AI-guided ultrasound and MRI staging to guide bladder cancer treatment
Official title: 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
Run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · NCT07051083 · Data updated 2026-11-12
This project will test whether a deep-learning system using ultrasound, MRI, and pathology can more accurately stage bladder cancer and predict who will benefit from additional chemotherapy for patients with suspected bladder tumors.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 480 (estimated) |
| Sponsor | Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University Academic / other |
| Drugs / interventions | chemotherapy, radiation |
| Locations | 1 site (Guangzhou, Guangdong) |
| Status | Recruiting |
| Started | January 1, 2024 |
| Main results expected | December 31, 2026 |
| Study ends | December 31, 2026 |
| Registry record updated | July 3, 2025 |
| Trial ID | NCT07051083 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults with suspected primary or recurrent bladder cancer who are planned for surgical resection, have not received prior radiotherapy or chemotherapy, and can undergo contrast-enhanced ultrasound and MRI are ideal candidates.
✕Probably not a fit
Patients who cannot tolerate or are allergic to ultrasound contrast agents, cannot complete the required imaging, have other primary malignancies, or have already received prior treatment may not benefit from this project.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
Researchers will build a multimodal dataset linking contrast-enhanced ultrasound, magnetic resonance images, pathology slides, clinical data, and follow-up for patients with suspected bladder cancer. They will preprocess and homogenize imaging, perform tissue segmentation with U-Net and Transformer-based methods, and train convolutional neural networks with attention mechanisms and transfer learning to derive staging and prognostic models. Tumor specimens will undergo immunohistochemical fluorescence staining to add molecular information and support multimodal feature extraction. The project is observational and will enroll surgical candidates at a single center to test the models' ability to stage disease and stratify risk.
Why it matters
Potential benefit: If successful, the system could provide more accurate preoperative staging to reduce overtreatment and help clinicians choose more appropriate surgery or chemotherapy.
How similar studies have gone: Previous AI and imaging studies have shown promising results for bladder cancer staging, but the specific multimodal combination of ultrasound, MRI, pathology, and transfer-learning deep models remains largely novel and unproven.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients. 2. The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found. 3. No surgery or radiotherapy/chemotherapy has been performed. 4. Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following: 1. Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.); 2. Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy; 3. Rapid urine cytology and urine cytology FISH testing suggest malignancy. Exclusion Criteria: 1. Individuals unable to tolerate surgery; 2. Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination; 3. Unsuccessful preoperative ultrasound contrast examination or non-compliant patients; 4. Postoperative pathology does not indicate bladder cancer; 5. Patients who have undergone chemotherapy or radiation therapy.
Where it's running
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Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China Recruiting
Study contacts
- Principal investigator: Qiyun Ou, Dr. — Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Study coordinator: Qiyun Ou, Dr.
- Email: ouqy5@mail.sysu.edu.cn
- Phone: (86)020-34071020
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.