Clinical trial Recruiting at 1 site Observational

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.

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Quick facts

Study typeObservational
SexAll
Enrollment480 (estimated)
SponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Academic / other
Drugs / interventionschemotherapy, radiation
Locations1 site (Guangzhou, Guangdong)
StatusRecruiting
StartedJanuary 1, 2024
Main results expectedDecember 31, 2026
Study endsDecember 31, 2026
Registry record updatedJuly 3, 2025
Trial IDNCT07051083 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

  • Sun Yat-sen Memorial Hospital, Sun Yat-sen University
    Guangzhou, Guangdong, China
    Recruiting

Study contacts

Similar studies

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How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.