Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.
Authors
Affiliations (15)
Affiliations (15)
- 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.
- College of Dentistry, Alnoor University, Mosul, Iraq.
- Marwadi University Research Center, Department of Computer Engineering, Faculty of Engineering & Technology, Marwadi University, Rajkot, Gujarat, India.
- Department of Biotechnology and Genetics, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
- Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
- Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
- Department of Computer science & Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, 248007, India.
- College of Pharmacy, Ahl Al Bayt University, Kerbala, Iraq.
- Almamon University College, Baghdad, Iraq.
- Collage of Pharmacy, National University of Science and Technology, 64001, Dhi Qar, Iraq.
- Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.
- Pharmacy college, Al-Farahidi University, Baghdad, Iraq.
- Gilgamesh Ahliya University, Baghdad, Iraq.
- Department of Medical Physics and Radiology, Faculty of Paramedical Sciences, Kashan University of Medical Sciences, Kashan, Islamic Republic of Iran. [email protected].
Abstract
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.