老年结直肠癌合并骨骼肌质量降低患者生存预测研究

王翠翠; 姚俊英; 李倩; 祝力; 范旻

doi:10.16689/j.cnki.cn11-9349/r.2026.01.018

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肿瘤代谢与营养电子杂志 ›› 2026, Vol. 13 ›› Issue (1) : 134-141. DOI: 10.16689/j.cnki.cn11-9349/r.2026.01.018

论著

老年结直肠癌合并骨骼肌质量降低患者生存预测研究

¹王翠翠, ¹姚俊英, ¹李倩, ²祝力, ¹范旻

作者信息 +

Survival prediction in elderly patients with colorectal cancer complicated by reduced skeletal muscle mass

¹Wang Cuicui, ¹Yao Junying, ¹Li Qian, ²Zhu Li, ¹Fan Min

Author information +

文章历史 +

摘要

目的构建并验证基于可解释性极端梯度提升(XGBoost)算法的老年结直肠癌合并骨骼肌质量降低患者生存预测模型。方法回顾性纳入2018年7月至2023年12月新疆维吾尔自治区人民医院528例确诊为骨骼肌质量降低的老年结直肠癌患者,收集一般资料、实验室及影像学数据。采用最小绝对收缩和选择算子(Lasso)回归和XGBoost算法筛选特征变量,结合SHAP值分析特征重要性,并基于多因素Cox比例风险回归模型构建列线图模型。通过一致性指数(C-index)及时间依赖受试者操作特征(ROC)曲线及其曲线下面积(AUC)分析评估模型性能。结果最终纳入的关键变量包括年龄、预后营养指数(PNI)、营养控制评分(CONUT)、癌胚抗原(CEA)、糖类抗原242(CA242)和C反应蛋白(CRP)为危险因素,骨骼肌指数(SMI)为保护因素。XGBoost模型在训练集和验证集中均表现出良好的区分度和稳健性,校准曲线拟合良好,决策曲线分析显示具有较高临床应用价值。结论基于可解释性XGBoost算法构建的生存预测模型能够有效识别影响老年结直肠癌合并骨骼肌质量降低患者总生存期(OS)和癌症特异性生存期(CSS)的关键预后因子,为临床风险分层与个体化干预提供参考。

Abstract

Objective To develop and validate a survival prediction model for elderly patients with colorectal cancer complicated by reduced skeletal muscle mass based on an interpretable Extreme Gradient Boosting (XGBoost) algorithm. Methods A total of 528 elderly patients diagnosed with colorectal cancer complicated by reduced skeletal muscle mass at the People's Hospital of Xinjiang Uygur Autonomous Region from July 2018 to December 2023 were retrospectively enrolled. General clinical characteristics, laboratory test results, and imaging data were collected. Least absolute shrinkage and selection operator (Lasso) regression and the XGBoost algorithm were used for feature selection. Feature importance was further interpreted using SHapley Additive exPlanations (SHAP) values, and a nomogram prediction model was constructed based on a multivariable Cox proportional hazards regression model. Model performance was evaluated using the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves along with the corresponding area under the curve (AUC). Results The final model incorporated age, prognostic nutritional index (PNI), controlling nutritional status (CONUT) score, carcinoembryonic antigen (CEA), carbohydrate antigen 242 (CA242), and C-reactive protein (CRP) as risk factors, while skeletal muscle index (SMI) was identified as a protective factor. The XGBoost model demonstrated good discrimination and robustness in both the training and validation cohorts. Calibration curves showed good agreement, and decision curve analysis indicated favorable clinical utility. Conclusion The interpretable XGBoost-based survival prediction model can effectively identify key prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in elderly patients with colorectal cancer complicated by reduced skeletal muscle mass, providing a reference for clinical risk stratification and individualized intervention.

导出引用

王翠翠, 姚俊英, 李倩, 祝力, 范旻. 老年结直肠癌合并骨骼肌质量降低患者生存预测研究[J]. 肿瘤代谢与营养电子杂志. 2026, 13(1): 134-141 https://doi.org/10.16689/j.cnki.cn11-9349/r.2026.01.018

Wang Cuicui, Yao Junying, Li Qian, Zhu Li, Fan Min. Survival prediction in elderly patients with colorectal cancer complicated by reduced skeletal muscle mass[J]. Electronic Journal of Metabolism and Nutrition of Cancer. 2026, 13(1): 134-141 https://doi.org/10.16689/j.cnki.cn11-9349/r.2026.01.018

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