title: Modeling the Dynamics of High-Grade Serous Ovarian Cancer Progression for Transvaginal Ultrasound-Based Screening and Early Detection.
abstract: High-grade serous ovarian cancer (HGSOC) represents the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. A timely detection of low volume HGSOC should be the goal of any screening studies. However, numerous transvaginal ultrasound (TVU) detection-based population studies aimed at detecting low-volume disease have not yielded reduced mortality rates. A quantitative invalidation of TVU as an effective HGSOC screening strategy is a necessary next step. Herein, we propose a mathematical model for a quantitative explanation on the reported failure of TVU-based screening to improve HGSOC low-volume detectability and overall survival.We develop a novel in silico mathematical assessment of the efficacy of a unimodal TVU monitoring regimen as a strategy aimed at detecting low-volume HGSOC in cancer-positive cases, defined as cases for which the inception of the first malignant cell has already occurred. Our findings show that the median window of opportunity interval length for TVU monitoring and HGSOC detection is approximately 1.76 years. This does not translate into reduced mortality levels or improved detection accuracy in an in silico cohort across multiple TVU monitoring frequencies or detection sensitivities. We demonstrate that even a semiannual, unimodal TVU monitoring protocol is expected to miss detectable HGSOC. Lastly, we find that circa 50% of the simulated HGSOC growth curves never reach the baseline detectability threshold, and that on average, 5-7 infrequent, rate-limiting stochastic changes in the growth parameters are associated with reaching HGSOC detectability and mortality thresholds respectively. Focusing on a malignancy poorly studied in the mathematical oncology community, our model captures the dynamic, temporal evolution of HGSOC progression. Our mathematical model is consistent with recent case reports and prospective TVU screening population studies, and provides support to the empirical recommendation against frequent HGSOC screening.
authors.affiliation: Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, Maryland, United States of America.|Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, United States of America.
authors.forename: Dana-Adriana
authors.identifier: http://orcid.org/0000-0002-5509-8987
authors.initials: DA
authors.lastname: Botesteanu
authors.affiliation: Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, United States of America.
authors.forename: Jung-Min
authors.initials: JM
authors.lastname: Lee
authors.affiliation: Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, Maryland, United States of America.
authors.forename: Doron
authors.initials: D
authors.lastname: Levy
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D018450:Disease Progression; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D007700:Kinetics; D008875:Middle Aged; D008962:Models, Theoretical; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D015203:Reproducibility of Results; D013269:Stochastic Processes; D014463:Ultrasonography