Prostate CancerPSMA-LLM-cTNMNCT07707232
Large Language Model-Assisted cTNM Annotation From Chinese PSMA PET/CT Reports
An observational study of Prostate Cancer, run by First Affiliated Hospital of Wenzhou Medical University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Cancer
- What is being tested?
- Large Language Model-Assisted Report Annotation
- How many people
- 4,600 participants
- Where
- 1 siteChina
- Study dates
- 17 Jun 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Qi Lin, +86 15205771010, devillynch@126.com
Yikai Chen, +86 13857730804, chenyikai@wzhospital.cn
About this study
Official title: Large Language Model-Assisted Imaging cTNM Staging Annotation and Uncertainty Recognition for Prostate Cancer Based on 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.
Read the detailed description
This is a multicenter observational diagnostic accuracy validation study based on Chinese PSMA PET/CT report texts from patients with prostate cancer or suspected prostate cancer. The study is not designed to evaluate a drug, device, surgical procedure, or imaging intervention. PSMA PET/CT examinations will be performed as part of routine clinical care, and the study will only analyze de-identified report texts and necessary baseline information after the reports have been finalized.
The study consists of retrospective and prospective components. Retrospectively, approximately 4,000 PSMA PET/CT reports from the First Affiliated Hospital of Wenzhou Medical University will be systematically annotated to construct a research database. An internal independent validation set of 300 reports, not used for model development or prompt optimization, will be used to evaluate the performance of the large language model. The reference standard for this 300-report validation set will be established by two experienced urologists through joint annotation, with adjudication by a nuclear medicine expert when needed. External validation will be performed using 110 de-identified reports from the First Affiliated Hospital of Ningbo University and 102 de-identified reports from Liuzhou People's Hospital. In addition, after ethics approval, 100 consecutive routine PSMA PET/CT reports from the First Affiliated Hospital of Wenzhou Medical University will be prospectively included to evaluate the accuracy and operational stability of the frozen model and prompt versions.
The large language model workflow will be deployed locally or in an institutionally controlled environment. The model will be instructed to generate structured JSON outputs, including cT_report, cN_report, cM_report, cT_uncertain, cN_uncertain, cM_uncertain, evidence_T, evidence_N, and evidence_M. The target task is report-derived imaging cTNM staging annotation, not pathological TNM staging or overall AJCC stage grouping. The model output will be used only for research evaluation and methodological analysis and will not be used for clinical diagnosis, treatment decision-making, or patient notification.
A human-AI comparison will be conducted on the 300-report internal validation set. Eight human evaluators from urology and imaging-related specialties, including trainees, residents, attending physicians, and associate chief physicians, will independently annotate the reports before and after learning the annotation manual. Annotation time will be recorded for each round. The performance of human evaluators and the large language model will be compared against the expert consensus reference standard.
The main outcome will be the accuracy of the large language model in identifying cT, cN, and cM categories from Chinese PSMA PET/CT reports. Secondary outcomes will include precision, recall, F1-score, macro-F1, micro-F1, complete cTNM triplet matching rate, uncertainty recognition performance, evidence extraction quality, human-AI comparison results, annotation time, external validation performance, prospective validation performance, and error type distribution. Error analysis will focus on local tumor extent, regional versus non-regional lymph node boundaries, bone and visceral metastasis recognition, equivocal wording, treatment-related context, benign or inflammatory alternatives, and lesions not attributable to prostate cancer.
- Conditions
- Prostate Cancer
- Being tested
- Large Language Model-Assisted Report Annotation
- Keywords
- PSMA PET/CT, Large Language Model, Artificial Intelligence, cTNM Staging, Uncertainty Recognition, Natural Language Processing, Medical Imaging Report
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language Model-Assisted Report AnnotationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Large Language Model-Assisted Report Annotation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
This outcome measures the component-level accuracy of the large language model in recognizing uncertainty labels for report-derived imaging cTNM staging. The LLM-generated cT_uncertain, cN_uncertain, and cM_uncertain labels will be compared with the expert consensus reference standard. Accuracy will be calculated as the number of correctly classified uncertainty labels divided by the total number of component-level uncertainty labels across all reports. The three uncertainty components will be aggregated into one percentage value.
中文对应
Measured: 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
This outcome measures the proportion of reports for which the complete report-derived imaging cTNM triplet assigned by human evaluators and by the large language model exactly matches the expert consensus reference standard. A report will be counted as correct only when all three components, cT_report, cN_report, and cM_report, are correct. The result will be reported as the percentage of reports with complete cTNM triplet agreement. Results will be summarized separately for pre-training human annotation, post-training human annotation, and LLM batch inference.
Measured: 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
This outcome measures the mean time required to complete report-derived imaging cTNM annotation per report. For human evaluators, annotation time will be recorded during the annotation rounds and divided by the number of annotated reports. For the LLM workflow, batch inference time will be divided by the number of processed reports. Results will be reported separately for pre-training human annotation, post-training human annotation, and LLM batch inference.
Measured: During pre-training and post-training human annotation rounds and LLM batch inference, up to 18 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jul 2026
- Study start17 Jun 2026
- Primary completion30 Jun 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Wenzhou Medical UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 4,600 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Qi Lin, MD, First Affiliated Hospital of Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07707232
- Sponsor's protocol ID
- KY2026-278
- Acronym
- PSMA-LLM-cTNM
- Status last verified
- Jul 2026
- First submitted
- 30 Jun 2026
- Last updated
- 21 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant-level data will not be shared. The study data consist of de-identified Chinese PSMA PET/CT report texts and necessary baseline clinical information generated during routine clinical care. Although direct identifiers will be removed, the free-text report data may still carry a potential risk of re-identification. Therefore, individual-level raw data will not be made publicly available. Study findings will be reported in aggregate form. De-identified summary data or analysis methods may be made available upon reasonable request and with approval from the ethics committee and the institutional data governance authority, when applicable.
Study at a glance
From the registry record, last updated by the sponsor on 21 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Large Language Model-Assisted Imaging cTNM Staging Annotation and Uncertainty Recognition for Prostate Cancer Based on Chinese PSMA PET/CT Reports
- ClinicalTrials.gov ID
- NCT07707232
- Acronym / sponsor ID
- PSMA-LLM-cTNM / KY2026-278
- Conditions
- Prostate Cancer
- Keywords
- PSMA PET/CT, Large Language Model, Artificial Intelligence, cTNM Staging, Uncertainty Recognition, Natural Language Processing, Medical Imaging Report
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,600 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 17 Jun 2026 (actual)
- Primary completion
- 30 Jun 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 16 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This is a multicenter observational diagnostic accuracy validation study based on Chinese PSMA PET/CT report texts from patients with prostate cancer or suspected prostate cancer. The study is not designed to evaluate a drug, device, surgical procedure, or imaging intervention. PSMA PET/CT examinations will be performed as part of routine clinical care, and the study will only analyze de-identified report texts and necessary baseline information after the reports have been finalized.
The study consists of retrospective and prospective components. Retrospectively, approximately 4,000 PSMA PET/CT reports from the First Affiliated Hospital of Wenzhou Medical University will be systematically annotated to construct a research database. An internal independent validation set of 300 reports, not used for model development or prompt optimization, will be used to evaluate the performance of the large language model. The reference standard for this 300-report validation set will be established by two experienced urologists through joint annotation, with adjudication by a nuclear medicine expert when needed. External validation will be performed using 110 de-identified reports from the First Affiliated Hospital of Ningbo University and 102 de-identified reports from Liuzhou People's Hospital. In addition, after ethics approval, 100 consecutive routine PSMA PET/CT reports from the First Affiliated Hospital of Wenzhou Medical University will be prospectively included to evaluate the accuracy and operational stability of the frozen model and prompt versions.
The large language model workflow will be deployed locally or in an institutionally controlled environment. The model will be instructed to generate structured JSON outputs, including cT_report, cN_report, cM_report, cT_uncertain, cN_uncertain, cM_uncertain, evidence_T, evidence_N, and evidence_M. The target task is report-derived imaging cTNM staging annotation, not pathological TNM staging or overall AJCC stage grouping. The model output will be used only for research evaluation and methodological analysis and will not be used for clinical diagnosis, treatment decision-making, or patient notification.
A human-AI comparison will be conducted on the 300-report internal validation set. Eight human evaluators from urology and imaging-related specialties, including trainees, residents, attending physicians, and associate chief physicians, will independently annotate the reports before and after learning the annotation manual. Annotation time will be recorded for each round. The performance of human evaluators and the large language model will be compared against the expert consensus reference standard.
The main outcome will be the accuracy of the large language model in identifying cT, cN, and cM categories from Chinese PSMA PET/CT reports. Secondary outcomes will include precision, recall, F1-score, macro-F1, micro-F1, complete cTNM triplet matching rate, uncertainty recognition performance, evidence extraction quality, human-AI comparison results, annotation time, external validation performance, prospective validation performance, and error type distribution. Error analysis will focus on local tumor extent, regional versus non-regional lymph node boundaries, bone and visceral metastasis recognition, equivocal wording, treatment-related context, benign or inflammatory alternatives, and lesions not attributable to prostate cancer.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| PSMA PET/CT Report Text Validation Cohort | – | Other: Large Language Model-Assisted Report AnnotationPatients 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language Model-Assisted Report AnnotationA 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. | Other | PSMA PET/CT Report Text Validation Cohort |
Primary outcome
Accuracy of LLM-Assisted Imaging cTNM Staging Annotation
Time frame: After freezing the model and prompt versions, through completion of internal, external, and prospective validation, up to 18 months
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.
3 secondary outcomes
Component-Level Accuracy of LLM-Based Uncertainty Recognition
Time frame: 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
Time frame: 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
Time frame: During pre-training and post-training human annotation rounds and LLM batch inference, up to 18 months.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Jul 2026.
- Individual participant dataSharing planned: No. Individual participant-level data will not be shared. The study data consist of de-identified Chinese PSMA PET/CT report texts and necessary baseline clinical information generated during routine clinical care. Although direct identifiers will be removed, the free-text report data may still carry a potential risk of re-identification. Therefore, individual-level raw data will not be made publicly available. Study findings will be reported in aggregate form. De-identified summary data or analysis methods may be made available upon reasonable request and with approval from the ethics committee and the institutional data governance authority, when applicable.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Cancer, run by First Affiliated Hospital of Wenzhou Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 21 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Qi Lin (+86 15205771010, devillynch@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of LLM-Assisted Imaging cTNM Staging Annotation (time frame: After freezing the model and prompt versions, through completion of internal, external, and prospective validation, up to 18 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Jul 2026.