Abstract
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.
Highlights
In 2018, renal cell carcinoma (RCC) accounted for 403,300 newly diagnosed cancer cases and175,100 deaths worldwide [1]
Studies that met the following criteria were included: (a) renal tumor radiomics-based analysis; (b) articles written in English; (c) peer-reviewed publications; (d) methodology documented in replicable detail
Tabibu et al classified a TCGA (The Cancer Genome Atlas) dataset into high- and low-risk clear cell renal cell carcinoma (ccRCC) according to a risk index based on tumor and nuclei shape features on histopathology slices constructed by a convolutional neural networks (CNN)
Summary
In 2018, renal cell carcinoma (RCC) accounted for 403,300 newly diagnosed cancer cases and. Renal tumor biopsy (RTB) provides a means for tissue sampling to assist in tumor histological and subtype diagnosis for risk-stratified management [4] It shows high diagnostic accuracy for RCC, RTB is an invasive procedure and it is criticized for its inability to sample tumors at multiple sites and distinguish tumor histologic subtypes and nuclear grade [5]. Radiomics is a term that encompasses various techniques for the extraction of quantitative features from medical images to improve diagnostic, prognostic, and predictive image interpretation accuracy. It is essentially the conversion of images into metadata for subsequent mining to improve clinical decision-making algorithms [9]. Recent advancements in AI, in machine and deep learning, accelerated the application of radiomics to medical imaging as a new beacon to guide clinical decisions
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