Three-Dimensional (3D) deep learning model complements existing models for preoperative disease-free survival prediction (DFS) in localized clear cell renal cell carcinoma (ccRCC): A multicenter retrospective cohort study

Background: Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is therefore crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. This purpose of this study was to develop and externally validate a CT-based deep learning (DL) model for pre-surgical disease-free survival (DFS) prediction. Methods: Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), clinical model we built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. Results: 707 patients with localized ccRCC were finally enrolled for models’ training and validating. The DLCR we established can perfectly stratify patients into low-, intermediate- and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689-0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients in the UISS/Leibovich score/Rad-Score stratified low-risk but DLCR-defined intermediate- and high-risk groups were significantly more likely to experience ccRCC recurrences than those of intermediate- and high-risk in DLCR determined low-risk (all Log-rank P values＜0.05). Conclusions: Our deep learning model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.