1. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening1/31 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, 5 th 2011 joint work with R. Marshall Austin and Marek J. Drużdżel

2. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening2/31 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

3. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening3/31 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”)

4. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening4/31 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

5. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening5/31 Cervical cancer HPV infection Cervical abnormality Cancer HSIL ASC-H AGC LSIL ASCUS Persistent HPV infection Cervical pre-cancer

6. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening6/31 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)

7. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening7/31 Pap (cytology) test (Papanicolaou test) vs. cervical cancer death rates Georgios Nicholas Papanicolaou (1883 – 1962) Source: Cancer Facts&Figures 2010, American Cancer Society 38208

8. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening8/31 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)

9. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening9/31

10. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening10/31 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.

11. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening11/31 Dynamic Bayesian networks (DBNs): Qualitative part BN models consist of: ―random variables ―static arcs DBN modelBN model In addition to BN models: - temporal arcs

12. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening12/31 Dynamic Bayesian networks: Unrolling the model step 0 step 1 step 2

13. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening13/31 Dynamic Bayesian networks (DBNs): Quantitative part

14. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening14/31 Dynamic Bayesian networks: Temporal evidence Pr(Cervix t (abnormal) | Evidence ) = ? Evidence = Pap t=0 (negative), Pap t=2 (abnormal), Pap t=3 (abnormal), ….

15. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening15/31 DBN: Results of reasoning Pr(Cervix t | Evidence) The DBN model computes the probability of cervical abnormality over time given observations time

16. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening16/31

17. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening17/31 The Magee-Womens Hospital data 72,657 data entries: biopsies and surgical procedures 696,390 Pap test results 163,396 HPV test results

18. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening18/31 The follow-up data time patient 1 patient 2 patient 3 patient 4

19. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening19/31 The follow-up data time patient 1 patient 2 patient 3 patient 4

20. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening20/31 The 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

21. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening21/31 Clinical data Cytology data Histology data HPV data Expert knowledge numerical parameters graphical structure CoPath system The Pittsburgh Cervical Cancer Screening Model (PCCSM)

22. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening22/31 The Pittsburgh Cervical Cancer Screening Model: Static version 19 variables; 278,178 numerical parameters

23. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening23/31 Patient Data (history data and current state) Cervical Precancer and Cancer Probability over Time The Pittsburgh Cervical Cancer Screening Model: Dynamic version

24. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening24/31 PCCSM: Probability for precancer and invasive cervical cancer given patient prior history

25. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening25/31 PCCSM: Probability for precancer and invasive cervical cancer given patient prior history

26. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening26/31 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). Cytotechnologists Cytopathologists The results of screening tests are interpreted by:

27. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening27/31 Magee-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

28. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening28/31 PCCSM: Web-based interface for individualized risk assessment Web-based user interface for cytotechnologists

29. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening29/31 The PCCSM model Web-based interface CoPath system Processed CoPath Data PCCSM: Risk assessment tool at Magee-Womens Hospital

30. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening30/31 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?) Challenges

31. Bayesian Modelling for Clinical Decision Support: Cervical Cancer Screening31/31 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.