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

Julia Mühlbauer, Luisa Egen, Karl‐Friedrich Kowalewski, Maurizio Grilli, Margarete Teresa Walach, Niklas Westhoff, Philipp Nuhn, Fabian Christopher Laqua, Bettina Baeßler, Maximilian C. Kriegmair

2021Cancers89 citationsDOIOpen Access PDF

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.

Topics & Concepts

MedicineRadiomicsRenal cell carcinomaMeta-analysisOncocytomaInternal medicineOdds ratioAngiomyolipomaClear cellOncologyUrologyRadiologyKidneyRenal cell carcinoma treatmentRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis
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