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

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.

Automated summary

Radiomics and artificial intelligence (AI) have the potential to improve the differentiation and characterization of various renal lesions, including distinguishing benign from malignant lesions, differentiating different subtypes of renal cell carcinoma, and predicting clinical outcomes such as Fuhrman grade and treatment response. By analyzing imaging data using neural networks, quantitative features related to contour, heterogeneity, and gray zone can be extracted, providing valuable information for diagnosis and prognosis. Standardization of scanner protocols is crucial for enhancing the diagnostic ability of imaging tools and improving the accuracy in differentiating between benign and clinically significant renal cancers.