ArticleBMC medical imaging2025
Machine learning-based radiomics from multiparametric MRI for predicting aggressive pathology in clear cell renal cell carcinoma.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Utility of Tumor Sphericity on CT for Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma.Journal of the Korean Society of Radiology · 2026Article
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10 authors.
Funding
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) exhibits significant biological heterogeneity, with aggressive forms demonstrating poor prognosis. Accurate preoperative discrimination between aggressive and indolent ccRCC is critical for individualized treatment but remains challenging. This study aimed to evaluate the performance of machine learning models based on multiparametric MRI radiomics for distinguishing aggressive from indolent ccRCC.
methodsThis retrospective study included 157 patients with pathologically confirmed ccRCC, comprising 114 indolent and 43 aggressive cases. Regions of interest (ROIs) were manually delineated on five MRI sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), as well as the corticomedullary, nephrographic, and excretory phases of contrast-enhanced fat-suppressed T1WI (CE-fsT1WI). Thirty-one feature combinations derived from the five sequences were input into 168 classification models (constructed using 8 classifiers and 21 feature selection methods). The performance of 5,208 models was compared, and the top-ranked features were analyzed.
resultsAggressive ccRCC showed significantly larger maximum tumor diameter compared with indolent tumors (8.3 [5.7-9.5] cm vs. 3.0 [2.2-4.2] cm, p < 0.05). Radiomic features derived from T2WI contributed most substantially to model performance relative to other MRI sequences, with the optimal classification model "RF + ICAP" achieving an area under the curve (AUC) of 0.960, accuracy of 86.1%, sensitivity of 86.4%, and specificity of 86.0%. Notably, the top 10 most predictive features from T2WI were predominantly shape-related features.
conclusionRadiomics features from renal T2WI demonstrated superior discriminative value compared with T1WI and contrast-enhanced T1WI in differentiating aggressive from indolent ccRCC. Through the integration of multiple classifiers and feature selection algorithms, the optimal classification model was identified, demonstrating the potential to distinguish aggressive ccRCC pathology.
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