RT Journal Article
SR Electronic
T1 Optimal timing for cancer screening and adaptive surveillance using mathematical modeling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.11.927475
DO 10.1101/2020.02.11.927475
A1 Kit Curtius
A1 Anup Dewanji
A1 William D. Hazelton
A1 Joel H. Rubenstein
A1 E. Georg Luebeck
YR 2020
UL http://biorxiv.org/content/early/2020/02/11/2020.02.11.927475.abstract
AB Cancer screening and early detection efforts 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-effective 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.Significance Our study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes. Surveillance regimes could also be improved if they were based on these models.