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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

Development and validation of a collagen signature to predict central lymph node metastasis in papillary thyroid

Weisheng Chen¹, Junna Ge¹, Tingyue Luo²

¹Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.

Frontiers in Endocrinology

|December 15, 2025

Summary

This summary is machine-generated.

A new collagen signature in the tumor microenvironment effectively predicts central lymph node metastasis in papillary thyroid cancer. This biomarker enhances prediction accuracy, improving patient management.

Keywords:

collagen signature lymph node metastasis papillary thyroid cancer predictive model tumor microenvironment

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Area of Science:

Oncology
Pathology
Biomarker Discovery

Background:

Clinicopathological risk factors have limited precision for predicting central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC).
Tumor microenvironment (TME) structural remodeling, specifically collagen organization, is implicated in metastatic spread.

Purpose of the Study:

To develop a collagen signature within the TME for predicting CLNM in PTC.
To validate if incorporating this signature with clinicopathological factors improves predictive accuracy.

Main Methods:

Retrospective analysis of 350 PTC patients undergoing thyroidectomy and central lymph node dissection.
Extraction of 142 collagen features from tumor specimens using second harmonic generation imaging.
Development of a collagen signature via LASSO regression and integration into a nomogram using multivariate logistic regression.

Main Results:

The collagen signature demonstrated predictive ability with AUCs of 0.821 (training) and 0.793 (testing).
The signature was an independent predictor, and the integrated model significantly outperformed the clinicopathological model alone (AUC 0.842 vs. 0.679, p < 0.001).

Conclusions:

A TME-based collagen signature is a promising biomarker for predicting CLNM in PTC.
This signature can enhance clinical decision-making and patient management for PTC.