Papillary Thyroid CarcinomaNCT07299318
Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer
An observational study of Papillary Thyroid Carcinoma, run by West China Hospital at 1 site in China.
Trial summary
Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies. The occurrence of cervical lymph node metastasis further increases the risk of local tumor recurrence and distant metastasis, thereby reducing patient survival… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Papillary Thyroid Carcinoma
- What is being tested?
- not intervention
- How many people
- 3,200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2026to 1 May 2026
- 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 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Clinical data from patients who underwent thyroidectomy at West China Hospital of Sichuan University and its affiliated branch hospitals between October 2020 and October 2025 were retrospectively collected and analyzed.
Who may be able to join
- Cases aged 18-80 years who underwent thyroid ultrasound examination and postoperative pathological examination of the thyroid.
- Cases with a first-time diagnosis of papillary thyroid carcinoma. Cases who underwent lymph node dissection
Who cannot join
- Cases aged <18 years or >80 years. Cases with poor-quality ultrasound images. Cases with incompletely visualized nodules. Cases with images showing multiple distinct lesions. Cases belonging to special populations. Cases with concurrent other tumors. Cases with a history of thyroid cancer resection
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Yu Feng contact
China1 site
West China hospital of Sichuan University
Chengdu, Sichuan, 610041
Jianyong Lei, +86 19983137992, leijianyong@scu.edu.cn
Yu Feng, +86 15183042703, 1350502131@qq.com
About this study
Official title: A Multicenter Study on Developing a Multimodal Deep Learning Model Based on Color Doppler Ultrasound for Predicting Lymph Node Metastasis and Cancer Staging in Papillary Thyroid Carcinoma
Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies. The occurrence of cervical lymph node metastasis further increases the risk of local tumor recurrence and distant metastasis, thereby reducing patient survival rates. Pathological examinations reveal that approximately 30-80% of PTC patients have lymph node metastasis. Early detection of metastatic lymph nodes and the development of individualized treatment plans are crucial for improving patient prognosis. Currently, the primary method for diagnosing lymph node metastasis is ultrasound-guided fine-needle aspiration, but its accuracy is limited by sample quality and carries a risk of false-negative results. In recent years, deep learning technology has demonstrated significant potential in the field of medical image analysis. Therefore, the investigators aim to develop a deep learning model based on neck ultrasound to more accurately predict lymph node metastasis.
- Conditions
- Papillary Thyroid Carcinoma
- Being tested
- not intervention
- Medical subject headings
- Thyroid Cancer, Papillary
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
not interventionOther
This is a retrospective observational study in which participants will not undergo any interventions, and only data collection and analysis will be performed on the participants.
| Study group | Role | What they receive |
|---|---|---|
| Papillary thyroid carcinoma group | Not described Receives: Other: not intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis
The researcher will employ a multimodal deep learning model that integrates preoperative cervical color Doppler ultrasound images with corresponding structured text reports. The final output of the model is a predicted probability of lymph node metastasis for each patient (a continuous value between 0 and 1). This predicted probability will be compared with postoperative histopathological diagnosis results (the gold standard). A receiver operating characteristic curve will be plotted for the model, and its area under the curve will be calculated.This is the gold standard metric for evaluating the discriminative ability of a binary classification model (metastasis vs. non-metastasis). A higher AUC value indicates stronger overall discriminative power of the model.
Measured: Within 2 months after the completion of subject enrollment
Sensitivity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis
This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly identify patients with papillary thyroid carcinoma who truly have cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathology, as well as the number of patients predicted as "positive" (i.e., predicted to have metastasis) by the model, in order to calculate the sensitivity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Sensitivity = Number of true positive patients / Total number of positive patients confirmed by postoperative pathology.
Measured: Within 2 months after the completion of subject enrollment.
Specificity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis
This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly rule out patients with papillary thyroid carcinoma who have not developed cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed without lymph node metastasis via postoperative pathology, as well as the number of patients predicted by the model as "negative" (i.e., predicted to have no metastasis), in order to calculate the specificity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Specificity = Number of true negative patients / Total number of negative patients confirmed by postoperative pathology.
Measured: Within 2 months after the completion of subject enrollment.
Secondary outcomes (3)
The pathologically confirmed lymph node metastasis rate in the study cohort
It refers to the percentage of patients with at least one metastatic lymph node confirmed by postoperative pathological examination, relative to the total number of individuals in the corresponding study population. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathological examination.
Measured: Within 2 months after the completion of subject enrollment
Adjusted Odds Ratios for Clinical Factors Associated with Pathologically Confirmed Lymph Node Metastasis
Researchers need to collect the outcome variable (i.e., postoperatively pathologically confirmed lymph node metastasis status) and its exposure variables (such as the specific location of the primary tumor within the thyroid gland, maximum tumor diameter, patient age, etc.). Using these variables, the adjusted odds ratios are calculated to reflect, after adjusting for other confounding factors, how many times more likely patients with a specific exposure characteristic (e.g., tumor located in the upper pole) are to have lymph node metastasis compared to patients in the reference group (e.g., tumor located in the lower pole).
Measured: Within 2 months after the completion of subject enrollment
The weighted Kappa coefficient for the consistency between model-predicted pTNM stage and pathological stage
Researchers need to collect and record the model-predicted pTNM stage and the patient's true pTNM stage to evaluate the consistency between the model-predicted complete pTNM stage and the pathological stage.
Measured: Within 2 months after the completion of subject enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Dec 2025
- Study start1 Jan 2026 (estimated)
- Primary completion1 Mar 2026 (estimated)
- Study completion1 May 2026 (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 West China HospitalThe 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
- 3,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- West China Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yu Feng, West China Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07299318
- Sponsor's protocol ID
- 2025(2352)
- Status last verified
- Dec 2025
- First submitted
- 9 Dec 2025
- Last updated
- 23 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- 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 23 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Study on Developing a Multimodal Deep Learning Model Based on Color Doppler Ultrasound for Predicting Lymph Node Metastasis and Cancer Staging in Papillary Thyroid Carcinoma
- ClinicalTrials.gov ID
- NCT07299318
- Acronym / sponsor ID
- 2025(2352)
- Conditions
- Papillary Thyroid Carcinoma
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 3,200 participants (estimated)
- Lead sponsor
- West China HospitalUniversity, hospital or other
- Study start
- 1 Jan 2026 (estimated)
- Primary completion
- 1 Mar 2026 (estimated)
- Study completion
- 1 May 2026 (estimated)
- First posted
- 23 Dec 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.
Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies. The occurrence of cervical lymph node metastasis further increases the risk of local tumor recurrence and distant metastasis, thereby reducing patient survival rates. Pathological examinations reveal that approximately 30-80% of PTC patients have lymph node metastasis. Early detection of metastatic lymph nodes and the development of individualized treatment plans are crucial for improving patient prognosis. Currently, the primary method for diagnosing lymph node metastasis is ultrasound-guided fine-needle aspiration, but its accuracy is limited by sample quality and carries a risk of false-negative results. In recent years, deep learning technology has demonstrated significant potential in the field of medical image analysis. Therefore, the investigators aim to develop a deep learning model based on neck ultrasound to more accurately predict lymph node metastasis.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Papillary thyroid carcinoma group | – | Other: not intervention |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Clinical data from patients who underwent thyroidectomy at West China Hospital of Sichuan University and its affiliated branch hospitals between October 2020 and October 2025 were retrospectively collected and analyzed.
Key inclusion criteria (2 listed)
- Cases aged 18-80 years who underwent thyroid ultrasound examination and postoperative pathological examination of the thyroid.
- Cases with a first-time diagnosis of papillary thyroid carcinoma. Cases who underwent lymph node dissection
Key exclusion criteria (1 listed)
- Cases aged <18 years or >80 years. Cases with poor-quality ultrasound images. Cases with incompletely visualized nodules. Cases with images showing multiple distinct lesions. Cases belonging to special populations. Cases with concurrent other tumors. Cases with a history of thyroid cancer resection
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 |
|---|---|---|
| not interventionThis is a retrospective observational study in which participants will not undergo any interventions, and only data collection and analysis will be performed on the participants. | Other | Papillary thyroid carcinoma group |
Primary outcome
Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis
Time frame: Within 2 months after the completion of subject enrollment
The researcher will employ a multimodal deep learning model that integrates preoperative cervical color Doppler ultrasound images with corresponding structured text reports. The final output of the model is a predicted probability of lymph node metastasis for each patient (a continuous value between 0 and 1). This predicted probability will be compared with postoperative histopathological diagnosis results (the gold standard). A receiver operating characteristic curve will be plotted for the model, and its area under the curve will be calculated.This is the gold standard metric for evaluating the discriminative ability of a binary classification model (metastasis vs. non-metastasis). A higher AUC value indicates stronger overall discriminative power of the model.
Sensitivity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis
Time frame: Within 2 months after the completion of subject enrollment.
This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly identify patients with papillary thyroid carcinoma who truly have cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathology, as well as the number of patients predicted as "positive" (i.e., predicted to have metastasis) by the model, in order to calculate the sensitivity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Sensitivity = Number of true positive patients / Total number of positive patients confirmed by postoperative pathology.
Specificity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis
Time frame: Within 2 months after the completion of subject enrollment.
This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly rule out patients with papillary thyroid carcinoma who have not developed cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed without lymph node metastasis via postoperative pathology, as well as the number of patients predicted by the model as "negative" (i.e., predicted to have no metastasis), in order to calculate the specificity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Specificity = Number of true negative patients / Total number of negative patients confirmed by postoperative pathology.
3 secondary outcomes
The pathologically confirmed lymph node metastasis rate in the study cohort
Time frame: Within 2 months after the completion of subject enrollment
Adjusted Odds Ratios for Clinical Factors Associated with Pathologically Confirmed Lymph Node Metastasis
Time frame: Within 2 months after the completion of subject enrollment
The weighted Kappa coefficient for the consistency between model-predicted pTNM stage and pathological stage
Time frame: Within 2 months after the completion of subject enrollment
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 23 Dec 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.
Yu Feng contact
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, run by West China Hospital 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 2 inclusion and 1 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 23 Dec 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 Jianyong Lei (+86 19983137992, leijianyong@scu.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 Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis (time frame: Within 2 months after the completion of subject enrollment). The study lists 3 primary outcomes in all. 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 23 Dec 2025.