Machine learning-based radiomics from multiparametric MRI for predicting aggressive pathology in clear cell renal...

Clear 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.

This 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.

Aggressive 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.

Radiomics 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.