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Review
. 2024 Mar;42(3):246-260.
doi: 10.1007/s11604-023-01504-0. Epub 2023 Nov 6.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Maxime Barat  1   2 , Anna Pellat  2   3 , Christine Hoeffel  4 , Anthony Dohan  1   2 , Romain Coriat  2   3 , Elliot K Fishman  5 , Stéphanie Nougaret  6   7 , Linda Chu  5 , Philippe Soyer  8   9
Affiliations
Review

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Maxime Barat et al. Jpn J Radiol. 2024 Mar.

Abstract

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Keywords: Abdominal cancer; Artificial intelligence; Diagnostic imaging; Machine learning; Oncology; Precision medicine.

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