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Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling.

Kit Curtius1,2, Anup Dewanji3, William D Hazelton4

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Mathematical modeling can optimize cancer screening protocols to improve early detection and reduce overdiagnosis. This approach, applied to esophageal adenocarcinoma, suggests older optimal screening ages for individuals with GERD, enhancing personalized cancer surveillance.

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Area of Science:

  • Oncology
  • Mathematical Biology
  • Health Economics

Background:

  • Current cancer screening guidelines are often based on expert opinion and epidemiological data, but lack quantitative optimization for individual patient risk.
  • Existing screening protocols may lead to overdiagnosis and underdiagnosis, highlighting the need for more precise and personalized approaches.
  • Early detection of cancer is crucial for improving patient outcomes and reducing mortality, but optimal timing remains a challenge.

Purpose of the Study:

  • To develop and apply a quantitative methodology for optimizing cancer screening and surveillance protocols using biologically based mechanistic modeling.
  • To determine optimal screening ages for esophageal adenocarcinoma in patients with symptomatic gastroesophageal reflux disease (GERD).
  • To assess the cost-effectiveness and validate the proposed screening strategy against existing guidelines.

Main Methods:

  • Utilized multiscale mathematical modeling of cancer evolution to simulate stochastic processes and predict optimal screening windows.
  • Calibrated a mechanistic model of esophageal adenocarcinoma to U.S. cancer registry data for Barrett's esophagus patients.
  • Determined optimal screening ages by maximizing the probability of detection within a critical intervention window and assessed cost-effectiveness.

Main Results:

  • The study identified optimal screening ages for esophageal adenocarcinoma in symptomatic GERD patients as 58 for men and 64 for women, which are older than current recommendations (>50 years).
  • These proposed ages fall within a cost-effective range and were validated using data that informed current screening guidelines.
  • The framework successfully captured critical aspects of cancer evolution for personalized screening design in Barrett's esophagus.

Conclusions:

  • Mathematical modeling of cancer evolution provides a powerful framework for optimizing screening and surveillance regimes.
  • Personalized screening strategies based on mechanistic models can improve early detection rates and potentially reduce overdiagnosis.
  • The proposed methodology offers a data-driven approach to refine cancer screening guidelines for better patient outcomes and healthcare resource allocation.