AI Tool to Stage Bladder Cancer and Predict Chemotherapy Response
Tests an AI tool using ultrasound, MRI and pathology images in people with bladder cancer.
Plain-language title generated from the registry record
Registry title: Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
Key facts
- Where
- Guangzhou, ChinaSee the site
Where in China
- Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityGuangzhou, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Trial code
- NCT07051083
- Record updated
- 3 July 2025
- Original description
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.Read full original descriptionShow less
Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.
Inclusion: Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients; The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found; No surgery or radiotherapy/chemotherapy has been performed; Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:; Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.); Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy; Rapid urine cytology and urine cytology FISH testing suggest malignancy.
Exclusion: Individuals unable to tolerate surgery; Patients who have undergone chemotherapy or radiation therapy; Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination; Postoperative pathology does not indicate bladder cancer; Unsuccessful preoperative ultrasound contrast examination or non-compliant patients.