Journal of Gastric Cancer

  • 제23권3호
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  • Pages.388-399
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  • 2023
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  • 2093-582X(pISSN)
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  • 2093-5641(eISSN)
대한위암학회 (The Korean Gastric Cancer Association)
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Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

  • Kyung Won Kim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jimi Huh (Department of Radiology, Ajou University School of Medicine) ;
  • Bushra Urooj (Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center) ;
  • Jeongjin Lee (School of Computer Science and Engineering, Soongsil University) ;
  • Jinseok Lee (Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University) ;
  • In-Seob Lee (Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Hyesun Park (Body Imaging Department of Radiology, Lahey Hospital and Medical Center) ;
  • Seongwon Na (Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center) ;
  • Yousun Ko (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 투고 : 2023.07.20
  • 심사 : 2023.07.28
  • 발행 : 2023.07.31
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초록

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

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참고문헌

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