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 (PMRA)
March 6, 2025 updated by: Xinliang Su, First Affiliated Hospital of Chongqing Medical University
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 Overview
Status
Completed
Conditions
Study Type
Observational
Enrollment (Actual)
4882
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Chongqing, China
- 1 Friendship Road, Yuzhong District Chongqing
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Sampling Method
Non-Probability Sample
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.
Description
Inclusion Criteria:
- First-time thyroid cancer surgery patients
- cN0-PTMC patients diagnosed through fine-needle aspiration and imaging.
Exclusion Criteria:
- Secondary surgery
- Other pathological types of thyroid cancer
- Incomplete clinical data
- Distant metastasis or history of cervical radiation exposure.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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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.
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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
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
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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
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2016-2023
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Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 1, 2016
Primary Completion (Actual)
December 31, 2023
Study Completion (Actual)
December 31, 2023
Study Registration Dates
First Submitted
February 21, 2025
First Submitted That Met QC Criteria
March 6, 2025
First Posted (Actual)
March 25, 2025
Study Record Updates
Last Update Posted (Actual)
March 25, 2025
Last Update Submitted That Met QC Criteria
March 6, 2025
Last Verified
February 1, 2025
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
- Endocrine System Diseases
- Pathologic Processes
- Neoplasms by Site
- Neoplasms
- Neoplasms by Histologic Type
- Endocrine Gland Neoplasms
- Head and Neck Neoplasms
- Neoplasms, Glandular and Epithelial
- Neoplastic Processes
- Carcinoma
- Neoplasms, Squamous Cell
- Neoplasm Metastasis
- Thyroid Diseases
- Thyroid Neoplasms
- Carcinoma, Papillary
- Lymphatic Metastasis
Other Study ID Numbers
- 1stHospitalofChongqingMU
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
IPD Plan Description
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.
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
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