Zhang Xing-Qi, Huang Ze-Ning, Wu Ju, Zheng Chang-Yue, Liu Xiao-Dong, Huang Ying-Qi, Chen Qi-Yue, Li Ping, Xie Jian-Wei, Zheng Chao-Hui, Lin Jian-Xian, Zhou Yan-Bing, Huang Chang-Ming
Department of Gastric Surgery, Fujian Medical University Union Hospital, No.29 Xinquan Road, Fuzhou, Fujian Province, 350001, China.
Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China.
BMC Cancer. 2025 Feb 1;25(1):188. doi: 10.1186/s12885-025-13545-x.
The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear.
This retrospective multicenter study included consecutive patients with GC aged ≥ 65 years treated between January 2012 and April 2018. The patients were allocated into three cohorts (training, internal, and external validation). The GC-Integrated Oxidative Stress Score (GIOSS) was developed using Cox regression to correlate biochemical markers with patient prognosis. Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. The SHapley Additive exPlanations (SHAP) method was used for model interpretation.
This study included a total of 1,859 older patients. The results demonstrated that a low GIOSS was a predictor of poor prognosis. RF was the most efficient method, with AUCs of 0.999, 0.869, and 0.796 in the training, internal validation, and external validation sets, respectively. The DT and SVM models showed low AUC values. Calibration and decision curve analyses demonstrated the considerable clinical usefulness of the RF model. The SHAP results identified pN, pT, perineural invasion, tumor size, and GIOSS as key predictive features. An online web calculator was constructed based on the best model.
Incorporating the GIOSS, the RF model effectively predicts postoperative OS in older patients with GC and is a robust prognostic tool. Our findings emphasize the importance of oxidative stress in cancer prognosis and provide a pathway for improved management of GC.
Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024-05-01).
人工智能和氧化应激生化标志物在预测老年胃癌（GC）患者预后方面的应用潜力尚不清楚。
这项回顾性多中心研究纳入了2012年1月至2018年4月期间连续治疗的年龄≥65岁的GC患者。患者被分为三个队列（训练、内部和外部验证）。使用Cox回归开发了GC综合氧化应激评分（GIOSS），以将生化标志物与患者预后相关联。使用随机森林（RF）、决策树（DT）和支持向量机（SVM）方法构建了五年总生存期（OS）的预测模型，并使用曲线下面积（AUC）和校准图进行验证。使用SHapley加性解释（SHAP）方法进行模型解释。
本研究共纳入1859例老年患者。结果表明，低GIOSS是预后不良的预测指标。RF是最有效的方法，在训练集、内部验证集和外部验证集中的AUC分别为0.999、0.869和0.796。DT和SVM模型的AUC值较低。校准和决策曲线分析证明了RF模型具有相当大的临床实用性。SHAP结果确定pN、pT、神经周围侵犯、肿瘤大小和GIOSS为关键预测特征。基于最佳模型构建了一个在线网络计算器。
结合GIOSS，RF模型可有效预测老年GC患者的术后OS，是一种可靠的预后工具。我们的研究结果强调了氧化应激在癌症预后中的重要性，并为改善GC的管理提供了一条途径。
在ClinicalTrials.gov上进行回顾性注册（试验注册号：NCT06208046，注册日期：2024年5月1日）。
