bioRxiv preprint doi: https://doi.org/10.1101/2020.02.11.927475
Kit Curtius, Anup Dewanji, William D. Hazelton, Joel H. Rubensteind, E. Georg Luebeck
Cancer screening and early detection e orts have been partially successful in reducing incidence and mortality but many improvements are needed. Although current medical practice is mostly informed by epidemiological studies, the decisions for guidelines are ultimately made ad hoc. We propose that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and to optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain \window of opportunity" for intervention when early cancer development may be observable. Alternative to a strictly empirical approach, or microsimulations of a multitude of possible scenarios, biologically-based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barrett's esophagus (BE), we applied our methods for a model of esophageal adenocarcinoma (EAC) that was previously calibrated to US cancer registry data. We found optimal screening ages for patients with symptomatic gastroesophageal reflux disease to be older (58 for men, 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-e ective range to start screening and were independently validated by data used in current guidelines. Our framework captures critical aspects of cancer evolution within BE patients for a more personalized screening design.