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Meta-Analysis
. 2023 Feb:94:9-17.
doi: 10.1016/j.clinimag.2022.11.007. Epub 2022 Nov 17.

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

Fatemeh Dehghani Firouzabadi  1 , Nikhil Gopal  2 , Fatemeh Homayounieh  1 , Pouria Yazdian Anari  1 , Xiaobai Li  3 , Mark W Ball  2 , Elizabeth C Jones  1 , Safa Samimi  1 , Evrim Turkbey  1 , Ashkan A Malayeri  4
Affiliations
Meta-Analysis

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

Fatemeh Dehghani Firouzabadi et al. Clin Imaging. 2023 Feb.

Abstract

Background: Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

Methods: From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

Results: After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

Conclusions: According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Keywords: Chromophobe RCC; Clear cell RCC; Machine learning; Oncocytoma; Papillary RCC; Renal cell carcinoma; Systematic review.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no competing interests.

Figures

Figure 1.
Figure 1.
Prisma chart for included studies
Figure 2.
Figure 2.
Summary of QUADAS-2 assessments of included studies
Figure 3;
Figure 3;
Forest plot for sensitivity(A), specificity(B), and odds ratio(C) of CT radiomics for differentiating between oncocytoma from renal cell carcinomas
Figure 4;
Figure 4;
Forest plot for sensitivity(A), specificity(B), and odds ratio(C) of CT radiomics for differentiating between oncocytoma from chromophobe renal cell carcinomas.
See this image and copyright information in PMC

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