Exploration of Prognostic Prediction Models for Renal Cell Carcinoma using Diffusion Relaxation Correlation Spectroscopic Imaging

Background The prognosis of renal cell carcinoma (RCC) varies greatly, and a highly efficient prognostic strategy is crucial for treatment selection. This study aim to evaluate the feasibility of predictive models based on diffusion relaxation correlation spectroscopic imaging (DR-CSI) in distinguishing RCC patients with different clinical outcomes. Methods One hundred and twenty-seven RCC patients who underwent DR-CSI were enrolled, including 48 patients as cohort 1 for development and 79 postoperative follow-up patients as cohort 2 for validation. DR-CSI results were analyzed using spectral equipartition method combined with various feature selection methods and classifiers, from 2*2 to 9*9. The Kruskal‒Wallis (KW), analysis of variance (ANOVA), recursive feature elimination (RFE), and Relief methods were used for subregion selection. The classifiers including Gaussian process (GP), support vector machine (SVM), linear discriminant analysis (LDA), and logistic regression via Lasso (LR Lasso). Clinicopathological and conventional MR parameters were obtained. Diagnostic performance was evaluated using AUC and compared with DeLong’s test. Kaplan‒Meier method and multivariable analysis were used for evaluating the performance of prediction model. Results DR-CSI-based equipartition models demonstrated excellent interobserver agreement (ICC: 0.86–0.99). The equipartition method (6*6) combined with KW feature selection and GP classifier achieved the highest diagnostic performance in distinguishing patients with metastatic RCC from those without metastatic RCC, with an AUC of 0.87, significantly outperforming clinicopathological and conventional MR parameters (vs. age, P < 0.001; vs. tumor diameter, P = 0.002; vs. WHO/ISUP grade, P < 0.001; vs. ADC, P = 0.001; vs. T2 value, P = < 0.001). The 6*6 model could effectively predict the recurrence of patients in cohort 2 (P = 0.005), whereas the other models could not. Additionally, the 6*6 model might serve as an independent predictive factor for recurrence in RCC. Conclusions DR-CSI-based 6*6 model combined with KW and GP may assess the aggressiveness of RCC and have great promise in predicting prognostic risk stratification.