AI model for predicting bladder cancer treatment efficacy
Development and Prospective Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · NCT06909643
This study is testing a new AI tool that helps doctors predict how well neoadjuvant treatment will work for patients with bladder cancer.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 550 (estimated) |
| Sex | All |
| Sponsor | Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University (other) |
| Drugs / interventions | chemotherapy, immunotherapy |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT06909643 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop and validate a multimodal artificial intelligence model that predicts the efficacy of neoadjuvant treatment for bladder cancer. It will enroll 130 patients who have undergone imaging examinations and have been pathologically diagnosed with bladder cancer. The study will collect clinical, genomic, and imaging data to build an AI model that integrates these multimodal data sources. The goal is to assist clinicians in making more accurate predictions regarding treatment outcomes, ultimately improving patient care.
Who should consider this trial
Good fit: Ideal candidates are bladder cancer patients who are planning to undergo neoadjuvant therapy and radical cystectomy.
Not a fit: Patients who have not undergone standard imaging examinations or have missing data may not benefit from this study.
Why it matters
Potential benefit: If successful, this model could lead to more personalized and effective treatment strategies for bladder cancer patients.
How similar studies have performed: Other studies have shown promise in using AI for treatment prediction in oncology, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection. * Planned neoadjuvant therapy and radical cystectomy. Exclusion Criteria: * Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data. * Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy). * Poor quality of imaging or pathological images.
Where this trial is running
Guangzhou, Guangdong
- Sun Yat-sen Memorial Hospital of Sun Yat-sen University — Guangzhou, Guangdong, China (RECRUITING)
Study contacts
- Study coordinator: Tianxin Lin, Ph.D
- Email: lintx@mail.sysu.edu.cn
- Phone: 13724008338
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Cancer, Neoadjuvant Therapy, Artificial Intelligence, Multimodal Fusion, Therapy Prediction, Magnetic Resonance Imaging, Digital Pathology