Clinical trial NCT06871956: 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
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Papillary Thyroid Microcarcinoma
Keywords: Papillary Thyroid Microcarcinoma
Lead sponsor: First Affiliated Hospital of Chongqing Medical University
Sponsor class: OTHER
First posted: 2025-03-12
Enrollment: 4882
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
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.
timeFrame: 2016-2023
Secondary outcomes:
Other outcomes:
Locations:
city: Chongqing
country: China
facility: 1 Friendship Road, Yuzhong District Chongqing
Study officials:
References: