Predicting postoperative prognosis in clear cell renal cell carcinoma using a multiphase CT-based deep learning model.

Abstract

Background: Some clinicopathological risk stratification systems (CRSSs) such as the leibovich score have been used to predict the postoperative prognosis of patients with clear cell renal cell carcinoma (ccRCC), but there are no reliable noninvasive preoperative indicators for predicting postoperative prognosis in clinical practice.

Purpose: To assess the value of a deep learning (DL) model based on CT images in predicting the postoperative prognosis of patients with ccRCC.

Materials and methods: A total of 382 patients with ccRCC were retrospectively enrolled andallocated to training (n = 229) or testing (n = 153) cohorts at a 6:4 ratio. The features were extracted from precontrast-phase (PCP), corticomedullary-phase (CMP) and nephrographic-phase (NP) CT images with ResNet50, and then extreme learning machines (ELMs) were used to construct classification models. The DL model and Leibovich score were compared and combined. A receiver operating characteristic (ROC) curve and integrated discrimination improvement (IDI) were used to evaluate model performance.

Results: Compared with other single-phase DL models, the three-phase CT-based DL model achieved the best performance, with an area under the curve (AUC) of 0.839. Combining the three-phase DL model and the Leibovich score (AUC = 0.823) into a nomogram (AUC = 0.888) statistically improved performance (IDI_{Nomogram vs. Three-phase} = 0.1358, IDI_{Nomogram vs. Leibovich} = 0.1393, [Formula: see text]< 0.001).

Conclusion: The CT-based DL model could be valuable for preoperatively predicting the prognosis of patients with ccRCC, and combining it with the Leibovich score can further improve its predictive performance.