AI-Guided TURBT for Bladder Cancer (AITURBT)
Evaluation of the Efficacy and Safety of an AI Navigation Planning System in Transurethral Resection of Bladder Tumor: A Prospective, Randomized, Controlled, Single-Center Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Lingzhi Du, MD
- Phone Number: +86-19839759960
- Email: 20301050222@fudan.edu.cn
Study Contact Backup
- Name: Jiajun Wang, MD
- Phone Number: +86-15201926887
- Email: wang.hang@zs-hospital.sh.cn
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Age between 18 and 75 years, male or female.
- Clinical or imaging diagnosis of non-muscle invasive bladder cancer (NMIBC) according to international diagnostic standards (CT, MRI, ultrasound, cystoscopy).
- Medically fit for TURBT surgery, with normal or essentially normal renal, cardiopulmonary, and hepatic function.
- Willing and able to provide written informed consent (signed by the patient or a legally authorized representative).
Exclusion Criteria:
- Pregnant or lactating women.
- Severe dysfunction of other vital organs (heart, lungs, kidneys, etc.).
- Imaging evidence of distant metastases.
- Severe infection or active inflammation.
- Psychiatric disorders or inability to comply with study procedures.
- Any other condition deemed by the investigator as inappropriate for participation.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
No Intervention: control
Participants in the control group undergo conventional Transurethral Resection of Bladder Tumor (TURBT) following standard clinical guidelines.
The surgeon determines tumor margins and resection range based solely on white-light cystoscopy findings and personal clinical experience, without the use of the AI navigation planning system.
All perioperative management, including anesthesia, postoperative care, and follow-up protocols, is identical to that of the experimental group.
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Experimental: AI-navigation
Participants in the experimental group undergo standard TURBT with the assistance of the AI navigation planning system.
During surgery, the real-time cystoscopic video is processed by the system, which automatically identifies suspicious tumor regions and dynamically displays recommended resection margins as visual overlays on the surgical monitor.
The surgeon integrates this AI-generated information with clinical judgment to determine the final resection plan, with the ultimate decision-making authority remaining with the surgeon at all times.
All perioperative management, including anesthesia, postoperative care, and follow-up protocols, is identical to that of the control group.
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The AI Navigation Planning System is a software-based medical device that processes real-time video from a standard cystoscope during TURBT.
It uses deep learning algorithms to automatically identify suspicious tumor regions and perform semantic segmentation of tumor boundaries on the endoscopic video stream.
The system overlays the identified tumor areas and dynamically recommended resection margins onto the surgical monitor as visual guidance for the surgeon.
The system achieves a diagnostic accuracy of 97% and an AUC of 0.97 in multi-center validation.
It is designed to assist surgeons in achieving more complete tumor resection by reducing reliance on subjective visual assessment alone.
The system operates locally on a standard workstation without requiring internet connectivity, ensuring data security.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Recurrence-Free Survival (RFS)
Time Frame: Time Frame: Up to 24 months post-surgery
|
Time from surgery to the first occurrence of tumor recurrence confirmed by imaging (CT/MRI/ultrasound) or cystoscopy with pathological biopsy, or death from any cause, whichever occurs first.
Participants without an event at the end of follow-up will be censored at the date of the last known follow-up visit.
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Time Frame: Up to 24 months post-surgery
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Surgical Duration
Time Frame: Day of surgery (intraoperative)
|
Time from cystoscope entry into the bladder to successful placement of the urinary catheter at the end of surgery, recorded in minutes.
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Day of surgery (intraoperative)
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Intraoperative Blood Loss
Time Frame: Day of surgery (intraoperative
|
Total blood loss during surgery, calculated by suction canister fluid minus irrigation fluid plus gauze weighing method, recorded in milliliters (mL).
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Day of surgery (intraoperative
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Perioperative Bleeding Rate
Time Frame: Up to 14 days post-surgery
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Proportion of participants experiencing bleeding requiring interventional therapy, unplanned reoperation, or blood transfusion within 14 days post-surgery.
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Up to 14 days post-surgery
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Number of Lesions Detected Intraoperatively
Time Frame: Day of surgery (intraoperative)
|
Total number of tumor lesions identified and documented during the surgical procedure.
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Day of surgery (intraoperative)
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Postoperative Residual Lesion Rate
Time Frame: Up to 3 months post-surgery
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Proportion of participants with residual or newly detected lesions at the first postoperative cystoscopy follow-up visit.
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Up to 3 months post-surgery
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Surgery-Related Complication Rate
Time Frame: Up to 30 days post-surgery
|
Incidence and severity of postoperative complications graded according to the Clavien-Dindo Classification (Grade I to V) within 30 days post-surgery.
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Up to 30 days post-surgery
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Director: Jiajun Wang, MD, Fudan University
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- ZS-AI based navigation
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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