Large Language Model-Assisted cTNM Annotation From Chinese PSMA PET/CT Reports

This observational study will develop and validate a large language model-assisted workflow for imaging cTNM staging annotation and uncertainty recognition in prostate cancer using Chinese PSMA PET/CT report texts generated during routine clinical care. The study will use de-identified report texts and necessary baseline clinical information only. No additional imaging examination, blood test, treatment, or follow-up visit will be assigned for this study. The main objective is to evaluate whether a locally or institutionally controlled large language model can identify report-derived imaging cT, cN, and cM categories, extract supporting evidence from the original report, and recognize uncertainty expressions. Model performance will be assessed using an internal independent validation set, external validation reports from two collaborating hospitals, and a prospective validation set of 100 consecutive routine PSMA PET/CT reports. A human-AI comparison will also be performed using physicians from urology and imaging-related specialties with different seniority levels.

NCT Number	NCT07707232
Lead Sponsor	First Affiliated Hospital of Wenzhou Medical University
Conditions	Prostate Cancer
Enrollment	4,600 participants
Start Date	2026-06-17
Primary Completion	2027-06-30 (estimated)
Study Completion	2027-12-31 (estimated)
Updated on ClinicalTrials.gov	2026-07-21