Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Autor: Matsui Y; Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama, 700-8558, Japan. y-matsui@okayama-u.ac.jp., Ueda D; Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan., Fujita S; Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Bunkyo-Ku, Tokyo, Japan., Fushimi Y; Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan., Tsuboyama T; Department of Radiology, Kobe University Graduate School of Medicine, Chuo-Ku, Kobe, Japan., Kamagata K; Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan., Ito R; Department of Radiology, Nagoya University Graduate School of Medicine, Showa-Ku, Nagoya, Japan., Yanagawa M; Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan., Yamada A; Medical Data Science Course, Shinshu University School of Medicine, Matsumoto, Nagano, Japan., Kawamura M; Department of Radiology, Nagoya University Graduate School of Medicine, Showa-Ku, Nagoya, Japan., Nakaura T; Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan., Fujima N; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita-Ku, Sapporo, Japan., Nozaki T; Department of Radiology, Keio University School of Medicine, Shinjuku-Ku, Tokyo, Japan., Tatsugami F; Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima, Japan., Fujioka T; Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-Ku, Tokyo, Japan., Hirata K; Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-Ku, Sapporo, Japan., Naganawa S; Department of Radiology, Nagoya University Graduate School of Medicine, Showa-Ku, Nagoya, Japan.
Jazyk: angličtina
Zdroj: Japanese journal of radiology [Jpn J Radiol] 2024 Oct 02. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1007/s11604-024-01668-3
Abstrakt: Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.
(© 2024. The Author(s).)
Databáze: MEDLINE
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