Papillary Thyroid MicrocarcinomaPMRANCT06871956
PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma: a Prospective Multicenter Validation and Development of a Web Calculator
An observational study of Papillary Thyroid Microcarcinoma, run by First Affiliated Hospital of Chongqing Medical University at 1 site in China.
Trial summary
Background:Management of clinically node-negative(cN0) papillary thyroid microcarcinoma (PTMC) is complicated by high occult lymph node metastasis (LNM) rates. We aimed to develop and validate a prediction model for central LNM using machine learning (ML) and traditional nomograms through Probability-based Ranking… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Papillary Thyroid Microcarcinoma
- What is being tested?
- PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma
- How many people
- 4,882 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2016to 31 Dec 2023
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: A retrospective analysis was conducted on 4,882 cases from the First Affiliated Hospital of Chongqing Medical University (Hospital A) between 2016 and 2020, collecting clinical, ultrasound, and intraoperative frozen pathology data. After applying inclusion and exclusion criteria, 1,953 patients were selected for model development and internal validation (split in a 7:3 ratio). For prospective external validation, patients from two additional centers were included: 286 cases from Women and Children's Hospital of Chongqing Medical University (Hospital B) and 176 cases from The People's Hospital of Yubei District of Chongqing (Hospital C), designated as external validation sets 1 and 2, respectively.
Who may be able to join
- First-time thyroid cancer surgery patients
- cN0-PTMC patients diagnosed through fine-needle aspiration and imaging.
Who cannot join
- Secondary surgery
- Other pathological types of thyroid cancer
- Incomplete clinical data
- Distant metastasis or history of cervical radiation exposure.
Trial locations and contacts
1 site in 1 country.
About this study
Background:Management of clinically node-negative(cN0) papillary thyroid microcarcinoma (PTMC) is complicated by high occult lymph node metastasis (LNM) rates. We aimed to develop and validate a prediction model for central LNM using machine learning (ML) and traditional nomograms through Probability-based Ranking Model Approach (PMRA).
Methods: We conducted a prospective multicenter study involving 4,882 patients across 3 hospitals (2016-2023). After applying inclusion criteria, 1,953 patients from the primary center were allocated to model train and test (7:3 ratio). External validation included prospective cohorts of 286 and 176 patients from two independent centers.13 ML algorithms and traditional nomogram models were systematically evaluated using PMRA.We compared models using preoperative features alone versus those incorporating both preoperative and intraoperative frozen section pathology data. Feature selection utilized six methods, with L1-based selection proving optimal for most predictions.Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) visualization.
- Conditions
- Papillary Thyroid Microcarcinoma
- Being tested
- PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma
- Keywords
- Papillary Thyroid Microcarcinoma
- Medical subject headings
- Papillary Thyroid Microcarcinoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid MicrocarcinomaDiagnostic test
PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma: A Prospective Multicenter Validation and Development of a Web Calculator
| Study group | Role | What they receive |
|---|---|---|
| After applying inclusion criteria, 1,953 patients from the primary center were allocated to model tr | After applying inclusion criteria, 1,953 patients from the primary center were allocated to model train and test (7:3 ratio). External validation included prospective cohorts of 286 and 176 patients from two independent centers.13 ML algorithms and traditional nomogram models were systematically evaluated using PMRA.We compared models using preoperative features alone versus those incorporating both preoperative and intraoperative frozen section pathology data. Feature selection utilized six methods, with L1-based selection proving optimal for most predictions.Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) visualization. Receives: Diagnostic Test: PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictors were analyzed after the data of 1953 patients were included in the training set and internal validation (at a ratio of 7:3), and the predictors were analyzed in 286 patients and 176 patients, respectively, in two external validation centers.
Measured: 2016-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2025
- Study start1 Jan 2016
- Primary completion31 Dec 2023
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Papillary Thyroid MicrocarcinomaStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 4,882 participants (actual)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xinliang Su, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06871956
- Sponsor's protocol ID
- 1stHospitalofChongqingMU
- Acronym
- PMRA
- Status last verified
- Feb 2025
- First submitted
- 21 Feb 2025
- Last updated
- 12 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The main reasons are as follows: IPD contains personal sensitive information, and sharing it may infringe on privacy and violate data protection regulations (such as GDPR). Sharing IPD may pose risks of data leakage or misuse, especially when it is not adequately anonymized. The use of data is subject to legal or contractual constraints and cannot be shared without permission. IPD may involve intellectual property rights or business secrets, and sharing it may affect the rights of data owners. Sharing IPD may violate the informed consent of research participants and trigger ethical disputes. The lack of background information may lead to incorrect interpretation or misuse of the data. The process of data preparation, anonymization, and sharing is time-consuming and labor-intensive, adding additional burdens. Therefore, IPD sharing should be done with caution and usually requires strict review and protection by agreement.
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma: a Prospective Multicenter Validation and Development of a Web Calculator
- ClinicalTrials.gov ID
- NCT06871956
- Acronym / sponsor ID
- PMRA / 1stHospitalofChongqingMU
- Conditions
- Papillary Thyroid Microcarcinoma
- Keywords
- Papillary Thyroid Microcarcinoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 4,882 participants (actual)
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical UniversityUniversity, hospital or other
- Study start
- 1 Jan 2016 (actual)
- Primary completion
- 31 Dec 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 12 Mar 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.
Background:Management of clinically node-negative(cN0) papillary thyroid microcarcinoma (PTMC) is complicated by high occult lymph node metastasis (LNM) rates. We aimed to develop and validate a prediction model for central LNM using machine learning (ML) and traditional nomograms through Probability-based Ranking Model Approach (PMRA).
Methods: We conducted a prospective multicenter study involving 4,882 patients across 3 hospitals (2016-2023). After applying inclusion criteria, 1,953 patients from the primary center were allocated to model train and test (7:3 ratio). External validation included prospective cohorts of 286 and 176 patients from two independent centers.13 ML algorithms and traditional nomogram models were systematically evaluated using PMRA.We compared models using preoperative features alone versus those incorporating both preoperative and intraoperative frozen section pathology data. Feature selection utilized six methods, with L1-based selection proving optimal for most predictions.Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) visualization.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| After applying inclusion criteria, 1,953 patients from the primary center were allocated to model tr | – | Diagnostic Test: PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid MicrocarcinomaAfter applying inclusion criteria, 1,953 patients from the primary center were allocated to model train and test (7:3 ratio). External validation included prospective cohorts of 286 and 176 patients from two independent centers.13 ML algorithms and traditional nomogram models were systematically evaluated using PMRA.We compared models using preoperative features alone versus those incorporating both preoperative and intraoperative frozen section pathology data. Feature selection utilized six methods, with L1-based selection proving optimal for most predictions.Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) visualization. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- A retrospective analysis was conducted on 4,882 cases from the First Affiliated Hospital of Chongqing Medical University (Hospital A) between 2016 and 2020, collecting clinical, ultrasound, and intraoperative frozen pathology data. After applying inclusion and exclusion criteria, 1,953 patients were selected for model development and internal validation (split in a 7:3 ratio). For prospective external validation, patients from two additional centers were included: 286 cases from Women and Children's Hospital of Chongqing Medical University (Hospital B) and 176 cases from The People's Hospital of Yubei District of Chongqing (Hospital C), designated as external validation sets 1 and 2, respectively.
Key inclusion criteria (2 listed)
- First-time thyroid cancer surgery patients
- cN0-PTMC patients diagnosed through fine-needle aspiration and imaging.
Key exclusion criteria (4 listed)
- Secondary surgery
- Other pathological types of thyroid cancer
- Incomplete clinical data
- Distant metastasis or history of cervical radiation exposure.
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 |
|---|---|---|
| PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid MicrocarcinomaPMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma: A Prospective Multicenter Validation and Development of a Web Calculator | Diagnostic test | After applying inclusion criteria, 1,953 patients from the primary center were allocated to model tr |
Primary outcome
Predictors were analyzed after the data of 1953 patients were included in the training set and internal validation (at a ratio of 7:3), and the predictors were analyzed in 286 patients and 176 patients, respectively, in two external validation centers.
Time frame: 2016-2023
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Mar 2025.
- Individual participant dataSharing planned: No. The main reasons are as follows: IPD contains personal sensitive information, and sharing it may infringe on privacy and violate data protection regulations (such as GDPR). Sharing IPD may pose risks of data leakage or misuse, especially when it is not adequately anonymized. The use of data is subject to legal or contractual constraints and cannot be shared without permission. IPD may involve intellectual property rights or business secrets, and sharing it may affect the rights of data owners. Sharing IPD may violate the informed consent of research participants and trigger ethical disputes. The lack of background information may lead to incorrect interpretation or misuse of the data. The process of data preparation, anonymization, and sharing is time-consuming and labor-intensive, adding additional burdens. Therefore, IPD sharing should be done with caution and usually requires strict review and protection by agreement.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Microcarcinoma, run by First Affiliated Hospital of Chongqing 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 2 inclusion and 4 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: finished, as of the sponsor's last update on 12 Mar 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictors were analyzed after the data of 1953 patients were included in the training set and internal validation (at a ratio of 7:3), and the predictors were analyzed in 286 patients and 176 patients, respectively, in two external validation centers. (time frame: 2016-2023). 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 12 Mar 2025.