Abstract
Objectives
To synthesise evidence on quantitative MRI biomarkers for predicting World Health Organisation/International Society of Urological Pathology (WHO/ISUP) grade in renal cell carcinoma (RCC).Materials and methods
We systematically searched PubMed, Embase, Scopus, and Web of Science from inception to August 2025 for patients with histopathologically proven RCC who underwent preoperative MRI. Eligible studies evaluated quantitative MRI biomarkers (diffusion, relaxometry, chemical exchange saturation transfer, radiomics) against WHO/ISUP grade. For diffusion-weighted imaging and radiomics/deep learning, we performed random-effects diagnostic meta-analyses and pooled mean differences in apparent diffusion coefficient (ADC) between low- and high-grade tumours.Results
Twenty studies were included; quantitative meta-analysis was feasible for 12 (seven ADC studies and five MRI-inclusive radiomics/deep learning studies). Seven diffusion-weighted MRI studies evaluating apparent diffusion coefficient-based grading of RCC yielded a pooled sensitivity of 0.84 (95% confidence interval [CI] 0.77-0.89) and specificity of 0.57 (95% CI 0.51-0.63) for identifying high-grade disease; the summary area under the curve (AUC) was 0.71. Low-grade tumours showed significantly higher apparent diffusion coefficient values than high-grade tumours (mean difference 0.21 × 10⁻³ mm²/s; 95% CI 0.11-0.30 × 10⁻³ mm²/s). Across five MRI-inclusive radiomics/deep learning studies, pooled sensitivity was 0.79 (95% CI 0.64-0.89) and specificity was 0.86 (95% CI 0.74-0.93), with an AUC of 0.90.Conclusion
Quantitative MRI, particularly diffusion-derived metrics, shows modest accuracy for identifying WHO/ISUP grade in RCC. MRI-inclusive radiomics/deep learning models achieve higher diagnostic performance, albeit with less consistency across studies. Standardised multiparametric protocols, external validation, and decision-impact studies are required before clinical implementation.Key points
Question Can quantitative MRI biomarkers, particularly diffusion metrics, non-invasively identify World Health Organisation/International Society of Urological Pathology grade in renal cell carcinoma? Findings Across 20 studies, diffusion showed modest grading performance, whereas MRI-inclusive radiomics/deep learning models achieved higher pooled diagnostic accuracy but remained heterogeneous. Clinical relevance Quantitative magnetic resonance imaging supports non-invasive renal cell carcinoma grading and biopsy triage; externally validated radiomics/deep learning models may improve preoperative risk stratification.