Development and Validation of a Histology-Specific Natural History Model of Ovarian Cancer.

BACKGROUND Ovarian cancer is the second leading cause of death from gynecologic cancers, yet no effective screening program exists for the general population. Past screening trials evaluated the effectiveness of annual ovarian cancer screening and concluded that it does not yield significant mortality reduction. Future investments on ovarian cancer screening trials would require convincing preliminary evidence on the effectiveness of interventions of interest. Simulation modeling offers an effective, fast, cost-efficient, and safe approach to gain insights on the effectiveness of interventions, that is increasingly being used to inform guidelines for cancer screening programs. Models that simulate the natural progression of diseases in the absence of any intervention, commonly referred to as natural history models (NHMs), are the cornerstone of such analyses because they provide a reference point for evaluating interventions. Currently, no histology-specific NHM exists for ovarian cancer despite significant differences among subtypes. OBJECTIVE Develop and validate a histology-specific ovarian cancer NHM. STUDY DESIGN We developed NHMs for the most common histological subtypes of epithelial ovarian cancer: high-grade serous carcinoma, low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, endometrioid carcinoma, carcinosarcoma, and not otherwise specified. Each NHM simulates the natural progression of ovarian cancer from disease's onset until death from any cause. We modeled ovarian cancer progression as a state-transition model comprising of 13 mutually exclusive and collectively exhaustive health states. We informed the model input parameters using observed, nationally representative estimates, whenever possible. Unobserved parameters (e.g., preclinical transitions) were estimated through calibration to histology-specific data from the Surveillance, Epidemiology, and End Results (SEER) registry. We validated the NHMs on the control arms of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the United Kingdom Collaborative Trial on Ovarian Cancer Screening (UKCTOCS) trials, in terms of ovarian cancer incidence and mortality rates, and stage distribution at diagnosis. Differences between observed and estimated outcomes were assessed using traditional statistical tests. RESULTS The calibrated NHMs reproduced the observed SEER data (range of weighted root mean square error (RMSE) across histological subtypes: 0.0081 to 0.0185) as well as individual calibration targets; survival after diagnosis, stage distribution at diagnosis, and age distribution at diagnosis (ranges of RMSE across histological subtypes: 0.0029 to 0.0204, 0.0005 to 0.0203, and 0.0637 to 0.0816, respectively). The NHMs reproduced PLCO's observed incidence and mortality rates, and stage at diagnosis (p-value=0.411 for incidence, p-value=0.195 for mortality, and p-value=0.200 for stage distribution at diagnosis) and UKCTOCS's observed ovarian cancer incidence (p-value=0.607) and mortality (p-value = 0.624) rates. The average duration of the preclinical phase ranges between 1-3 years, which partly explains screening's failure to yield mortality reduction. Moreover, across all subtypes considered stage II ovarian cancer is a transient state with significantly shorter average duration as compared to other stages. CONCLUSION The NHMs accurately describe the histology-specific natural progression of ovarian cancer and provide important insights into the natural history of the disease. The developed models may be used to evaluate the impact of future and emerging ovarian cancer interventions, thus providing valuable insights to decision-makers and policy-makers.