Clinical trial · Observational
Development and Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
| Neoadjuvant Therapy | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial intelligence (AI)-based predictive model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients with bladder cancer undergoing neoadjuvant therapy
- description
- Patients pathological diagnosed with bladder cancer undergoing neoadjuvant therapy.
- interventionNames
- Diagnostic Test: Artificial intelligence (AI)-based predictive model
Primary outcomes (1)
- measure
- AUC (Area Under the Receiver Operating Characteristic Curve)
- timeFrame
- For each enrolled patient, the AI model's prediction results will be generated within several days after neoadjuvant therapy. The AUC of the model will be evaluated upon study completion, an average of 3 years.
- description
- A comprehensive metric reflecting the overall discriminative ability of the AI model, which integrates the model's sensitivity and specificity across all possible threshold values. It quantifies the probability that the model will correctly rank a randomly selected therapy-sensitive patient higher than a randomly selected therapy-insensitive patient.
Secondary outcomes (2)
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available