This review discusses potential oncologic and nononcologic applications of CT texture
analysis (CTTA CT texture analysis), an emerging area of “radiomics” that extracts, analyzes …

Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …

Context The incidental detection of localized renal masses has been rising steadily, but a
significant proportion of these tumors are benign or indolent and, in most cases, do not …

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 …

Objectives (1) To assess the methodological quality of radiomics studies investigating
histological subtypes, therapy response, and survival in patients with renal cell carcinoma …

Simple Summary Radiomics may answer questions where the conventional interpretation of
medical imaging has limitations. The aim of our systematic review and meta-analysis was to …

Texture analysis has arisen as a tool to explore the amount of data contained in images that
cannot be explored by humans visually. Radiomics is a method that extracts a large number …

Objective CT texture analysis (CTTA) using filtration-histogram–based parameters has been
associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor …

Objectives To explore whether clear cell renal cell carcinoma (ccRCC), papillary renal cell
carcinoma (pRCC), and chromophobe renal cell carcinoma (cRCC) can be distinguished …