Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.

Abstract

Objective: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with clinical characteristics.

Methods: Multiparametric abdominal MRI was performed on 47 pathologically confirmed RCC patients between 2019 and 2023. Preoperative MRI was performed on all patients to assess their clinical characteristics. The CNN model was developed and validated to assess the predictive value of b value images, combined b value images, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and their parametric maps for RCC aggressiveness. The least absolute shrinkage and selection operator (LASSO) regression was used to identify clinical features highly correlated with RCC aggressiveness. These clinical features were combined with selected b values to develop a fusion model. All models were evaluated using receiver operating characteristic (ROC) curve analysis.

Results: A total of 47 patients (mean age, 56.17 ± 1.70 years; 37 men, 10 women) were evaluated. LASSO regression identified renal sinus/perirenal fat invasion, tumor stage, and tumor size as the most significant clinical features. The combined b values of b = 0,1000 achieved an area under the curve (AUC) of 0.642 (95% CI: 0.623-0.661), and b = 0,100,1000 achieved an AUC of 0.657 (95% CI: 0.647-0.667). The fusion model combining clinical features with b = 0,1000 yielded the highest performance with an AUC of 0.861 (95% CI: 0.667-0.992), demonstrating superior predictive accuracy compared to the other models.

Conclusion: Deep learning using a CNN fusion model, integrating multiple b value images and clinical features, could effectively promote the preoperative prediction of tumor aggressiveness in RCC patients.