An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

Abstract

BackgroundFecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history.MethodsWe present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments.ResultsIn Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations.DiscussionThe method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

Faecal occult blood loss accurately predicts future detection of colorectal cancer. A prognostic model

“…Decision-analytic modelling can help elucidate whether screening intensity should be reduced for the majority of participants and increased for those at increased risk or whether a different breakdown is better. 28 A favourable harms–benefits ratio also needs to be further demonstrated through clinical trials. Meanwhile, prior modelling studies 8 and decision curve analysis ( online supplemental figure 3 ) support the potential clinical utility of risk-stratified screening based on these predictions, particularly for relatively ‘sensitive’ strategies.…”

“…As a result, adopting an average-risk threshold for earlier rescreening or colonoscopy could earlier identify >60% of participants with AN or CRC, while burdening relatively few participants overall. Decision-analytic modelling can help elucidate whether screening intensity should be reduced for the majority of participants and increased for those at increased risk or whether a different breakdown is better 28. A favourable harms–benefits ratio also needs to be further demonstrated through clinical trials.…”