AIMC Topic: Urinary Bladder Neoplasms

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Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.

Annals of surgical oncology
BACKGROUND: Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment....

Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.

Journal of imaging informatics in medicine
Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It i...

Artificial Intelligence-Based Assessment of Preoperative Body Composition Is Associated With Early Complications After Radical Cystectomy.

The Journal of urology
PURPOSE: We aimed to use a validated artificial intelligence (AI) algorithm to extract muscle and adipose areas from CT images before radical cystectomy (RCx) and then correlate these measures with 90-day post-RCx complications.

Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning.

Computers in biology and medicine
Bladder cancer (BC) diagnosis presents a critical challenge in biomedical research, necessitating accurate tumor classification from diverse datasets for effective treatment planning. This paper introduces a novel wrapper feature selection (FS) metho...

is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

Frontiers in immunology
BACKGROUND: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the ...