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.
Male patients aged 18 years or older.
Patients with clinically diagnosed, pathologically diagnosed, or clinically suspected prostate cancer.
Patients who underwent PSMA PET/CT for initial staging, recurrence assessment, treatment response evaluation, metastatic assessment, or other clinical purposes during routine care.
Complete or basically complete Chinese PSMA PET/CT report text is available, including imaging findings and/or diagnostic impression.
PSMA PET/CT reports unrelated to prostate cancer, or reports clearly irrelevant to the research task.
Reports with severely missing, unreadable, or unavailable main text, imaging findings, or diagnostic impression.
Reports that cannot be adequately de-identified or contain residual direct personal identifiers that cannot be safely removed.
Duplicate records, repeated exports of the same examination, or records for which the unique report version cannot be confirmed.