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
NCT ID: NCT06871956
Last Updated: 2025-03-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
4882 participants
OBSERVATIONAL
2016-01-01
2023-12-31
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
PROSPECTIVE
Study Groups
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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.
PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma
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
Interventions
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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
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
Eligibility Criteria
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Inclusion Criteria
* cN0-PTMC patients diagnosed through fine-needle aspiration and imaging.
Exclusion Criteria
* Other pathological types of thyroid cancer
* Incomplete clinical data
* Distant metastasis or history of cervical radiation exposure.
ALL
No
Sponsors
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First Affiliated Hospital of Chongqing Medical University
OTHER
Responsible Party
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Xinliang Su
Chief Physician
Locations
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1 Friendship Road, Yuzhong District Chongqing
Chongqing, , China
Countries
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Other Identifiers
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1stHospitalofChongqingMU
Identifier Type: -
Identifier Source: org_study_id
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