Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening1/31 University of Pittsburgh Medical Center (UPMC) Magee-Womens Hospital (MWH)

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


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


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”)


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


Cervical cancerCervical cancer

HPV infection

Cervical abnormality

CancerHSIL

ASC-HAGCLSIL

ASCUS

Persistent HPV infection

Cervical pre-cancer


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)


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


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)



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.


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


Dynamic Bayesian networks: Unrolling the modelDynamic Bayesian networks: Unrolling the model

step 0 step 1 step 2


Dynamic Bayesian networks (DBNs): Quantitative partDynamic Bayesian networks (DBNs): Quantitative part


Dynamic Bayesian networks: Temporal evidenceDynamic Bayesian networks: Temporal evidence

Pr(Cervixt (abnormal) | Evidence ) = ?

Evidence = Papt=0 (negative), Papt=2(abnormal), Papt=3(abnormal), ….


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



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


The follow-up dataThe follow-up data

time

patient 1

patient 2

patient 3

patient 4



time

patient 1

patient 2

patient 3

patient 4



• 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


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)


The Pittsburgh Cervical Cancer Screening Model: Static versionThe Pittsburgh Cervical Cancer Screening Model: Static version

19 variables; 278,178 numerical parameters


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


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


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




PCCSM: Probability for precancer and invasive cervical cancer given patient prior historyPCCSM: Probability for precancer and invasive cervical cancer given patient prior history


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:


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


PCCSM: Web-based interface for individualized risk assessmentPCCSM: Web-based interface for individualized risk assessment

Web-based user interface for cytotechnologists


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


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


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.

Documents

Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening1/31 University of Pittsburgh Medical Center (UPMC) Magee-Womens Hospital (MWH)