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

NCT06871956 | Completed DMC

• 1 other identifier: 1stHospitalofChongqingMU
• Study Type: observational
• Target: 4,882
• Locations: 1 country
• Sites: 1

Brief 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 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

Keywords

Papillary Thyroid Microcarcinoma

Outcome Measures

Primary Outcomes (1)

• 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.

  2016-2023

Study Arms (1)

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.

Diagnostic Test: PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma

Interventions

PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma DIAGNOSTIC_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

After applying inclusion criteria, 1,953 patients from the primary center were allocated to model tr

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• First-time thyroid cancer surgery patients
• cN0-PTMC patients diagnosed through fine-needle aspiration and imaging.

You may not qualify if:

• Secondary surgery
• Other pathological types of thyroid cancer
• Incomplete clinical data
• Distant metastasis or history of cervical radiation exposure.

Sponsors & Collaborators

• First Affiliated Hospital of Chongqing Medical University: lead

Study Sites (1)

1 Friendship Road, Yuzhong District Chongqing

Chongqing, China

Location

MeSH Terms

Conditions

Papillary Thyroid Microcarcinoma

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: February 21, 2025
• First Posted: March 12, 2025
• Study Start: January 1, 2016
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: March 12, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)