[Exploration of the clinical transformation application of the artificial intelligence diagnosis system for bladder cancer based on cystoscopy].

This study aims to explore the possibility and bottleneck of clinical translation for an artificial intelligence (AI) diagnosis system for bladder cancer based on cystoscopy.We retrospectively collected videos of 101 bladder cancer patients from January to November 2023, at Sun Yat-sen Memorial Hospital, Sun Yat-sen University. Among these patients, with a median age of 63 years and 81.0% were male. The bladder cancer AI diagnosis system was utilized for diagnosis, and the accuracy of diagnoses from the videos was assessed. Additionally, a surgical evaluation scale was formulated to evaluate the quality of the videos, simulating clinical usage.The final test results showed a system sensitivity of 97.8%, a positive predictive value of 81.7%, specificity of 54.2%, and a negative predictive value of 92.3%. Furthermore, the surgical evaluation scale scores ranged from 3.96 to 4.69, indicating the feasibility of clinical application for this system.This study further quantitatively validated the accuracy of an artificial intelligence system using cystoscopy videos and assessed the potential for clinical application.

[Strategies for diagnosis and treatment of bladder cancer in precise times].

Bladder cancer is one of the most common malignant tumors of the urinary system in China, but it is still difficult to be accurate in the diagnosis and treatment. The rapid development of high-throughput sequencing technology and data analysis of biological information pushes the medicine to enter into the precise times. In the review, the recent progress of molecular subtype, assessment of immune and metastatic status, efficacy prediction of chemotherapy, immunotherapy and targeted therapy are summarized. It provides new ideas and methods for accurate diagnosis and treatment of bladder cancer.

Subpopulations of stem-like cells in side population cells from the human bladder transitional cell cancer cell line T24.

Cancer stem cells can be isolated from human tumours using specific cell surface markers. Bladder cancer cells, however, lack specific cell surface markers, making this approach impracticable. In this study an alternative method was used, involving isolation of side population cells to explore the stem cell characteristics of bladder cancer. Side population cells were isolated from the bladder transitional cell cancer cell line T24 and examined for potential stem cell characteristics related to proliferation, cell cycle distribution, self-renewal and differentiation. It was observed that T24 side population cells have stronger proliferative and colony formation abilities than non-side population cells. Side population cells were also more resistant to chemotherapy and radiotherapy, which may be due to expression of the ATP-binding cassette half-transporter, sub-family G, member 2 protein. Overall, the results suggest that side population cells from the human bladder transitional cell cancer cell line T24 harbour stem-like cells.