Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

• Published in: Cancers Vol. 12; no. 6; p. 1387
• Main Authors: Suarez-Ibarrola, Rodrigo, Basulto-Martinez, Mario, Heinze, Alexander, Gratzke, Christian, Miernik, Arkadiusz
• Format: Journal Article
• Language: English
• Published: MDPI 28-05-2020
• Subjects: Review
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers12061387

Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.