Alsallal Muna, Habeeb Marwah Shaker, Vaghela Krunal, Malathi H, Vashisht Alisha, Sahu Prabhat Kumar, Singh Devendra, Al-Hussainy Ali Fawzi, Aljanaby Idris A, Sameer Hayder Naji, Athab Zainab H, Adil Mohaned, Yaseen Ahmed, Farhood Bagher
Electronics and Communication Department, College of Engineering, Al-Muthanna University, Education Zone, Samawah, AL-Muthanna, Iraq.
Department of Medical Physics and Radiation Therapy, College of Engineering Technology, Sawa University, Samawah, AL-Muthanna, Iraq.
Abdom Radiol (NY). 2025 Sep 11. doi: 10.1007/s00261-025-05181-7.
Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations.
This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities.
Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment.
A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration.
ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.
胃癌（GC）仍然是全球主要的健康问题，是全球第五大常见恶性肿瘤和第四大致癌死亡原因。尽管早期检测可将早期胃癌（EGC）的5年生存率提高到90%以上，但由于临床症状不明显和诊断困难，超过80%的病例在晚期才被诊断出来。人工智能（AI），特别是机器学习（ML）和深度学习（DL），在克服这些局限性方面显示出巨大的潜力。
本系统评价旨在评估ML和DL模型在GC管理中的性能、应用和局限性，重点关注其在各种临床影像和数据模式下在检测、诊断、治疗规划和预后预测中的应用。
按照PRISMA 2020指南，在MEDLINE、Web of Science和Scopus中对2004年至2025年5月发表的研究进行了全面的文献检索。符合条件的研究使用来自内窥镜检查、计算机断层扫描（CT）、病理学或多模式来源的数据，将ML或DL算法应用于GC的诊断或预后任务。两名评审员独立进行研究选择、数据提取和偏倚风险评估。
共有59项研究符合纳入标准。DL模型，特别是卷积神经网络（CNN），在EGC检测中表现出强大的性能，报告的灵敏度高达95.3%，曲线下面积（AUC）高达0.981，常常超过专家内镜医师。基于CT的放射组学和DL模型在肿瘤分期和转移预测方面的AUC范围为0.825至0.972。基于病理学的模型报告EGC检测准确率高达100%，预测治疗反应的AUC高达0.92。结合放射组学和病理组学的跨模式方法的AUC高达0.951。主要挑战包括算法偏差、数据集多样性有限、可解释性问题以及临床整合障碍。
ML和DL模型已显示出在改善GC的早期检测、诊断准确性和个体化治疗方面的巨大潜力。为了推动临床应用，未来的研究应优先开发大型、多样的数据集，实施可解释的AI框架，并进行前瞻性临床试验。这些努力对于将AI整合到精准肿瘤学中以及应对日益增加的全球胃癌负担至关重要。
