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

NCT07707232 | Recruiting

• 1 other identifier: KY2026-278
• Study Type: observational
• Target: 4,600
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Prostate Cancer

Keywords

PSMA PET/CT; Large Language Model; Artificial Intelligence; cTNM Staging; Uncertainty Recognition; Natural Language Processing; Medical Imaging Report

Outcome Measures

Primary Outcomes (1)

• Accuracy of LLM-Assisted Imaging cTNM Staging Annotation

  The primary outcome is the accuracy of the large language model in identifying report-derived imaging cT, cN, and cM categories from de-identified Chinese PSMA PET/CT report texts. The LLM-generated cT\_report, cN\_report, and cM\_report will be compared with the expert consensus reference standard. Accuracy, precision, recall, F1-score, macro-F1, micro-F1, complete cTNM triplet matching rate, and confusion matrices will be calculated in the internal 300-report validation set, external validation sets, and prospective 100-report validation set.

  After freezing the model and prompt versions, through completion of internal, external, and prospective validation, up to 18 months

Secondary Outcomes (3)

• Component-Level Accuracy of LLM-Based Uncertainty Recognition

  After freezing the model and prompt versions, through completion of all validation analyses, up to 18 months.

• Complete cTNM Triplet Matching Rate for Human Evaluators and the LLM

  During pre-training and post-training human annotation rounds and LLM batch inference, up to 18 months.

• Annotation Time per Report for Human Evaluators and the LLM

  During pre-training and post-training human annotation rounds and LLM batch inference, up to 18 months.

Study Arms (1)

PSMA PET/CT Report Text Validation Cohort

Patients with prostate cancer or suspected prostate cancer who underwent PSMA PET/CT as part of routine clinical care. De-identified Chinese PSMA PET/CT report texts and necessary baseline information will be used for manual annotation, large language model-assisted imaging cTNM staging annotation, uncertainty recognition, internal validation, external validation, prospective validation, and human-AI comparison. No additional examination, treatment, or follow-up will be assigned for this study.

Other: Large Language Model-Assisted Report Annotation

Interventions

Large Language Model-Assisted Report Annotation OTHER

A locally or institutionally controlled large language model workflow will analyze de-identified Chinese PSMA PET/CT report texts and generate structured outputs for report-derived imaging cTNM staging annotation, uncertainty recognition, and supporting evidence extraction. This workflow is used only for research evaluation and methodological analysis. It will not assign any examination, treatment, medication, procedure, or follow-up to participants, and it will not guide clinical diagnosis or treatment decisions.

PSMA PET/CT Report Text Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of male patients aged 18 years or older with clinically diagnosed, pathologically diagnosed, or clinically suspected prostate cancer who underwent PSMA PET/CT as part of routine clinical care. The study will include de-identified Chinese PSMA PET/CT report texts from the First Affiliated Hospital of Wenzhou Medical University, the First Affiliated Hospital of Ningbo University, and Liuzhou People's Hospital, as well as a prospective set of 100 consecutive routine PSMA PET/CT reports from the First Affiliated Hospital of Wenzhou Medical University.

You may qualify if:

• 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.
• The report text contains information that can be used to evaluate at least one target field, such as local prostate lesion, regional lymph nodes, non-regional lymph nodes, bone metastasis, visceral metastasis, or uncertainty expressions.
• The research data can be de-identified and replaced by a study identification number before analysis.

You may not qualify if:

• 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.
• Reports judged by the research team to be of insufficient quality for manual annotation, model evaluation, or statistical analysis.

Sponsors & Collaborators

• First Affiliated Hospital of Wenzhou Medical University: lead

Study Sites (1)

The First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Central Study Contacts

Qi Lin

CONTACT

+86 15205771010; devillynch@126.com

Yikai Chen

CONTACT

+86 13857730804; chenyikai@wzhospital.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: June 30, 2026
• First Posted: July 16, 2026
• Study Start: June 17, 2026
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: July 21, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)