An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.

Optimal treatment decision-making is critical to the long-term survival rate of patients in clinical management of cancer. Traditional treatment decision-making models typically reach treatment decisions indirectly by estimating the various risks to the beneficial outcome of patients, which limits the interpretability of treatment effects and the performance of the models. We propose SurvS(survival supervision), a novel interpretable survival benefit analytics framework based on the real-coded genetic algorithm, to integrate individual treatment effects that directly impact long-term survival benefits. SurvS can not only construct binary (beneficial and nonbeneficial), but also construct ternary (beneficial, insensitive, and detrimental) decision-making models, allowing the identification of all three possible outcomes in a single model. The framework generates personalized model scores by integrating weighted clinical features and real-valued cutoff thresholds, optimized to reflect survival outcome differences. The robust performances of SurvS were demonstrated in decision-making tasks of two clinical cancer treatment scenarios: induction chemotherapy for nasopharyngeal carcinoma and adjuvant chemotherapy for rectal cancer. SurvS outperformed traditional methods and maintained robust performance in the independent validation cohort. By enabling survival benefit-supervised optimization and interpretable model construction, reported SurvS offers a powerful tool for personalized cancer treatment planning with the potential to improve treatment efficacy and reduce overtreatment. The code is open source and available at: https://github.com/odindis/SurvS .