An interpretable contrast-enhanced CT-based cross-attention transformer model for predicting progression-free survival in muscle-invasive bladder cancer: a multicenter study.

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Abstract

Patients with muscle-invasive bladder cancer (MIBC) typically have a dismal prognosis, even after undergoing radical cystectomy (RC). To address this limitation, we constructed and evaluated a preoperative deep learning (DL) model that leverages computed tomography (CT) imaging to stratify MIBC patients according to their risk of postoperative disease progression. Data from MIBC patients undergoing RC at one of four institutions were retrospectively analyzed and allocated to training and validation cohorts. A dual-channel information fusion DL model was developed to predict the postoperative risk of progression according to preoperative CT images. Subsequently, a DL-based clinical signature was created by combining clinical features with the DL-derived score. The concordance index (C-index) and area under the curve (AUC) were used to evaluate the DL model's predictive capability, which was compared with the DL-based clinical signature, a clinical model, and a Ki67 model. In the retrospective cohort of 427 patients with MIBC, the DL score demonstrated favorable predictive performance for disease progression in the validation cohort (C-index: 0.774; AUC: 0.785), and generally showed higher predictive performance than the comparator models in the validation cohort. Using an optimal threshold, the patient cohort was categorized into two distinct subgroups, high-risk and low-risk, with differences in the cumulative incidence of progression observed between the two groups, supporting the model's ability to stratify patients according to progression risk. The utilization of preoperative CT imaging in a DL framework enabled PFS prediction and risk stratification in patients with MIBC, with potential value for individualized postoperative surveillance.

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