Novel genetic algorithm‐based individual treatment effect scoring model for optimizing decision‐making: Induction chemotherapy in nasopharyngeal carcinoma

Abstract Traditional decision‐making models often focus on risk prediction rather than treatment effects, potentially leading to suboptimal outcomes. This study developed an individual treatment effect (ITE) model to predict the survival benefit of induction chemotherapy (IC) in locoregionally advanced nasopharyngeal carcinoma (LANPC). This study evaluated a bi‐center cohort of 1213 patients with LANPC and devoloped a genetic algorithm‐based ITE model, classifying patients into IC‐beneficial, IC‐ambiguous, and IC‐detrimental groups. Traditional risk‐stratified models based on the least absolute shrinkage and selection operator and AutoML were established for comparison. Overall survival was the primary endpoint. Models’ efficacy was assessed using Kaplan‒Meier survival analysis. Further validation included correlation analysis, restricted cubic spline curves, and distribution pattern evaluation. In the IC‐beneficial group, IC reduced the mortality risk by 68% (adjusted p = .002) and 48% (adjusted p = .029) in the training and validation sets, respectively. Conversely, IC increased mortality risk in the IC‐detrimental group, with adjusted hazard ratios of 2.66 ( p = .031) and 2.11 ( p = .023). No significant survival difference was observed in the IC‐ambiguous group ( p = .285 and .602). For traditional model, while it stratified patients by risk of dead, it performed poorly in guiding IC decisions in the validation cohort ( p > .05 in high‐risk group). Additionally, the ITE score correlated with short‐term treatment efficacy, and exhibited a stronger association with relative hazard change. The ITE model provides an accurate tool for optimizing IC decisions in LANPC, improving survival, short‐term efficacy, and facilitating personalized treatment strategies.