Bladder cancer is one of the most prevalent malignancies worldwide, with a recurrence rate of up to 78%, necessitating accurate post-operative monitoring for effective patient management. Multi-sequence contrast-enhanced MRI is commonly used for recurrence detection; however, interpreting these scans remains challenging, even for experienced radiologists, due to post-surgical alterations such as scarring, swelling, and tissue remodeling. AI-assisted diagnostic tools have shown promise in improving bladder cancer recurrence prediction, yet progress in this field is hindered by the lack of dedicated multi-sequence MRI datasets for recurrence assessment study. In this work, we first introduce a curated multi-sequence, multi-modal MRI dataset specifically designed for bladder cancer recurrence prediction, establishing a valuable benchmark for future research. We then propose H-CNN-ViT, a new Hierarchical Gated Attention Multi-Branch model that enables selective weighting of features from the global (ViT) and local (CNN) paths based on contextual demands, achieving a balanced and targeted feature fusion. Our multi-branch architecture processes each modality independently, ensuring that the unique properties of each imaging channel are optimally captured and integrated. Evaluated on our dataset, H-CNN-ViT achieves an AUC of 78.6%, surpassing state-of-the-art models. Our model is publicly available at https://github.com/XLIAaron/H-CNN-ViT.

In this paper, we propose an approach to support the diagnosis of urinary tract diseases, with a focus on bladder cancer, using SHAP (SHapley Additive exPlanations)-based feature selection to enhance the transparency and effectiveness of predictive models. Six binary classification scenarios were developed to distinguish bladder cancer from other urological and oncological conditions. The algorithms XGBoost, LightGBM, and CatBoost were employed, with hyperparameter optimization performed using Optuna and class balancing with the SMOTE technique. The selection of predictive variables was guided by importance values through SHAP-based feature selection while maintaining or even improving performance metrics such as balanced accuracy, precision, and specificity. The use of explainability techniques (SHAP) for feature selection proved to be an effective approach. The proposed methodology may contribute to the development of more transparent, reliable, and efficient clinical decision support systems, optimizing screening and early diagnosis of urinary tract diseases.

Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints. In this work, we present a PINN framework applied to oncology. Here, we seek to learn time-varying interactions due to a combination therapy in a tumor microenvironment. In oncology, experimental data are often sparse and composed of a few time points of tumor volume. By embedding inductive biases derived from prior information about a dynamical system, we extend the physics-informed neural networks (PINN) and incorporate observed biological constraints as regularization agents. The modified PINN algorithm is able to steer itself to a reasonable solution and can generalize well with only a few training examples. We demonstrate the merit of our approach by learning the dynamics of treatment applied intermittently in an ordinary differential equation (ODE) model of a combination therapy. The algorithm yields a solution to the ODE and time-varying forms of some of the ODE model parameters. We demonstrate a strong convergence using metrics such as the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cystoscopic images. Our framework includes a robust classification model using EfficientNet-B0 enhanced with Convolutional Block Attention Module (CBAM), an advanced segmentation model based on ResNet34-UNet++ architecture with self-attention mechanisms and attention gating, and molecular subtyping using ConvNeXt-Tiny to classify molecular markers such as HER-2 and Ki-67. Additionally, we introduce a Gradio-based online diagnostic platform integrating all developed models, providing intuitive features including multi-format image uploads, bilingual interfaces, and dynamic threshold adjustments. Extensive experimentation demonstrates the effectiveness of our methods, achieving outstanding accuracy (93.28%), F1-score (82.05%), and AUC (96.41%) for classification tasks, and exceptional segmentation performance indicated by a Dice coefficient of 0.9091. The online platform significantly improved the accuracy, efficiency, and accessibility of clinical bladder cancer diagnostics, enabling practical and user-friendly deployment. The code is publicly available. Our multi-task framework and integrated online tool collectively advance the field of intelligent bladder cancer diagnosis by improving clinical reliability, supporting early tumor detection, and enabling real-time diagnostic feedback. These contributions mark a significant step toward AI-assisted decision-making in urology.

After Hall of Fame athlete Deion Sanders' announcement that he battled bladder cancer, doctors are sharing warning signs to monitor. Sanders, who is currently head football coach at the University of Colorado Boulder, spoke about his medical struggles during a Monday press conference held at Folsom Field in Boulder. The former NFL and MLB star, 57, appeared alongside his care team and representatives from University of Colorado Health (UC Health) and University of Colorado Anschutz Medical Campus (CU Anschutz). Sanders was diagnosed with "very high-risk, non-muscle invasive bladder cancer," but is now cancer-free, according to a statement from his oncologist. It was very high-grade and invading through the bladder wall," said Dr. Janet Kukreja, urological oncology director at CU Anshutz. "I am pleased to report that the results from the surgery are that he is cured from the cancer." Head coach Deion Sanders of the University of Colorado speaks about his journey beating bladder cancer during a press conference in the Touchdown Club at Folsom Field in Boulder, Colorado, on July 28, 2025. The oncologist noted that Sanders' type of cancer has a very high rate of recurrence and progression. Treating the disease within the bladder would require a long series of treatments over a three-year period, and there would still be a 50% chance of the cancer coming back. The cancer could also have spread to the muscle, the doctor said, which happens in about half of cases. "Only about 10% of people live five years, even with our current medical treatment, if it metastasizes," she said. Together with his care team, Sanders made the decision to have a bladder removal, in which surgeons performed a "full robot-assisted laparoscopic bladder removal" and created a new bladder. "It is a new way of life.

Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence rates soaring as high as 70-80%. Each recurrence triggers a cascade of invasive procedures, lifelong surveillance, and escalating healthcare costs - affecting 460,000 individuals worldwide. However, existing clinical prediction tools remain fundamentally flawed, often overestimating recurrence risk and failing to provide personalized insights for patient management. In this work, we propose an interpretable deep learning framework that integrates vector embeddings and attention mechanisms to improve NMIBC recurrence prediction performance. We incorporate vector embeddings for categorical variables such as smoking status and intravesical treatments, allowing the model to capture complex relationships between patient attributes and recurrence risk. These embeddings provide a richer representation of the data, enabling improved feature interactions and enhancing prediction performance. Our approach not only enhances performance but also provides clinicians with patient-specific insights by highlighting the most influential features contributing to recurrence risk for each patient. Our model achieves accuracy of 70% with tabular data, outperforming conventional statistical methods while providing clinician-friendly patient-level explanations through feature attention. Unlike previous studies, our approach identifies new important factors influencing recurrence, such as surgical duration and hospital stay, which had not been considered in existing NMIBC prediction models.

Bladder cancer, the leading urinary tract cancer, is responsible for 15 deaths daily in the UK. This cancer predominantly manifests as non-muscle-invasive bladder cancer (NMIBC), characterised by tumours not yet penetrating the muscle layer of the bladder wall. NMIBC is plagued by a very high recurrence rate of 70-80% and hence the costliest treatments. Current tools for predicting recurrence use scoring systems that overestimate risk and have poor accuracy. Inaccurate and delayed prediction of recurrence significantly elevates the likelihood of mortality. Accurate prediction of recurrence is hence vital for cost-effective management and treatment planning. This is where Machine learning (ML) techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data. This review provides a comprehensive analysis of ML approaches for predicting NMIBC recurrence. Our systematic evaluation demonstrates the potential of diverse ML algorithms and markers, including radiomic, clinical, histopathological, genomic, and biochemical data in enhancing recurrence prediction and personalised patient management. We summarise various prediction tasks, data modalities, and ML models used, highlighting their performance, limitations, and future directions of incorporating cost-effectiveness. Challenges related to generalisability and interpretability of artificial intelligent models are discussed, emphasising the need for collaborative efforts and robust datasets.

Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors' expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and treated as urinary infections. To address this, we suggest a deep learning approach for bladder cancer detection and segmentation which combines CNNs with a lightweight positional-encoding-free transformer and dual attention gates that fuse self and spatial attention for feature enhancement. The architecture suggested in this paper is efficient making it suitable for medical scenarios that require real time inference. Experiments have proven that this model addresses the critical need for a balance between computational efficiency and diagnostic accuracy in cystoscopic imaging as despite its small size it rivals large models in performance.

Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious prototypical patient. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to all patients alike. The clinical outcomes associated with BCG treatment vary significantly among patients due to the biological and clinical complexity of the interaction between the immune system, treatments, and cancer cells. In this study, we take advantage of the patient's socio-demographics to offer a personalized mathematical model that describes the clinical dynamics associated with BCG-based treatment. To this end, we adopt a well-established BCG treatment model and integrate a machine learning component to temporally adjust and reconfigure key parameters within the model thus promoting its personalization. Using real clinical data, we show that our personalized model favorably compares with the original one in predicting the number of cancer cells at the end of the treatment, with 14.8% improvement, on average.

We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work.