Fiche publication
Date publication
septembre 2022
Journal
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HOEFFEL Christine
Tous les auteurs :
Barat M, Marchese U, Pellat A, Dohan A, Coriat R, Hoeffel C, Fishman EK, Cassinotto C, Chu L, Soyer P
Lien Pubmed
Résumé
Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.
Mots clés
computer-assisted, deep learning, image processing, pancreatic neoplasms, radiomics, texture analysis
Référence
Can Assoc Radiol J. 2022 09 5;:8465371221124927
