CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.
Fiche publication
Date publication
novembre 2023
Journal
Japanese journal of radiology
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HOEFFEL Christine
Tous les auteurs :
Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P
Lien Pubmed
Résumé
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.
Mots clés
Abdominal cancer, Artificial intelligence, Diagnostic imaging, Machine learning, Oncology, Precision medicine
Référence
Jpn J Radiol. 2023 11 6;: