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University of Pittsburgh Medical Center (UPMC) Magee-Womens Hospital (MWH) Department of Pathology. Bayesian Modelling for Clinical Decision Support when Screening for Cervical Cancer. Agnieszka Oniśko. joint work with R. Marshall Austin and Marek J. Dru ż d ż el. - PowerPoint PPT Presentation
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Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 1/31
University of Pittsburgh Medical Center (UPMC)
Magee-Womens Hospital (MWH) Department of Pathology
Bayesian Modelling for Clinical Decision Support when Screening
for Cervical Cancer
Agnieszka Oniśko
Can Systems Biology Aid Personalized Medication? Linköping, December, 5th 2011
joint work with R. Marshall Austin and Marek J. Drużdżel
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 2/31
Overview of this talkOverview of this talk
1. Screening for cervical cancer
2. Dynamic Bayesian networks
3. The Pittsburgh Cervical Cancer Screening Model (PCCSM)
4. Personalized screening for cervical cancer with PCCSM
5. Conclusions
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 3/31
Cervical cancer death rates mapCervical cancer death rates map
WHO: age-standardized death from cervical cancer per 100,000 inhabitants in 2004
(from “less than 2” to “more than 26”)
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 4/31
Human PapillomaVirusHuman PapillomaVirus
• HPV = Human PapillomaVirus
• There are around 150 HPV types identified
• About 30-40 HPV types are typically transmitted through sexual contact and infect the anogenital region
• Dr. Harald zur Hausen (German Cancer Research Centre, Heidelberg) was awarded 2008 Nobel Prize in Physiology or Medicine for his discovery of human papilloma viruses causing cervical cancer
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 5/31
Cervical cancerCervical cancer
HPV infection
Cervical abnormality
CancerHSIL
ASC-HAGCLSIL
ASCUS
Persistent HPV infection
Cervical pre-cancer
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 6/31
Screening tests for cervical cancerScreening tests for cervical cancer
1. Pap test (cytology): tells about changes in cervix
Cervical abnormality
CancerHSIL
ASC-HAGCLSIL
ASCUS
2. HPV test: tells about the presence of infection
3. Visual inspection of the cervix, using acetic acid (VIA) or Lugol’s iodine (VILI) to highlight pre-cancerous lesions (this testing is used in low-resource countries)
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 7/31
Pap (cytology) test (Papanicolaou test) vs. cervical cancer death ratesPap (cytology) test (Papanicolaou test) vs. cervical cancer death rates
Georgios Nicholas Papanicolaou (1883 – 1962)
Source: Cancer Facts&Figures 2010, American Cancer Society
38 20 8
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 8/31
HPV vaccineHPV vaccine
• Around 15 (out of 150) are classified as high-risk HPV types
• Two types of high risk HPV: HPV16, HPV18 cause around 70% of cervical cancer cases
• Two different vaccines available: cover two types of high risk HPV (HPV16 and HPV18)
• Introduction of HPV vaccine: June 2006 (USA)
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 9/31
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 10/31
ObjectivesObjectives
Employ Bayesian network modelling to create a quantitative multivariable model of cervical cancer screening, which reflects data from a large health system using the latest advances in screening and prevention technologies.
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 11/31
Dynamic Bayesian networks (DBNs): Qualitative partDynamic Bayesian networks (DBNs): Qualitative part
BN models consist of:― random variables― static arcs
DBN modelBN model
In addition to BN models:
- temporal arcs
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 12/31
Dynamic Bayesian networks: Unrolling the modelDynamic Bayesian networks: Unrolling the model
step 0 step 1 step 2
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 13/31
Dynamic Bayesian networks (DBNs): Quantitative partDynamic Bayesian networks (DBNs): Quantitative part
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 14/31
Dynamic Bayesian networks: Temporal evidenceDynamic Bayesian networks: Temporal evidence
Pr(Cervixt (abnormal) | Evidence ) = ?
Evidence = Papt=0 (negative), Papt=2(abnormal), Papt=3(abnormal), ….
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 15/31
DBN: Results of reasoningDBN: Results of reasoningP
r(C
ervi
xt |
Evi
den
ce)
The DBN model computes the probability of cervical abnormality over time given observations
time
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 16/31
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 17/31
105,248 102,886
58,342
108,554113,197
111,019
97,144
27,98125,77130,150
18,652
9,120
21,005
30,717
11,28712,26811,79810,59011,009
7,5008,205
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2006 2007 2008 2009 2010 Jan-Jul2011
Pap tests HPV tests Histological data
The Magee-Womens Hospital dataThe Magee-Womens Hospital data
72,657 data entries: biopsies and surgical procedures
696,390 Pap test results163,396 HPV test results
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 18/31
The follow-up dataThe follow-up data
time
patient 1
patient 2
patient 3
patient 4
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 19/31
The follow-up dataThe follow-up data
time
patient 1
patient 2
patient 3
patient 4
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 20/31
The follow-up dataThe follow-up data
• 241,136 patient cases
• year 0: indicates the year when a patient showed up for a screening test for the first time
100.0%
65.6%
56.7%
44.1%
34.2%
24.7%
10.5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
year 0 year 1 year 2 year 3 year 4 year 5 year 6
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 21/31
Clinical data
Cytology data
Histology data
HPV data
Expert knowledge
numerical parameters
graphical structure
Co
Pa
th s
ys
tem
The Pittsburgh Cervical Cancer Screening Model (PCCSM)The Pittsburgh Cervical Cancer Screening Model (PCCSM)
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 22/31
The Pittsburgh Cervical Cancer Screening Model: Static versionThe Pittsburgh Cervical Cancer Screening Model: Static version
19 variables; 278,178 numerical parameters
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 23/31
Patient Data (history data and current state)
Cervical Precancer and Cancer Probability over Time
The Pittsburgh Cervical Cancer Screening Model: Dynamic versionThe Pittsburgh Cervical Cancer Screening Model: Dynamic version
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 24/31
PCCSM: Probability for precancer and invasive cervical cancer given patient prior historyPCCSM: Probability for precancer and invasive cervical cancer given patient prior history
history record:
0%
5%
10%
15%
20%
25%
30%
35%
40%
0 1 2 3 4 5
years from ASCUS HPV(-) result
cum
ula
tive
ris
k o
f p
reca
nce
r+
SUSP Malignant Cells
One HSIL result
Two Positive HPV results
One Positive HPV result
AGC result
One ASC-H result
One LSIL result
ASCUS result
Two Negative Pap results
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 25/31
history record:
0%
10%
20%
30%
40%
50%
60%
0 1 2 3 4 5
years from ASCUS, HPV(-) result
cum
ula
tive
ris
k o
f p
reca
nce
r+
precancer: year ago
precancer: 2 years ago
precancer: 3 years ago
precancer: 4 years ago
precancer: 5 years ago
PCCSM: Probability for precancer and invasive cervical cancer given patient prior historyPCCSM: Probability for precancer and invasive cervical cancer given patient prior history
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 26/31
Magee-Womens Hospital: Pathology department data managementMagee-Womens Hospital: Pathology department data management
• CoPath: computer system that stores patient medical records
• CoPath indicates high risk patients if any of four variables is present (for example: a patient had cervical precancer in the past).
Cytotechnologists
Cytopathologists
• The results of screening tests are interpreted by:
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 27/31
Magee-Womens Hospital: Pathology department data managementMagee-Womens Hospital: Pathology department data management
Low risk patient or negative
screening test result?
Screening test performed
Signed out by cytotechnologists
Reviewed and signed out by cytopathologists
Yes
No
Screening test result reviewed by cytotechnologists
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 28/31
PCCSM: Web-based interface for individualized risk assessmentPCCSM: Web-based interface for individualized risk assessment
Web-based user interface for cytotechnologists
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 29/31
The PCCSM model
Web-based interface
CoPath system
Processed CoPath
Data
PCCSM: Risk assessment tool at Magee-Womens HospitalPCCSM: Risk assessment tool at Magee-Womens Hospital
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 30/31
There are no complete follow-up data:
– only 20% of cytology data is followed by HPV test results
– only 12% of cytology data is followed by histological results
– only 1-30% of cytology data is followed by clinical findings (for example: no information on smoking status in our data)
• Seven years worth of data (only?)
ChallengesChallenges
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening 31/31
ConclusionsConclusions
• The Pittsburgh Cervical Screening Model (PCCSM) is a dynamic Bayesian network that reflects prevalent current use in the U.S. of advanced screening technologies.
• The PCCSM identifies groups of patients that are at different risk levels for developing cervical pre-cancer and cervical cancer, based on both combinations of current test results and varying prior history.
• Both the current and near term (1-5 yrs) future risk of precancer and invasive cervical cancer in the PCCSM are most strongly correlated with the degree of cytologic abnormality.
• PCCSM quantitative risk assessments can be used as a personalized aid in clinical management and follow-up decision-making.