Renal cancer management is challenging from diagnosis to treatment and follow-up. In
cases of small renal masses and cystic lesions the differential diagnosis of benign or …

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from
malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma …

Purpose The purpose of the study was to provide a comprehensive review of recent
machine learning (ML) and deep learning (DL) applications in urological practice …

Plenty of disease types exist in world communities that can be explained by humans'
lifestyles or the economic, social, genetic, and other factors of the country of residence …

OBJECTIVES: To investigate the effect of transfer learning on computed tomography (CT)
images for the benign and malignant classification on renal tumors and to attempt to improve …

Purpose: With increasing incidence of renal mass, it is important to make a pretreatment
differentiation between benign renal mass and malignant tumor. We aimed to develop a …

Abstract Machine and deep learning are used worldwide. Machine Learning (ML) and Deep
Learning (DL) are playing an increasingly important role in the healthcare sector, particularly …

Objective To evaluate the performance of quantitative computed tomography (CT) texture
analysis using different machine learning (ML) classifiers for discriminating low and high …

Objectives To develop and validate a radiomics nomogram for preoperative differentiating
renal angiomyolipoma without visible fat (AML. wovf) from homogeneous clear cell renal cell …

Objective To develop externally validated, reproducible, and generalizable models for
distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning …