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ID06421142

Study of FAPI PETMRI, FDG PETMRI, and MRI for Diagnosing and Predicting Treatment Response in Muscle-Invasive Bladder Cancer

Led by First Affiliated Hospital of Fujian Medical University · Updated on 2026-06-01

80

Participants Needed

1

Research Sites

43 weeks

Total Duration

AI-Summary

What this Trial Is About

Researchers are evaluating the use of FAPI PETMRI, FDG PETMRI, and MRI imaging techniques to diagnose muscle-invasive bladder cancer MIBC and predict how well patients respond to neoadjuvant therapy. The study aims to help doctors adjust treatment plans in a timely manner to benefit patients with MIBC. This prospective trial collects imaging data alongside clinical laboratory and pathological results to compare the accuracy of these imaging methods using postoperative pathology as the standard. Participants with suspected MIBC undergo FAPI PETMRI, FDG PETMRI, and MRI scans to assess diagnosis accuracy. Patients confirmed with MIBC then receive these imaging exams both before and after neoadjuvant therapy. Following therapy, patients have either transurethral tumor resection or radical cystectomy based on treatment response and condition. The study compares imaging and clinical data to evaluate how well these methods assess therapy effectiveness and guide treatment decisions. During the study, participants will have imaging exams, clinical laboratory tests, and pathological assessments. Researchers monitor diagnostic accuracy measures such as sensitivity, specificity, and predictive values within one week after surgical pathology results. They also evaluate therapy response outcomes like remission and metabolic response within one month. Long-term outcomes including overall survival and progression-free survival are followed for one to three years. The study runs until June 2029, and all data will help improve diagnosis and treatment monitoring for MIBC patients.

CONDITIONS

Brief Title

FAPI and FDG PET/MRI in Diagnosis and Therapy Prediction of Bladder Cancer

Research Team

N

Ning Xu, Doctor

X

Xiao-Dong Li, Master

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