Bayesian Modelling for Clinical Decision Support when Screening for Cervical Cancer

1. 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

2. Overview of this talk • Screening for cervical cancer • Dynamic Bayesian networks • The Pittsburgh Cervical Cancer Screening Model (PCCSM) • Personalized screening for cervical cancer with PCCSM • Conclusions

3. 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. 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. HPV infection Persistent HPV infection Cervical abnormality Cancer HSIL ASC-H AGC LSIL ASCUS Cervical pre-cancer Cervical cancer

6. Screening tests for cervical cancer • Pap test (cytology): tells about changes in cervix Cervical abnormality Cancer HSIL ASC-H AGC LSIL ASCUS • 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. 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

8. 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) HPV vaccine

10. 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. Dynamic Bayesian networks (DBNs): Qualitative part BN model DBN model • BN models consist of: • random variables • static arcs In addition to BN models: - temporal arcs

12. Dynamic Bayesian networks: Unrolling the model step 2 step 0 step 1

13. Dynamic Bayesian networks (DBNs): Quantitative part

14. Dynamic Bayesian networks: Temporal evidence Pr(Cervixt (abnormal) | Evidence ) = ? Evidence =Papt=0(negative), Papt=2(abnormal), Papt=3(abnormal), ….

15. DBN: Results of reasoning The DBN model computes the probability of cervical abnormality over time given observations Pr(Cervixt | Evidence) time

17. The Magee-Womens Hospital data 696,390 Pap test results 163,396HPVtest results 72,657 data entries: biopsies and surgical procedures

18. The follow-up data patient 1 patient 2 patient 3 patient 4 time

19. The follow-up data patient 1 patient 2 patient 3 patient 4 time

20. The follow-up data • year 0: indicates the year when a patient showed up for a screening test for the first time • 241,136 patient cases

21. The Pittsburgh Cervical Cancer Screening Model (PCCSM) graphical structure Expert knowledge Clinical data Cytology data CoPath system Histology data numerical parameters HPV data

22. The Pittsburgh Cervical Cancer Screening Model: Static version 19 variables; 278,178 numerical parameters

23. The Pittsburgh Cervical Cancer Screening Model: Dynamic version Patient Data (history data and current state) Cervical Precancer and Cancer Probability over Time

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

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

26. 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

27. 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 Magee-Womens Hospital: Pathology department data management

28. PCCSM: Web-based interface for individualized risk assessment Web-based user interface for cytotechnologists

29. PCCSM: Risk assessment tool at Magee-Womens Hospital The PCCSM model Web-based interface CoPath system Processed CoPath Data

30. 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. 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. Conclusions