Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling

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.