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Review
. 2023 Apr 17:15:17562872231164803.
doi: 10.1177/17562872231164803. eCollection 2023 Jan-Dec.

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

Matteo Ferro  1   2 , Felice Crocetto  3 , Biagio Barone  3 , Francesco Del Giudice  4 , Martina Maggi  4 , Giuseppe Lucarelli  5 , Gian Maria Busetto  6 , Riccardo Autorino  7 , Michele Marchioni  8   9 , Francesco Cantiello  10 , Fabio Crocerossa  10 , Stefano Luzzago  11   2 , Mattia Piccinelli  12   11 , Francesco Alessandro Mistretta  11   13 , Marco Tozzi  11   2 , Luigi Schips  8 , Ugo Giovanni Falagario  6 , Alessandro Veccia  14 , Mihai Dorin Vartolomei  15   16 , Gennaro Musi  11   13 , Ottavio de Cobelli  11   13 , Emanuele Montanari  17 , Octavian Sabin Tătaru  18
Affiliations
Review

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

Matteo Ferro et al. Ther Adv Urol. .

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.

Keywords: artificial intelligence; imaging; machine learning; radiomics; renal cancer.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
PRISMA methodology for identification of radiomics and renal lesions studies.
Figure 2.
Figure 2.
Radiomics flow analysis.
See this image and copyright information in PMC

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