Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images
Cristian Anghel,
Mugur Cristian Grasu,
Denisa Andreea Anghel,
Gina-Ionela Rusu-Munteanu,
Radu Lucian Dumitru,
Ioana Gabriela Lupescu
Affiliations
Cristian Anghel
Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania
Mugur Cristian Grasu
Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania
Denisa Andreea Anghel
Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
Gina-Ionela Rusu-Munteanu
Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
Radu Lucian Dumitru
Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania
Ioana Gabriela Lupescu
Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
