A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a deep learning (DL) model based on preoperative CT for predicting post-cystectomy overall survival in patients with MIBC. Methods: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation and external validation sets. A deep learning model incorporated the convolutional block attention module (CBAM) was built for predicting overall survival using preoperative CT images. We assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. Results: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95%CI: 0.627-0.798) in the internal validation set and 0.685 (95%CI: 0.586-0.765) in external validation set, respectively. Conclusions: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.