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Radiomics and Artificial Intelligence in Renal Lesion Assessment.
Cellina, Michaela; Irmici, Giovanni; Pepa, Gianmarco Della; Ce, Maurizio; Chiarpenello, Vittoria; Alì, Marco; Papa, Sergio; Carrafiello, Gianpaolo.
Affiliation
  • Cellina M; Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy.
  • Irmici G; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy.
  • Pepa GD; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy.
  • Ce M; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy.
  • Chiarpenello V; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy.
  • Alì M; Radiology Unit, CDI, Centro Diagnostico Italiano, 20147 Milan, Italy.
  • Papa S; Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy.
  • Carrafiello G; Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy.
Crit Rev Oncog ; 29(2): 65-75, 2024.
Article in En | MEDLINE | ID: mdl-38505882
ABSTRACT
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Neoplasms Limits: Humans Language: En Journal: Crit Rev Oncog Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Neoplasms Limits: Humans Language: En Journal: Crit Rev Oncog Year: 2024 Document type: Article