Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) 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 DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors 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.

Objectives: Lymphovascular invasion significantly impacts the prognosis of urothelial carcinoma of the bladder. Traditional lymphovascular invasion detection methods are time-consuming and costly. This study aims to develop a deep learning-based model to preoperatively predict lymphovascular invasion status in urothelial carcinoma of bladder using CT images. Methods: Data and CT images of 577 patients across four medical centers were retrospectively collected. The largest tumor slices from the transverse, coronal, and sagittal planes were selected and used to train CNN models (InceptionV3, DenseNet121, ResNet18, ResNet34, ResNet50, and VGG11). Deep learning features were extracted and visualized using Grad-CAM. Principal Component Analysis reduced features to 64. Using the extracted features, Decision Tree, XGBoost, and LightGBM models were trained with 5-fold cross-validation and ensembled in a stacking model. Clinical risk factors were identified through logistic regression analyses and combined with DL scores to enhance lymphovascular invasion prediction accuracy. Results: The ResNet50-based model achieved an AUC of 0.818 in the validation set and 0.708 in the testing set. The combined model showed an AUC of 0.794 in the validation set and 0.767 in the testing set, demonstrating robust performance across diverse data. Conclusion: We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder. This model offers a non-invasive, cost-effective tool to assist clinicians in personalized treatment planning. Critical relevance statement: We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder. Key Points: We developed a deep learning feature-based stacking model to predict lymphovascular invasion in urothelial carcinoma of the bladder patients using CT. Max cross sections from three dimensions of the CT image are used to train the CNN model. We made comparisons across six CNN networks, including ResNet50. Graphical

Objectives: Precise perioperative risk stratification for upper tract urothelial carcinoma (UTUC) is essential. We developed a multimodal prognostic model integrating perioperative clinical data, radiomics, and deep learning (DL) features from baseline CT urography to improve survival prediction and guide adjuvant management. Materials and methods: We retrospectively enrolled 623 patients from six institutions, divided into training, internal validation, and independent external validation sets. Four single-modal models (clinical, radiomics, 2D DL, and 2.5D DL) were developed, and an integrated combined model was constructed by fusing their prognostic scores. Performance was evaluated using the C-index, area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The combined model consistently outperformed all single-modal models across all cohorts. C-indices reached 0.758 (95% CI: 0.712–0.804), 0.725 (95% CI: 0.651–0.798), and 0.704 (95% CI: 0.631–0.777) in the training, internal validation, and external validation sets, respectively, numerically surpassing the best single-modal models. Notably, our 2.5D DL model (C-index: 0.705) demonstrated a consistent incremental improvement over the 2D DL model (C-index: 0.681) in capturing prognostic information. In external validation, the combined model achieved a 3-year AUC of 0.766. DCA indicated the comprehensive model exhibited excellent calibration and provided the highest net benefits. Conclusion: This multimodal system, featuring a robust 2.5D DL strategy, improves overall survival prediction in UTUC. It offers a valuable tool for accurate perioperative risk stratification immediately after radical nephroureterectomy, demonstrating particularly reliable value for 3-year intermediate-term clinical decision-making. Critical relevance statement: This multimodal system advances clinical radiology by fusing perioperative clinical data, radiomics, and DL features from CTU images, enhancing risk stratification accuracy to guide postoperative adjuvant management for UTUC. Key Points: Current prognostic models for UTUC lack accuracy, creating an unmet clinical need for precise perioperative risk stratification to guide adjuvant management. A multimodal prognostic model fusing clinical, radiomic, and DL features from baseline CT urography consistently outperformed single-modality models in predicting overall survival.