Research progress of machine learning applications in gastric cancer diagnosis and therapy

• Wen-Zhuo Cui ^[1] ; Cheng-Quan Wen ^[2] ; Chao-Qun Li ^[1] ; Qiu-Jie Zhang ^[3] ; Qing-Qing Yu ^[4] ; Wei-Wei Sun ^[1]
  1. [1] Jining Medical University

     Jining Medical University

     China

     
  2. [2] Department of Pharmacy, Qingdao Eighth People’s Hospital, Qingdao, Shandong, China
  3. [3] Department of Oncology, Jining NO.1 People’s Hospital, Jining, Shandong, China
  4. [4] Clinical Research Center, Jining NO.1 People’s Hospital, Jining, Shandong, China

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• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 28, Nº. 8, 2026, págs. 3005-3019
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Gastric cancer (GC), a malignant neoplasm originating from the gastric mucosal epithelium, represents one of the most prevalent cancers worldwide. Early detection is critical for improving treatment outcomes and patient prognosis. Recent advances in artificial intelligence (AI), particularly in machine learning, have introduced powerful computational and analytical capabilities that are increasingly being applied in GC research. Machine learning algorithms have shown considerable promise in enhancing the accuracy of GC diagnosis and optimizing therapeutic strategies. This review provides a concise overview of progress in machine learning applications within oncology, examines their current role and clinical utility in GC diagnosis and treatment, and highlights the transformative potential of machine learning in advancing GC management and patient care.