Bladder Cancer

Bladder cancer research is intensely focused on improving diagnosis and predicting recurrence, particularly for non-muscle-invasive bladder cancer (NMIBC), which has high recurrence rates and associated costs. Current efforts utilize machine learning, employing deep learning architectures like LSTMs, Transformers, and convolutional neural networks (CNNs), often combined with other techniques such as autoencoders and generative adversarial networks (GANs), to analyze multi-omics data, histopathological images, and cystoscopic images for improved accuracy and efficiency in grading and risk stratification. These advancements aim to enhance diagnostic accuracy, personalize treatment strategies, and ultimately improve patient outcomes by providing more precise risk assessments and facilitating earlier interventions.