Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 2:46 AM
Ignite Modification Date:
2025-12-25 @ 2:46 AM
NCT ID:
NCT05096533
Brief Summary:
This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
Detailed Description:
Preliminary research: This research is multi-disciplinary joint research by combining artificial intelligence with magnetic resonance, it can make the preoperative determination of bladder cancer stage more accurate and guides the clinician worker's treatment plan. At present, It has been constructed that an artificial intelligence model based on preoperative magnetic resonance images to predict staging and patient prognosis. We built a staging prediction model through deep learning artificial intelligence network, and collected magnetic resonance image data and related postoperative pathological data of patients, afterwards, We followed 576 patients on the basis of staging model construction. By obtaining OS, PFS, and RFS of patients, a part was randomly selected as a training set for training the deep learning network model. The other part is used as a test set to verify its accuracy. This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
Study:
NCT05096533
Study Brief:
Protocol Section:
NCT05096533