Norouzkhani Narges, Mobaraki Hesam, Varmazyar Shirin, Zaboli Hadis, Mohamadi Zhina, Nikeghbali Golnaz, Bagheri Kamyar, Marivany Newsha, Najafi Mirmehdi, Nozad Varjovi Mahdiyeh, Abouzeid Mohamed, Zeidi Baghrabad Hanieh, Eini Pooya, Azhdarimoghaddam Aida, Khosravi Farbod, Asadi Anar Mahsa
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Faculty of Medicine, İstanbul Yeniyuzyil University, Istanbul, Turkey.
BMC Gastroenterol. 2025 Apr 29;25(1):310. doi: 10.1186/s12876-025-03884-1.
Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features.
This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data.
A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86-0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52).
AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.
人工智能（AI）网络通过分析基因突变谱，在预测胃肠道癌症免疫治疗结果方面具有巨大潜力。其在预后方面的应用仍有待深入探索。本系统评价和荟萃分析旨在评估基于AI的模型（即利用人工智能分析数据并进行预测的系统）在利用基因突变特征预测胃肠道癌症免疫治疗反应方面的有效性。
本研究遵循PRISMA指南，旨在评估利用基因突变特征预测胃肠道癌症免疫治疗预后的AI网络。通过在PubMed、WOS和Scopus中检索确定相关研究。进行数据提取和质量评估，统计分析包括敏感性、特异性、准确性和AUC的合并估计值。回归模型和插补方法处理缺失值，确保结果准确可靠。使用STATA 18版分析数据。
共纳入45项均发表于2024年的研究，训练集涉及14047名参与者，测试集涉及10885名参与者。基于基因突变特征的AI模型对胃肠道癌症的性能合并结果为：AUC = 0.86（95%CI：0.86 - 0.87），敏感性 = 83%（95%CI：83% - 84%），特异性 = 72%（95%CI：72% - 73%），准确性 = 82%（95%CI：82% - 83%）。异质性为低到中度，未检测到发表偏倚。亚组分析显示，胃癌模型的AUC较高（AUC：0.87），胰腺癌模型的AUC较低（AUC：0.52）。
AI网络在基于基因突变特征预测胃肠道癌症免疫治疗结果方面显示出有前景的潜力。本系统评价突出了它们在对患者进行分层和优化治疗决策方面的有效性。然而，需要进一步的大规模研究来验证AI模型，并将其整合到临床实践中，以提高癌症免疫治疗的精度。
