Papillary Thyroid CarcinomaRET-USNCT07042984
RET-US Study - Ultrasound-Based Prediction of RET Alterations and Lateral-Neck Metastasis in Thyroid Cancer
An observational study of Papillary Thyroid Carcinoma and Thyroid Neoplasms, run by Fujian Medical University at 1 site in China.
Trial summary
Why is this study being done? RET gene alterations occur in only 5-10 % of papillary thyroid cancers, but they can change how surgeons treat the disease. Gene testing is costly and not always performed, so many RET-positive tumours are missed. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Papillary Thyroid Carcinoma, Thyroid Neoplasms, RET Proto-Oncogene Mutation
- What is being tested?
- AI-Ultrasound RET Prediction
- How many people
- 800 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 31 Dec 2029
- 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 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Consecutive adults with ultrasound-suspected papillary thyroid carcinoma scheduled for thyroidectomy at participating centers.
Who may be able to join
- Age 18-75 years, able to provide written informed consent.
- Pre-operative ultrasound findings highly suggestive of papillary thyroid carcinoma.
- Planned thyroidectomy (any extent) at a participating institution.
- Willing to undergo rapid 11-gene next-generation sequencing (NGS) panel and allow use of ultrasound DICOM images for AI analysis.
Who cannot join
- Prior thyroid or major neck surgery.
- Known medullary thyroid carcinoma, anaplastic carcinoma, or metastatic disease outside the neck.
- Multiple endocrine neoplasia (MEN) syndromes or clinical suspicion of multi-gland disease.
- Pregnant or breastfeeding.
- Severe renal impairment (eGFR < 30 mL/min/1.73 m²) or other condition that precludes surgery or gene testing.
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
Fujian Medical University Union Hospital
Fuzhou, FJ, 350001
Bo WANG, MD PhD, +8613959123550, wangbo@fjmu.edu.cn
About this study
Official title: RET-US Cohort: Prospective Evaluation of an AI-Ultrasound Model for Detecting RET Gene Alterations and Predicting Lateral Cervical Lymph-Node Metastasis in Papillary Thyroid Carcinoma
Why is this study being done? RET gene alterations occur in only 5-10 % of papillary thyroid cancers, but they can change how surgeons treat the disease. Gene testing is costly and not always performed, so many RET-positive tumours are missed. Researchers have built a computer program (artificial-intelligence or "AI" model) that reads routine thyroid ultrasound images and predicts whether the tumour carries a RET alteration and whether the cancer has already spread to lymph-nodes in the side of the neck.
What will happen in this study?
Read more
About 800 adults who are scheduled for thyroid-cancer surgery will take part. Each participant will:
- have a standard pre-operative ultrasound exam (no extra scanning time),
- give a routine fine-needle sample for a 14-gene panel test (results in 24 h), and
- allow the AI model to analyse the ultrasound images in the background. Doctors making treatment decisions will not see the AI result. After surgery, the research team will compare the AI predictions with the gene-panel result and the final pathology report.
Main goal: To find out how accurately the AI model detects RET alterations. Secondary goals: To measure the model's ability to predict lymph-node spread, and to compare costs between ultrasound-only prediction and full gene testing.
Benefits and risks: Participants will receive the current standard of care; there is no added risk beyond the usual ultrasound and needle biopsy. The study could lead to faster, less expensive ways to identify high-risk thyroid cancers in the future.
Read the detailed description
Background RET rearrangements or point mutations drive a minority of papillary thyroid carcinomas (PTC) yet are associated with aggressive behaviour and may qualify patients for selective RET inhibitors. Because of low prevalence, RET testing is often omitted, resulting in under-recognition. Recent work shows that high-resolution ultrasound contains radiomic signatures linked to tumour genotypes. A deep-learning model (EfficientNet-B3 backbone with dual segmentation + multi-label heads) was trained on 1 000 retrospectively collected cases, including 74 RET-positive tumours augmented with GAN-based synthetic images, achieving an AUC of 0.87 for RET prediction in internal cross-validation.
Objectives Primary: validate the AI model's area under the receiver-operating characteristic curve (AUC) for RET alteration detection in a prospective cohort.
Secondary: (i) sensitivity/specificity for RET; (ii) accuracy for predicting lateral-neck (pN1b) metastasis; (iii) incremental cost per correct RET diagnosis; (iv) concordance between AI probability score and lymph-node burden.
Design Single-arm, prospective observational cohort (n = 800). Consecutive eligible patients will undergo: (1) routine pre-operative thyroid ultrasound; (2) upload of DICOM files to a cloud inference server; (3) rapid 14-gene next-generation sequencing panel on FNA or paraffin tissue (includes RET fusions KIF5B, CCDC6, NCOA4 and point mutations M918T, V804). Surgeons remain blinded to AI output. Surgical specimens provide ground truth for pN staging. Data captured in REDCap; statistical analysis uses DeLong test for AUC and McNemar test for paired accuracy.
Eligibility Adults 18-75 y with radiologically suspected PTC, planned thyroidectomy, and consent for gene testing. Exclusions: re-operative neck, medullary/anaplastic carcinoma, pregnancy, eGFR < 30 mL min-¹ 1.73 m-².
Sample Size With expected RET prevalence 6 % and target AUC ≥ 0.80 vs null 0.50, 800 cases provide 90 % power (α = 0.05).
Ethics \& Oversight IRB approved; minimal-risk diagnostic study. Ultrasound and FNA are standard-of-care; AI inference uses de-identified images. Results will be disseminated via peer-reviewed journals and conference presentations.
- Conditions
- Papillary Thyroid Carcinoma, Thyroid Neoplasms, RET Proto-Oncogene Mutation, Lymphatic Metastasis
- Being tested
- AI-Ultrasound RET Prediction
- Keywords
- Ultrasound Radiomics, Artificial Intelligence, RET Fusion, BRAF V600E, Lateral Neck Metastasis, Deep Learning Model, 11-Gene Panel
- Medical subject headings
- Thyroid Cancer, Papillary, Thyroid Neoplasms, Lymphatic Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Ultrasound RET PredictionDiagnostic test
Deep-learning algorithm that analyses thyroid ultrasound DICOM images and outputs a probability score for RET gene alteration and lateral-neck lymph-node metastasis; run offline, results blinded to treating surgeons.
| Study group | Role | What they receive |
|---|---|---|
| Prospective Thyroid Cancer Cohort | Consecutive adults (18-75 y) with ultrasound-suspected papillary thyroid carcinoma scheduled for surgery. Each participant undergoes standard pre-operative ultrasound, rapid 14-gene next-generation sequencing (NGS) panel, and blinded AI analysis of the ultrasound images. No treatment allocation is made; data are collected prospectively to validate the AI model's ability to detect RET alterations and predict lateral-neck lymph-node metastasis. Receives: Diagnostic Test: AI-Ultrasound RET Prediction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the ROC Curve (AUC) for AI-Ultrasound Detection of RET Alterations
The receiver-operating-characteristic area under the curve comparing the AI-generated probability score against the reference 14-gene next-generation sequencing (NGS) result for RET fusion or point mutation. AUC calculated with 95 % confidence interval via DeLong method.
Measured: Date of surgery (assessment completed when gene-panel result is available)
Secondary outcomes (1)
Sensitivity and Specificity of AI-Ultrasound for Detecting RET Alterations
Using the threshold that maximized the Youden index in the development set, calculate sensitivity (true-positive rate) and specificity (true-negative rate) of the AI model versus 11-gene NGS reference for RET fusion or point mutation. Results reported with 95 % confidence intervals.
Measured: Date of surgery (assessment completed when NGS result is available)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2025
- Study start1 Jul 2025 (estimated)
- Primary completion31 Jul 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Papillary Thyroid CarcinomaStudies of the same condition, open and under way.
- More trials run by Fujian 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
- Retrospective
- Enrolment
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fujian Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Bo Wang,MD, Fujian Medical University Union Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07042984
- Sponsor's protocol ID
- RET-US
- Acronym
- RET-US
- Status last verified
- Jun 2025
- First submitted
- 21 Jun 2025
- Last updated
- 29 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- RET-US Cohort: Prospective Evaluation of an AI-Ultrasound Model for Detecting RET Gene Alterations and Predicting Lateral Cervical Lymph-Node Metastasis in Papillary Thyroid Carcinoma
- ClinicalTrials.gov ID
- NCT07042984
- Acronym / sponsor ID
- RET-US / RET-US
- Conditions
- Papillary Thyroid Carcinoma, Thyroid Neoplasms, RET Proto-Oncogene Mutation, Lymphatic Metastasis
- Keywords
- Ultrasound Radiomics, Artificial Intelligence, RET Fusion, BRAF V600E, Lateral Neck Metastasis, Deep Learning Model, 11-Gene Panel
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Fujian Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (estimated)
- Primary completion
- 31 Jul 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 29 Jun 2025 (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.
Why is this study being done? RET gene alterations occur in only 5-10 % of papillary thyroid cancers, but they can change how surgeons treat the disease. Gene testing is costly and not always performed, so many RET-positive tumours are missed. Researchers have built a computer program (artificial-intelligence or "AI" model) that reads routine thyroid ultrasound images and predicts whether the tumour carries a RET alteration and whether the cancer has already spread to lymph-nodes in the side of the neck.
What will happen in this study?
About 800 adults who are scheduled for thyroid-cancer surgery will take part. Each participant will:
- have a standard pre-operative ultrasound exam (no extra scanning time),
- give a routine fine-needle sample for a 14-gene panel test (results in 24 h), and
- allow the AI model to analyse the ultrasound images in the background. Doctors making treatment decisions will not see the AI result. After surgery, the research team will compare the AI predictions with the gene-panel result and the final pathology report.
Main goal: To find out how accurately the AI model detects RET alterations. Secondary goals: To measure the model's ability to predict lymph-node spread, and to compare costs between ultrasound-only prediction and full gene testing.
Benefits and risks: Participants will receive the current standard of care; there is no added risk beyond the usual ultrasound and needle biopsy. The study could lead to faster, less expensive ways to identify high-risk thyroid cancers in the future.
Detailed description
Background RET rearrangements or point mutations drive a minority of papillary thyroid carcinomas (PTC) yet are associated with aggressive behaviour and may qualify patients for selective RET inhibitors. Because of low prevalence, RET testing is often omitted, resulting in under-recognition. Recent work shows that high-resolution ultrasound contains radiomic signatures linked to tumour genotypes. A deep-learning model (EfficientNet-B3 backbone with dual segmentation + multi-label heads) was trained on 1 000 retrospectively collected cases, including 74 RET-positive tumours augmented with GAN-based synthetic images, achieving an AUC of 0.87 for RET prediction in internal cross-validation.
Objectives Primary: validate the AI model's area under the receiver-operating characteristic curve (AUC) for RET alteration detection in a prospective cohort.
Secondary: (i) sensitivity/specificity for RET; (ii) accuracy for predicting lateral-neck (pN1b) metastasis; (iii) incremental cost per correct RET diagnosis; (iv) concordance between AI probability score and lymph-node burden.
Design Single-arm, prospective observational cohort (n = 800). Consecutive eligible patients will undergo: (1) routine pre-operative thyroid ultrasound; (2) upload of DICOM files to a cloud inference server; (3) rapid 14-gene next-generation sequencing panel on FNA or paraffin tissue (includes RET fusions KIF5B, CCDC6, NCOA4 and point mutations M918T, V804). Surgeons remain blinded to AI output. Surgical specimens provide ground truth for pN staging. Data captured in REDCap; statistical analysis uses DeLong test for AUC and McNemar test for paired accuracy.
Eligibility Adults 18-75 y with radiologically suspected PTC, planned thyroidectomy, and consent for gene testing. Exclusions: re-operative neck, medullary/anaplastic carcinoma, pregnancy, eGFR < 30 mL min-¹ 1.73 m-².
Sample Size With expected RET prevalence 6 % and target AUC ≥ 0.80 vs null 0.50, 800 cases provide 90 % power (α = 0.05).
Ethics \& Oversight IRB approved; minimal-risk diagnostic study. Ultrasound and FNA are standard-of-care; AI inference uses de-identified images. Results will be disseminated via peer-reviewed journals and conference presentations.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective Thyroid Cancer Cohort | – | Diagnostic Test: AI-Ultrasound RET PredictionConsecutive adults (18-75 y) with ultrasound-suspected papillary thyroid carcinoma scheduled for surgery. Each participant undergoes standard pre-operative ultrasound, rapid 14-gene next-generation sequencing (NGS) panel, and blinded AI analysis of the ultrasound images. No treatment allocation is made; data are collected prospectively to validate the AI model's ability to detect RET alterations and predict lateral-neck lymph-node metastasis. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Consecutive adults with ultrasound-suspected papillary thyroid carcinoma scheduled for thyroidectomy at participating centers.
Key inclusion criteria (4 listed)
- Age 18-75 years, able to provide written informed consent.
- Pre-operative ultrasound findings highly suggestive of papillary thyroid carcinoma.
- Planned thyroidectomy (any extent) at a participating institution.
- Willing to undergo rapid 11-gene next-generation sequencing (NGS) panel and allow use of ultrasound DICOM images for AI analysis.
Key exclusion criteria (5 listed)
- Prior thyroid or major neck surgery.
- Known medullary thyroid carcinoma, anaplastic carcinoma, or metastatic disease outside the neck.
- Multiple endocrine neoplasia (MEN) syndromes or clinical suspicion of multi-gland disease.
- Pregnant or breastfeeding.
- Severe renal impairment (eGFR < 30 mL/min/1.73 m²) or other condition that precludes surgery or gene testing.
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 |
|---|---|---|
| AI-Ultrasound RET PredictionDeep-learning algorithm that analyses thyroid ultrasound DICOM images and outputs a probability score for RET gene alteration and lateral-neck lymph-node metastasis; run offline, results blinded to treating surgeons. | Diagnostic test | Prospective Thyroid Cancer Cohort |
Primary outcome
Area Under the ROC Curve (AUC) for AI-Ultrasound Detection of RET Alterations
Time frame: Date of surgery (assessment completed when gene-panel result is available)
The receiver-operating-characteristic area under the curve comparing the AI-generated probability score against the reference 14-gene next-generation sequencing (NGS) result for RET fusion or point mutation. AUC calculated with 95 % confidence interval via DeLong method.
1 secondary outcome
Sensitivity and Specificity of AI-Ultrasound for Detecting RET Alterations
Time frame: Date of surgery (assessment completed when NGS result is available)
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 29 Jun 2025.
- Individual participant dataSharing planned: Undecided
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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 Papillary Thyroid Carcinoma and Thyroid Neoplasms, run by Fujian 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 4 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: opening soon, as of the sponsor's last update on 29 Jun 2025. 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 Bo WANG, MD PhD (+8613959123550, wangbo@fjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the ROC Curve (AUC) for AI-Ultrasound Detection of RET Alterations (time frame: Date of surgery (assessment completed when gene-panel result is available)). 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 29 Jun 2025; AzTrials last refreshed its copy on 7 Nov 2026.