Objective:The optimal patient subgroup that derives substantial benefit from triplet chemotherapy (FOLFOXIRI/FOLFIRINOX) as first-line treatment for metastatic colorectal cancer (mCRC), and the clinical scenarios in which its increased toxicity is justified, remain uncertain. This study employed a machine learning–based approach to develop a predictive biomarker capable of identifying patients most likely to benefit from triplet therapy.Methods:Clinical data from 136 patients in the Ankara University de novo mCRC cohort were retrospectively reviewed. 66 clinical and biochemical variables were analyzed. Consistent with the existing literature, progression-free survival (PFS) ≥ 270 days was selected as the primary outcome. Individual treatment effect (ITE) estimation was performed using the T-Learner method with separate regression models for each treatment arm (μ1 − μ0). Model performance was evaluated using leave-one-out cross-validation (LOOCV). Feature importance was assessed using SHAP analysis, after which a reduced model was constructed using only the most influential variables.Results:The model incorporating all features demonstrated the highest predictive performance, with a ROC AUC of 0.919. SHAP analysis identified the top 10 predictive variables: primary tumor localization, ferritin, CA19-9, CRP, uric acid, TSH, triglycerides, total protein, LDL, and platelet count. The reduced model built using only these 10 features achieved an AUC of 0.869 for predicting PFS ≥270 days.Conclusion:This machine learning–based model presents a promising framework for improving patient selection for triplet chemotherapy in mCRC. Prospective validation in larger cohorts will be essential to support its integration into clinical decision making.
目的：对于转移性结直肠癌（mCRC）一线治疗，哪些患者亚组能从三药联合化疗（FOLFOXIRI/FOLFIRINOX）中获得显著获益，以及在何种临床情况下其增加的毒性风险是合理的，目前仍不明确。本研究采用基于机器学习的方法，开发一种能够识别最可能从三药联合治疗中获益患者的预测性生物标志物。 方法：回顾性分析安卡拉大学新发转移性结直肠癌队列中136例患者的临床数据，共分析了66项临床及生化变量。参照现有文献，选择无进展生存期（PFS）≥270天作为主要结局指标。采用T-Learner方法进行个体治疗效果（ITE）估计，为每个治疗组（μ1 − μ0）分别建立回归模型。通过留一交叉验证（LOOCV）评估模型性能。使用SHAP分析评估特征重要性，随后仅使用最具影响力的变量构建简化模型。 结果：包含所有特征的模型显示出最高的预测性能，其ROC曲线下面积（AUC）为0.919。SHAP分析确定了前10位预测变量：原发肿瘤部位、铁蛋白、CA19-9、C反应蛋白（CRP）、尿酸、促甲状腺激素（TSH）、甘油三酯、总蛋白、低密度脂蛋白（LDL）和血小板计数。仅使用这10个特征构建的简化模型，在预测PFS≥270天方面AUC达到0.869。 结论：这一基于机器学习的模型为优化转移性结直肠癌三药联合化疗的患者选择提供了一个有前景的框架。在更大规模队列中进行前瞻性验证，对于支持其融入临床决策至关重要。
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