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Multimodal ultrasonographic and clinicopathological model for predicting high-volume lymph node metastasis in cN0 papillary thyroid carcinoma

  • 0Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Frontiers in Endocrinology +

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September 8, 2025

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September 8, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A new predictive model accurately identifies high-volume lymph node metastasis (HVLNM) in papillary thyroid cancer (PTC) patients. This tool aids clinicians in personalizing treatment and reducing reoperations.

Area Of Science

  • Oncology
  • Radiology
  • Genetics

Background

  • Preoperative diagnosis of high-volume lymph node metastasis (HVLNM) in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) presents a significant clinical challenge.
  • Accurate prediction of HVLNM is crucial for effective treatment planning and reducing unnecessary interventions.

Purpose Of The Study

  • To construct and validate a comprehensive predictive model for HVLNM in cN0 PTC.
  • To integrate conventional ultrasound, contrast-enhanced ultrasound (CEUS), BRAF<sup>V600E</sup> mutation status, and clinicopathological data into a predictive tool.

Main Methods

  • A retrospective study included 126 cN0 PTC patients for training and 47 for external validation.
  • Univariate and multivariate analyses identified independent predictors.
  • A binary logistic regression model and nomogram were developed and validated using cross-validation, calibration curves, and decision curve analysis (DCA).

Main Results

  • Age, Dmax, ACR scores ≥11, and heterogeneous enhancement were identified as independent predictors of HVLNM.
  • The nomogram achieved an Area Under the Curve (AUC) of 0.860 in the training set and 0.885 in the external validation set.
  • The model demonstrated robust calibration and clinical utility via DCA.

Conclusions

  • The developed nomogram is a simple, cost-effective tool for predicting HVLNM in cN0 PTC.
  • This visualization tool assists clinicians in formulating personalized treatment strategies and potentially lowering reoperation rates.

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