Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review / Ferro, Matteo; Crocetto, Felice; Barone, Biagio; Del Giudice, Francesco; Maggi, Martina; Lucarelli, Giuseppe; Busetto, Gian Maria; Autorino, Riccardo; Marchioni, Michele; Cantiello, Francesco; Crocerossa, Fabio; Luzzago, Stefano; Piccinelli, Mattia; Mistretta, Francesco Alessandro; Tozzi, Marco; Schips, Luigi; Falagario, Ugo Giovanni; Veccia, Alessandro; Vartolomei, Mihai Dorin; Musi, Gennaro; de Cobelli, Ottavio; Montanari, Emanuele; Tătaru, Octavian Sabin. - In: THERAPEUTIC ADVANCES IN UROLOGY. - ISSN 1756-2872. - 15:(2023), p. 17562872231164803. [10.1177/17562872231164803]

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Ferro, Matteo;Del Giudice, Francesco;Maggi, Martina;Lucarelli, Giuseppe;Busetto, Gian Maria;Tozzi, Marco;Schips, Luigi;
2023

Abstract

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
2023
artificial intelligence; imaging; machine learning; radiomics; renal cancer
01 Pubblicazione su rivista::01g Articolo di rassegna (Review)
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review / Ferro, Matteo; Crocetto, Felice; Barone, Biagio; Del Giudice, Francesco; Maggi, Martina; Lucarelli, Giuseppe; Busetto, Gian Maria; Autorino, Riccardo; Marchioni, Michele; Cantiello, Francesco; Crocerossa, Fabio; Luzzago, Stefano; Piccinelli, Mattia; Mistretta, Francesco Alessandro; Tozzi, Marco; Schips, Luigi; Falagario, Ugo Giovanni; Veccia, Alessandro; Vartolomei, Mihai Dorin; Musi, Gennaro; de Cobelli, Ottavio; Montanari, Emanuele; Tătaru, Octavian Sabin. - In: THERAPEUTIC ADVANCES IN UROLOGY. - ISSN 1756-2872. - 15:(2023), p. 17562872231164803. [10.1177/17562872231164803]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1697820
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