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Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.
Anghel, Cristian; Grasu, Mugur Cristian; Anghel, Denisa Andreea; Rusu-Munteanu, Gina-Ionela; Dumitru, Radu Lucian; Lupescu, Ioana Gabriela.
Afiliación
  • Anghel C; Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania.
  • Grasu MC; Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania.
  • Anghel DA; Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania.
  • Rusu-Munteanu GI; Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania.
  • Dumitru RL; Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania.
  • Lupescu IG; Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania.
Diagnostics (Basel) ; 14(4)2024 Feb 16.
Article en En | MEDLINE | ID: mdl-38396476
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diagnostics (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Rumanía

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diagnostics (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Rumanía
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