Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis

Abstract

Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, p < 0.001), 3.08 (95%-CI 2.09–4.06, p < 0.001) and 3.57 (95%-CI 2.69–4.45, p < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, p < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

“…Ursprung et al calculated an odds ratio of 6.24 (95% CI 4.27-9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from renal cell carcinoma [196]. Mühlbauer et al, similarly, concluded that this approach seems promising in the differential diagnosis of histological subtypes, but shared data and open science must aid in improving reproducibility of future studies [197].…”

Untitled

“…The diagnostic capabilities of radiomics for differentiating renal tumor subtypes were recently assessed in two meta-analyses including 10 and 30 studies and with Log Odds Ratio of 2.56 [95%-CI 2.01, 3.11] and 3.17 [95%-CI 2.73, 3.62], respectively 12,21 .…”

Untitled

“…Scarce data is currently available about CT-based radiomics for predicting response to different treatments. Mühlbauer reported only six studies investigating quantitative imaging for predicting response to systemic therapy 21 , of which only two applied CT-based radiomics to predict response to anti-angiogenic treatment with limited data on radiomics model accuracy 37,38 . Both articles implemented a univariate radiomics Cox regression model rather than combining different radiomics features in a multivariate predictive model.…”

“…Radiomics Quality Score (RQS) has been proposed as a standardized tool to assess the scientific integrity and the clinical relevance of radiomics studies by evaluating the key challenges in radiomics analysis 42 . Over the last three years, a few systematic reviews assessing the RQS of radiomics studies in renal cancer have been published 12,21,43 showing a rather low quality of radiomics studies (RQS from 9.4% to 33.3%). Of note, the key identified deficiencies were related to standardization, independent validation, cost effectiveness and open science data sharing.…”