基于机器学习的生存分析在预测胃癌辅助化疗结局中的开发与验证：一项多中心、纵向队列研究。
Development and validation of machine learning-based survival analysis to predict outcome in gastric cancer with adjuvant chemotherapy: A multicenter, longitudinal, cohort study.
作者信息
Pan Yan, Lu Linbin, Gao Xianchun, Yu Jun, Dai Sitian, Yao Ruirong, Han Ning, Wang Xinlin, Reyila Abudurousuli, Wang Shibo, Yan Junya, Xu Zhen, Lu Yuanyuan, Li Mengbin, Li Jipeng, Liu Jiayun, Zhao Qingchuan, Wu Kaichun, Nie Yongzhan
机构信息
State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an 710032, China.
Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350028, China.
出版信息
Chin J Cancer Res. 2025 Jun 30;37(3):377-389. doi: 10.21147/j.issn.1000-9604.2025.03.07.
OBJECTIVE
The previously integrated tumor-inflammation-nutrition (HI-GC) score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer (GC). However, its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment. This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.
METHODS
This retrospective, multicenter, longitudinal cohort study, spanning nine hospitals, included 7,085 patients with GC post-radical gastrectomy. A baseline prognostic model was constructed using 117 machine-learning algorithms. The dynamic survival decision tree model (dySDT) was employed to combine the baseline model with the HI-GC score.
RESULTS
A Cox regression model incorporating six factors was used to create a nomogram [Harrell's C-index: training cohort: 0.765; 95% confidence interval (95% CI): 0.747, 0.783; validation set: 0.810; 95% CI: 0.747, 0.783], including pT stage, positive lymph node ratio, pN stage, tumor size, age, and adjuvant chemotherapy. The best-performing machine learning model exhibited similar predictive accuracy to the nomogram (C-index: 0.770). For the short-term dySDT at 1 month, the mortality hazard ratios (HRs) for groups IIa, IIb, and III were 2.61 (95% CI: 2.24, 3.04), 5.02 (95% CI: 4.15, 6.06), and 8.88 (95% CI: 7.57, 10.42), respectively, compared to group I. Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup (P<0.001). The long-term dySDT at 1 year showed HRs of 3.25 (95% CI: 2.12, 4.97) for group II, 6.73 (95% CI: 4.29, 10.56) for group IIIa, and 17.88 (95% CI: 10.71, 29.84) for group IIIb.
CONCLUSIONS
The dySDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.
目的
先前整合的肿瘤-炎症-营养（HI-GC）评分已显示出对胃癌（GC）术后患者复发及临床决策的动态监测价值。然而，其未纳入临床病理因素限制了其基线风险评估能力。本研究旨在开发一种能准确识别辅助化疗患者并动态评估复发风险的模型。
方法
这项回顾性、多中心、纵向队列研究涵盖九家医院，纳入7085例行根治性胃切除术后的GC患者。使用117种机器学习算法构建基线预后模型。采用动态生存决策树模型（dySDT）将基线模型与HI-GC评分相结合。
结果
采用包含六个因素的Cox回归模型创建列线图[Harrell's C指数：训练队列：0.765；95%置信区间（95%CI）：0.747，0.783；验证集：0.810；95%CI：0.747，0.783]，包括pT分期、阳性淋巴结比例、pN分期、肿瘤大小、年龄和辅助化疗。表现最佳的机器学习模型显示出与列线图相似的预测准确性（C指数：0.770）。对于1个月时的短期dySDT，与I组相比，IIa、IIb和III组的死亡风险比（HR）分别为2.61（95%CI：2.24，3.04）、5.02（95%CI：4.15，6.06）和8.88（95%CI：7.57，10.42）。分层分析显示各亚组中辅助化疗与总生存之间存在显著交互作用（P<0.001）。1年时的长期dySDT显示，II组的HR为3.25（95%CI：2.12，4.97），IIIa组为6.73（95%CI：4.29，10.56），IIIb组为17.88（95%CI：10.71，29.84）。
结论
dySDT有效地对死亡风险进行分层，并为胃切除术后的临床决策提供有价值的帮助。
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