Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers.

IF 3.4 2区医学 Q2 ONCOLOGY BMC Cancer Pub Date : 2025-02-01 DOI:10.1186/s12885-025-13545-x

Xing-Qi Zhang, Ze-Ning Huang, Ju Wu, Chang-Yue Zheng, Xiao-Dong Liu, Ying-Qi Huang, Qi-Yue Chen, Ping Li, Jian-Wei Xie, Chao-Hui Zheng, Jian-Xian Lin, Yan-Bing Zhou, Chang-Ming Huang

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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Trial registration: Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024-05-01).

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基于氧化应激生化标志物的老年胃癌患者预后预测模型的建立与验证

背景：应用人工智能和氧化应激生化标志物预测老年胃癌（GC）患者预后的潜力尚不清楚。方法：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）。采用Cox回归建立气相色谱综合氧化应激评分（GIOSS），将生化指标与患者预后联系起来。采用随机森林（RF）、决策树（DT）和支持向量机（SVM）方法构建5年总生存期（OS）预测模型，并使用曲线下面积（AUC）和校准图进行验证。采用SHapley加性解释（SHAP）方法进行模型解释。结果：本研究共纳入1859例老年患者。结果表明，低GIOSS是不良预后的预测因子。RF是最有效的方法，训练集、内部验证集和外部验证集的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表明RF模型具有相当大的临床实用性。SHAP结果确定pN、pT、神经周围浸润、肿瘤大小和GIOSS是主要的预测特征。在最佳模型的基础上，构建了一个在线网络计算器。结论：结合GIOSS， RF模型可有效预测老年GC患者术后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，并为改善胃癌的管理提供了一条途径。试验注册：回顾性注册在ClinicalTrials.gov（试验注册号：NCT06208046，注册日期：2024-05-01）。

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来源期刊

BMC Cancer 医学-肿瘤学

CiteScore

6.00

自引率

2.60%

发文量

1204

审稿时长

6.8 months

期刊介绍： BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.