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Review
. 2025 Jun 11.
doi: 10.1007/s00261-025-05056-x. Online ahead of print.

Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis

Mohsen Salimi  1 , Bardia Hajikarimloo  2 , Pouria Vadipour  3 , Ali Abdolizadeh  4 , Farzad Fayedeh  5 , Sharareh Seifi  6
Affiliations
Review

Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis

Mohsen Salimi et al. Abdom Radiol (NY). .

Abstract

Background and purpose: Renal cell carcinoma (RCC) represents the most prevalent malignant neoplasm of the kidney, with a rising global incidence. Tumor nuclear grade is a crucial prognostic factor, guiding treatment decisions, but current histopathological grading via biopsy is invasive and prone to sampling errors. This study aims to assess the diagnostic performance and quality of CT-based radiomics for preoperatively predicting RCC nuclear grade.

Materials and methods: A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science to identify relevant studies up until 19 April 2025. Quality was assessed using the QUADAS-2 and METRICS tools. A bivariate random-effects meta-analysis was performed to evaluate model performance, including sensitivity, specificity, and Area Under the Curve (AUC). Results from separate validation cohorts were pooled, and clinical and combined models were analyzed separately in distinct analyses.

Results: A total of 26 studies comprising 1993 individuals in 10 external and 16 internal validation cohorts were included. Meta-analysis of radiomics models showed pooled AUC of 0.88, sensitivity of 0.78, and specificity of 0.82. Clinical and combined (clinical-radiomics) models showed AUCs of 0.73 and 0.86, respectively. QUADAS-2 revealed significant risk of bias in the Index Test and Flow and Timing domains. METRICS scores ranged from 49.7 to 88.4%, with an average of 66.65%, indicating overall good quality, though gaps in some aspects of study methodologies were identified.

Conclusion: This study suggests that radiomics models show great potential and diagnostic accuracy for non-invasive preoperative nuclear grading of RCC. However, challenges related to generalizability and clinical applicability remain, as further research with standardized methodologies, external validation, and larger cohorts is needed to enhance their reliability and integration into routine clinical practice.

Keywords: Artificial intelligence; Computed tomography; Machine learning; Nuclear grade; Radiomics; Renal cell carcinoma.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare that there is no conflict of interest. Ethical Approval: Since this study is a review and does not involve human or animal subjects, ethical approval and Institutional Review Board (IRB) approval are not required, as per our institution’s guidelines. Informed Consent: Not applicable.

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