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. Drużdżel
Can Systems Biology Aid Personalized Medication?
Linköping, December, 5th 2011
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening
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Overview 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
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Cervical 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”)
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Human 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
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Cervical cancer
HPV infection
Persistent
HPV infection
Cervical abnormality
Cancer
HSIL
ASC-H
AGC
LSIL
ASCUS
Cervical pre-cancer
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Screening tests for cervical cancer
1. Pap test (cytology): tells about changes in cervix
Cervical abnormality
Cancer
HSIL
ASC-H
AGC
LSIL
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)
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Pap (cytology) test (Papanicolaou test)
vs. cervical cancer death rates
38
20
8
Georgios Nicholas
Papanicolaou
(1883 – 1962)
Source: Cancer Facts&Figures 2010, American Cancer Society
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HPV 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)
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Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening
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Objectives
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.
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Dynamic Bayesian networks (DBNs): Qualitative part
BN model
BN models consist of:
― random variables
― static arcs
DBN model
In addition to BN models:
- temporal arcs
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Dynamic Bayesian networks: Unrolling the model
step 0
step 1
step 2
Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening
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Dynamic Bayesian networks (DBNs): Quantitative part
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Dynamic Bayesian networks: Temporal evidence
Pr(Cervixt (abnormal) | Evidence ) = ?
Evidence = Papt=0 (negative), Papt=2(abnormal), Papt=3(abnormal), ….
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DBN: Results of reasoning
Pr(Cervixt | Evidence)
The DBN model computes the probability of cervical
abnormality over time given observations
time
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The Magee-Womens Hospital data
120,000
113,197
111,019
100,000
108,554
105,248
102,886
97,144
80,000
58,342
60,000
40,000
30,150
25,771
27,981
30,717
18,652
20,000
11,009
9,120
10,590
21,005
11,798
12,268
11,287
8,205
7,500
0
2005
2006
2007
2008
2009
2010
Jan-Jul
2011
696,390 Pap test results
Pap tests
HPV tests
Histological data
163,396 HPV test results
72,657 data entries: biopsies and surgical procedures
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The follow-up data
patient 1
patient 2
patient 3
patient 4
time
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The follow-up data
patient 1
patient 2
patient 3
patient 4
time
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The follow-up data
100%
100.0%
90%
80%
65.6%
70%
56.7%
60%
44.1%
50%
34.2%
40%
24.7%
30%
20%
10.5%
10%
0%
year 0
year 1
year 2
year 3
year 4
year 5
year 6
• year 0: indicates the year when a patient showed up for a
screening test for the first time
• 241,136 patient cases
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The Pittsburgh Cervical Cancer
Screening Model (PCCSM)
graphical structure
CoPath system
Expert
knowledge
Clinical data
Cytology data
Histology data
HPV data
numerical
parameters
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The Pittsburgh Cervical Cancer
Screening Model: Static version
19 variables; 278,178 numerical parameters
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The Pittsburgh Cervical Cancer
Screening Model: Dynamic version
Patient Data (history data and current state)
Cervical Precancer and Cancer
Probability over Time
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PCCSM: Probability for precancer and invasive
cervical cancer given patient prior history
40%
history record:
cumulative risk of precancer+
35%
SUSP Malignant Cells
One HSIL result
30%
Two Positive HPV results
25%
One Positive HPV result
20%
AGC result
One ASC-H result
15%
One LSIL result
10%
ASCUS result
Two Negative Pap results
5%
0%
0
1
2
3
4
5
years from ASCUS HPV(-) result
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PCCSM: Probability for precancer and invasive
cervical cancer given patient prior history
cumulative risk of precancer+
60%
history record:
50%
precancer: year ago
40%
precancer: 2 years ago
30%
precancer: 3 years ago
precancer: 4 years ago
20%
precancer: 5 years ago
10%
0%
0
1
2
3
4
5
years from ASCUS, HPV(-) result
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Magee-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).
• The results of screening
tests are interpreted by:
Cytotechnologists
Cytopathologists
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Magee-Womens Hospital: Pathology
department data management
Screening test performed
Screening test result reviewed
by cytotechnologists
Low risk patient or
negative screening
test result?
Yes
Signed out by
cytotechnologists
No
Reviewed and signed out by cytopathologists
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PCCSM: Web-based interface for
individualized risk assessment
Web-based user
interface for
cytotechnologists
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PCCSM: Risk assessment tool
at Magee-Womens Hospital
The PCCSM model
Web-based interface
CoPath system
Processed
CoPath
Data
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Challenges
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?)
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Conclusions
• 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.
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