BACKGROUND: Pathologic and genetic data suggest that epithelial ovarian cancer may consist of indolent and aggressive phenotypes. The objective of the current study was to estimate the impact of a 2-phenotype paradigm of epithelial ovarian cancer on the mortality reduction achievable using available screening technologies. METHODS: The authors modified a Markov model of ovarian cancer natural history (the 1-phenotype model) to incorporate aggressive and indolent phenotypes (the 2-phenotype model) based on histopathologic criteria. Stage distribution, incidence, and mortality were calibrated to data from the Surveillance, Epidemiology, and End Results Program of the US National Cancer Institute. For validation, a Monte Carlo microsimulation (1000,000 events) of the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) multimodality prevalence screen was performed. Mortality reduction and positive predictive value (PPV) were estimated for annual screening. RESULTS: In validation against UKCTOCS data, the model-predicted percentage of screen-detected cancers diagnosed at stage I and II was 41% compared with 47% (UKCTOCS data), and the model-predicted PPV of screening was 27% compared with 35% (UKCTOCS data). The model-estimated PPV of a strategy of annual population-based screening in the United States at ages 50 to 85 years was 14%. The mortality reduction using annual postmenopausal screening was 14.7% (1-phenotype model) and 10.9% (2-phenotype model). Mortality reduction was lower with the 2-phenotype model than with the 1-phenotype model regardless of screening frequency or test sensitivity; 68% of cancer deaths are accounted for by the aggressive phenotype. CONCLUSIONS: The current analysis suggested that reductions in ovarian cancer mortality using available screening technologies on an annual basis are likely to be modest. A model that incorporated 2 clinical phenotypes of ovarian carcinoma into its natural history predicted an even smaller potential reduction in mortality because of the more frequent diagnosis of indolent cancers at early stages.
BACKGROUND: Pathologic and genetic data suggest that epithelial ovarian cancer may consist of indolent and aggressive phenotypes. The objective of the current study was to estimate the impact of a 2-phenotype paradigm of epithelial ovarian cancer on the mortality reduction achievable using available screening technologies. METHODS: The authors modified a Markov model of ovarian cancer natural history (the 1-phenotype model) to incorporate aggressive and indolent phenotypes (the 2-phenotype model) based on histopathologic criteria. Stage distribution, incidence, and mortality were calibrated to data from the Surveillance, Epidemiology, and End Results Program of the US National Cancer Institute. For validation, a Monte Carlo microsimulation (1000,000 events) of the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) multimodality prevalence screen was performed. Mortality reduction and positive predictive value (PPV) were estimated for annual screening. RESULTS: In validation against UKCTOCS data, the model-predicted percentage of screen-detected cancers diagnosed at stage I and II was 41% compared with 47% (UKCTOCS data), and the model-predicted PPV of screening was 27% compared with 35% (UKCTOCS data). The model-estimated PPV of a strategy of annual population-based screening in the United States at ages 50 to 85 years was 14%. The mortality reduction using annual postmenopausal screening was 14.7% (1-phenotype model) and 10.9% (2-phenotype model). Mortality reduction was lower with the 2-phenotype model than with the 1-phenotype model regardless of screening frequency or test sensitivity; 68% of cancer deaths are accounted for by the aggressive phenotype. CONCLUSIONS: The current analysis suggested that reductions in ovarian cancer mortality using available screening technologies on an annual basis are likely to be modest. A model that incorporated 2 clinical phenotypes of ovarian carcinoma into its natural history predicted an even smaller potential reduction in mortality because of the more frequent diagnosis of indolent cancers at early stages.
Authors: Joellen M Schildkraut; Lauren C Peres; Traci N Bethea; Fabian Camacho; Deanna Chyn; Emily K Cloyd; Elisa V Bandera; Alicia Beeghly-Fadiel; Loren Lipworth; Charlotte E Joslin; Faith G Davis; Patricia G Moorman; Evan Myers; Heather M Ochs-Balcom; Veronica Wendy Setiawan; Malcolm C Pike; Anna H Wu; Lynn Rosenberg Journal: Cancer Causes Control Date: 2019-06-24 Impact factor: 2.506
Authors: William D Hazelton; Gary Goodman; William N Rom; Melvyn Tockman; Mark Thornquist; Suresh Moolgavkar; Joel L Weissfeld; Ziding Feng Journal: Math Biosci Date: 2012-06-15 Impact factor: 2.144
Authors: Jennifer M Watson; Photini F Rice; Samuel L Marion; Molly A Brewer; John R Davis; Jeffrey J Rodriguez; Urs Utzinger; Patricia B Hoyer; Jennifer K Barton Journal: J Biomed Opt Date: 2012-07 Impact factor: 3.170
Authors: Dirk Bauerschlag; Karen Bräutigam; Roland Moll; Jalid Sehouli; Alexander Mustea; Darius Salehin; Maryla Krajewska; John C Reed; Nicolai Maass; Garret M Hampton; Ivo Meinhold-Heerlein Journal: J Cancer Res Clin Oncol Date: 2012-10-23 Impact factor: 4.553
Authors: Maaike Buskermolen; Andrea Gini; Steffie K Naber; Esther Toes-Zoutendijk; Harry J de Koning; Iris Lansdorp-Vogelaar Journal: Med Decis Making Date: 2018-10-20 Impact factor: 2.583