cancer, Propensity Weighted Based on Receipt of Surveillance Within 3 Years Of Diagnosis

Tumor Subgroup N Year 3 Percentage (SD)

Year 4 Percentage (SD)

Survival in Months

Median (95% CI)

ER– and PR–, HER2–

Asymptomatic 84 54% ± 6% 36% ± 6% 39.0 (33.1-45.3)

Signs/symptoms 254 45% ± 3% 27% ± 3% 33.7 (30.2-37.7)

HER2+

Asymptomatic 59 85% ± 5% 68% ± 7% 63.6 (49.8-5+ years)a

Signs/symptoms 213 68% ± 4% 53% ± 4% 51.4 (44.2-57.9) Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor. a Greater than 50% of cohort survived beyond 5 years from diagnosis.

Sensitivity Analysis

Sensitivity analyses yielded a consistent pattern of findings for the triple negative group

after we excluded 3 groups of patients: (1) patients with brain metastasis (HR = 0.60; 95% CI,

0.42-0.85); (2) patients not treated with systemic therapy at the time of recurrence (HR = 0.49;

95% CI, 0.30-0.80); and (3) patients who had cancer that recurred within 4 months of the start

of surveillance (HR = 0.69; 95% CI, 0.47-1.02).

26

Specific Aim 3: Engage Stakeholders to Develop a Patient-Centered Risk-Based Tailored Approach to Post-treatment Surveillance and Identify the Highest-Priority Comparators for Prospective Randomized Trials

Our stakeholders generally agreed that 2 key findings from this study have several

immediate implications for clinical practice. First, more than two-thirds of women diagnosed

with breast cancer have ER/PR+, HER2– tumors. This group, in our study, had no demonstrated

potential benefit from current advanced imaging in the context of surveillance. Second, for the

first time, we identified subgroups of patients for whom surveillance imaging may improve

survival outcomes.

In addition to the findings above, through stakeholder engagement the study team

successfully identified high-priority comparators for a future prospective randomized trial, and

developed an initial decision support tool that provides clinicians with a risk-based tailored

decision support tool.

Identification of High-Priority Comparators

Stakeholders expressed support for a prospective randomized trial to investigate the

use of asymptomatic surveillance systemic imaging in triple negative and HER2+ patients. An

anonymous survey of Alliance Breast Committee members (N = 27) confirmed support for a

prospective trial of surveillance imaging. The following characteristics describe the

respondents: 23% were surgical oncologists, 65% were medical oncologists (currently

responsible for most surveillance for distant recurrence), 4% were radiation oncologists, and 8%

were other specialties (eg, general surgery); 30% practiced in a community setting; 85%

reported that more than one-half of their practices were dedicated to the care of women with

breast cancer; and 81% had been in practice for more than 6 years.

Of respondents, 65% supported enrolling both triple negative and HER2+ patients who

were stage II and III at diagnosis (Figure 3). In addition, a large majority of providers (81%)

indicated that a clinical trial examining the impact of systemic imaging was intermediate to high

priority (data not shown).

27

Figure 3. Alliance Breast Committee support for clinical trial

28

After presenting study findings at the Alliance PAC meeting, audience response system

data confirmed that patients viewed results as meaningful, with further research possibly or

definitely justified (84%). Further, nearly two-thirds (61%) indicated that they would be willing

to enroll in a trial in which they were randomized to either standard of care or standard of care

with the addition of surveillance imaging. These findings, coupled with engagement of our

multi-stakeholder group, indicated strong support for a clinical trial, without the need for a

formal VOI analysis, given the clear inclusion criteria of the logical next trial. A 2016 Cochrane

review assessed the role of advanced imaging on recurrence detection and survival. It

concluded that a targeted imaging trial that takes into consideration advances in biological

knowledge and diagnostic modalities is needed.15

Creation of Decision Support Tool

Through our stakeholder engagement activities, we built consensus about the goal of

the decision support tool as well as its design requirements and special features (Figure 4).

Figure 4. Stakeholder-derived decision support tool design considerations

Goal of Tool • Describe risks patients face after active treatment (toxicities, recurrence, death)

in a way that can be readily communicated. • Inform how often patients should be seen after active treatment (not

prescriptive). Design Requirements

• Target audience: Clinicians involved in follow-up care for women with diagnosis of stage I to III breast cancer

• Presentation: Web-based, including images and text (multiple formats, web, ability to print out)

• English language only • Easy-to-use interface—checkboxes and radio buttons for relevant inputs • Ability to implement in clinical encounter

Special Features • Tailored summary display of key outcome information based on identified

patient and tumor characteristics • Ability to expand to include values clarification (future research direction) • Print-out, paper-based version (brief 1-2 pages)

29

Consensus was also achieved regarding the content for the decision support tool,

including key input factors and outputs as well as future inputs that stakeholders deemed to be

important but are not currently included in available data (Figure 5).

Figure 5. Base inputs and outputs for decision support tool

Abbreviations: BMI, body mass index; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

30

The study team used data from aim 1 to construct all key input and outcome measures.

We also successfully completed constructing a prototype web-based decision support tool with

a live active link. The initial tool is fully programmed; we developed the front-end interface and

the back-end data structure for all key end points and a graphical representation of outcome

measures. The decision support tool has 3 components:

1. An initial page in which clinicians enter patient characteristics from the time of

diagnosis (Figure 6). We made the selection of inputs after 3 years of engagement with

all stakeholder groups.

2. After selecting the “calculate” button, clinicians are taken to a screen where they can

see a graphical representation of their patient’s personalized risks of first local and first

distant recurrence and risk of death based on their input values (Figure 7).

3. Stakeholders (both clinicians and patients) consistently described the need for a tool in

which end points could be turned off for final patient display, depending on whether

patients wanted to see a given end point (eg, death). We integrated this functionality

into the decision support tool. Unchecking factors in the “prepare for print” section and

navigating to the next screen automatically removes the deselected end point from the

graphs.

31

Figure 6. Initial page of data entry for clinicians in the decision support tool

32

Figure 7. Output page in the decision support tool

33

DISCUSSION

Context for Study Results

Breast cancer is no longer considered a single disease. Biologic markers have been

integrated into the American Joint Committee on Cancer version 8 staging guidelines that were

implemented with diagnoses beginning January 1, 2018, reflecting the critical role tumor

biology plays in recurrence and mortality risk.41 Consistent with prior investigations, our

analyses in aim 1 demonstrated higher recurrence and mortality for patients diagnosed with

triple negative and ER/PR–, HER2+ cancers25 than for patients diagnosed with ER/PR+, HER2–

cancers. Furthermore, this aim demonstrates that the best outcomes exist for patients with

ER/PR+, HER2+ cancers treated with modern trastuzumab-based therapies, which are just

emerging and should be incorporated into clinical practice. Based on this information, we have

developed a decision support tool that can support individualized decision-making regarding

follow-up visits.

Prior research has not assessed the impact of modern-era surveillance imaging on

survival in these subgroups. This gap is attributable mainly to advances in surveillance imaging

and treatment strategies that have occurred in the time since definitive studies that inform the

current guidelines were conducted.10-14 Aim 2 represents the first national study to

demonstrate a survival advantage with asymptomatic detection of distant metastases for

patients, with the benefit limited to triple negative and HER2+ disease.

Cancer survivors, patient advocates, and multidisciplinary breast cancer clinicians were

engaged throughout the study, including as members of the core research team. Engaging

these stakeholders separately as well as together as part of a multi-stakeholder advisory group

was critical to the study team gaining a comprehensive understanding of the content and

communication of post-active treatment follow-up care. Bringing patients and provider

stakeholders together was also important for working through key design considerations as

well as interpreting and communicating results.

34

Generalizability of Findings

We sought to maximize generalizability through the use of 2 unique data sources: data

from past trials conducted in the Alliance and the NCDB. A significant strength of using

randomized trials is standardized data collection and data elements, including the collection of

detailed diagnosis, treatment, and recurrence information. Nevertheless, 2 limitations of

randomized trials must be acknowledged: (1) Clinical trial settings may not reflect real-world

conditions, thus limiting generalizability; (2) as with all secondary analyses, we are limited to

existing data elements. By combining multiple trials to study a question unrelated to the

primary study design of the trials, we mitigate the first limitation. In addition, we addressed

these limitations by including real-world registry data from the NCDB, thus mitigating the

second limitation. The NCDB data have been augmented by registrars to include the collection

of cancer follow-up care. The NCDB captures 70% of the cancers diagnosed in the United States

annually and contains data on more than 30 million cancer patients in the United States; thus, it

optimizes external validity and generalizability.

Implementation of Study Results

We have developed a decision support tool that will be made available on multiple

websites, including those of the Alliance and the University of Wisconsin Department of

Surgery. Placing the tool on multiple websites will facilitate the use of aim 1 findings to inform

individual clinician–patient decision-making.

In aim 2, we found no evidence that asymptomatic detection of distant recurrence

confers a survival advantage for women with ER/PR+, HER2– disease, a patient population that

comprises more than two-thirds of women diagnosed with breast cancer at a locoregional

(nonmetastatic) stage.25 This finding is consistent with overall results reported in the older

randomized trials on which current guidelines are based. This body of evidence suggests that

most women diagnosed with stage II or III breast cancer would not benefit from surveillance

systemic imaging during routine follow-up. We conclude, therefore, that no further study is

warranted and practice should remain as it is.10-14

35

We did, however, find a survival advantage for patients with stage II and III cancers that

are triple negative or HER2+. On their own, these findings, based on observational data, are

insufficient evidence to change clinical practice. Nevertheless, we identified a patient subgroup

for which a randomized trial is warranted. This conclusion is consistent with an explicit

recommendation made by the authors of the 2016 Cochrane review on the topic.15 In the

meantime, we continue to support shared decision-making and the cautious application of

existing evidence regarding limitations of surveillance imaging for breast cancer.

Subpopulation Considerations

Heterogeneity of patient risk forms the basis of our underlying theory; it is the main

premise for the study. Our team’s primary goal was to inform the design of a new evidence-

based, tailored, and patient-centered approach to surveillance, and to identify the highest-

value prospective trials to support it. We have identified significant variation in risk of

recurrence and death according to tumor biology group and stage. Furthermore, we have

shown that tumor biology groups that are associated with higher risks of recurrence and death

at 3 and 5 years may benefit from the use of asymptomatic surveillance systemic scans.25

Study Limitations

Several limitations should be considered. First, some analyses were restricted by the

data elements that were collected as part of previous clinical trial designs (aim 1) or as part of

the cancer registry, or that were abstractable on medical record review (aim 2). For example,

our pilot study demonstrated that family history, including genetic predispositions, could not be

reliably abstracted from medical records; such history information is not available

systematically across legacy clinical trials, despite the known importance of such risk factors. In

addition, our aim 2 analysis depended on the ability of registrars to reliably abstract how

distant recurrences were first detected. Our team provided detailed abstraction instructions

that were piloted extensively by trained cancer registrars at each site. To ensure consistency,

for all patients with distant recurrence, our team compared how distant recurrences were

detected with the indications for and results from the systemic imaging that preceded the

36

recurrence diagnosis. Second, to allow for sufficient follow-up, the data used in each aim were

by necessity at least 10 years old, which may not reflect the most modern therapies. Third,

patients enrolled in clinical trials may not reflect the general population in terms of race and

competing risks, limiting the generalizability of aim 1. However, the data set created as a part of

aim 1 is the only large data set of its kind with systematic collection of diagnosis factors and

detailed treatment information as well as recurrence. As cancer registries continue to explore

integrating the collection of recurrence, it will be important to use these data to confirm study

results. In aim 2, because of the need to use deidentified data based on NCDB agreements with

participating hospitals, we were not able to validate directly the abstraction of scan intent or

recurrence, although the availability of patient records abstracted from 2 facilities by different

registrars afforded a unique opportunity to assess reliability.

Two final limitations have implications for future clinical trial design, as described in aim

3. First, in aim 2, we used 2006-2007 diagnoses to allow for the collection of 5-year follow-up

information. HER2 status was not recorded consistently. Trastuzumab, the targeted therapy

given to patients who have HER2+ tumors, was not routinely administered during these years; it

was missing for 7% of patients despite reabstraction of this information. Only 39% of HER2+

patients who were sampled in 2006-2007 received HER2 targeted therapy at the time of

diagnosis. The magnitude of the survival advantage observed for this subgroup is, therefore,

likely overestimated, which emphasizes the need for a randomized controlled trial but also

underscores that this should be accounted for in power calculations. Second, observational

studies do not allow for the complete control of differences between asymptomatic and

symptom-detected recurrences, and they cannot consider biological differences between these

recurrences that may influence survival; however, our findings were robust in sensitivity

analyses, including models restricted by site of recurrence and to patients who received

treatment for recurrence of cancer.

Future Research

Our research provides further evidence that patient risk of recurrence and mortality

varies by receptor status. This finding suggests current “one-size-fits-all” follow-up guidelines

37

can be better tailored to the risks that patients face following active treatment. We designed

our initial decision support tool for integration into clinical workflow; however, future

refinement of the tool is planned to include psychosocial patient characteristics, such as risk

aversion and state-trait anxiety, as well as other important patient factors, such as genetic

predisposition and family history. Future clinical trials should integrate this information into

standard forms to aid investigation into how these factors influence the timing and magnitude

of recurrence risk. Future implementation of the decision support tool that we developed will

necessarily require usability testing, clinician and patient interviews, and a formal assessment

of key dissemination and implementation considerations (acceptability, appropriateness,

feasibility in a clinical setting, clinician update, barriers and facilitators to large-scale

implementation).

A prospective randomized controlled trial is currently being developed by the Alliance

for Clinical Trials in Oncology Breast Committee to investigate the effectiveness of

asymptomatic surveillance imaging in triple negative and HER2+ patients.

38

CONCLUSIONS

This investigation supports the importance of considering not only tumor size and nodal

status but also receptor status in staging breast cancer, as these factors influence the likelihood

of recurrence within 5 years. Given the predictable variation in the likelihood and timing of

recurrence, these data also support the need for a more personalized approach to follow-up

than current “one-size-fits-all” guidelines. We have developed a decision support tool to aid

clinicians in identifying the optimal follow-up approach based on an individual patient’s risk.

Further, this is the first national study to show a potential survival advantage with

asymptomatic detection of distant metastases for a subset of breast cancer patients following

active treatment. The benefit is limited to 2 subgroups of patients. We found no survival

advantage to surveillance imaging for distant metastatic disease in asymptomatic women with

ER/PR+, HER2– tumors, who represent nearly two-thirds of all breast cancer survivors. By

contrast, we detected a survival advantage to systemic imaging for triple negative and HER2+

breast cancer; however, this observational study can only demonstrate correlation, not

causation. Therefore, this finding should justify investment in a large prospective trial to

address this question.

39

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ACKNOWLEDGMENTS

The authors wish to thank cancer registrars and cancer physician liaisons at all

participating Commission on Cancer–accredited facilities and National Cancer Database staff for

their contributions and dedication to this project. We additionally are grateful to all

stakeholders who participated in this project. Other members of the Alliance ACS-CRP Cancer

Care Delivery Research PCORI Breast Cancer Surveillance Working Group include Karla Ballman,

PhD; Elizabeth Berger, MD; Nicole Brys, MPH; Elizabeth Burnside, MD, MPH, MS; Ronald Chen,

MD, MPH; Patrick Gavin, RPh; Bettye Green, RN; Ann Partridge, MD, MPH; Jane Perlmutter,

PhD, MBA; Rinaa Punglia, MD, MPH; Kathryn Ruddy, MD; Deborah Schrag, MD, MPH; and Ying

Zhang, PhD.

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Copyright© 2020. The Alliance for Clinical Trials in Oncology Foundation. All Rights Reserved.

Disclaimer:

The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.

Acknowledgement:

Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#CE-1304-6543) Further information available at: https://www.pcori.org/research-results/2013/follow-care-strategies-after-treatment-breast-cancer