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A risk stratification model based on ultrasound radiologic features for cervical metastatic lymph nodes in papillary

Hai-Long Tan¹, Sai-Li Duan², Qiao He²

¹Department of General Surgery, Xiangya Hospital Central South University, Changsha, Hunan, 410008, P.R. China. tanhailong@csu.edu.cn.

World Journal of Surgical Oncology

|March 26, 2025

Summary

This study developed a new ultrasound-based risk model to predict lymph node metastasis in papillary thyroid carcinoma (PTC) patients. The model identifies key US features to improve preoperative evaluation and surgical planning for PTC.

Keywords:

Lymph node Papillary thyroid carcinoma Risk stratification model Ultrasound

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Area of Science:

Radiology
Oncology
Medical Imaging

Background:

Preoperative evaluation of metastatic lesions is crucial for papillary thyroid carcinoma (PTC) patients.
Existing stratification systems show inconsistencies in ultrasound (US) features of cervical metastatic lymph nodes (LNs).

Purpose of the Study:

To investigate and develop a risk stratification model based on US radiologic features for cervical metastatic lesions in PTC patients.

Main Methods:

Retrospective enrollment of 1806 LNs from 1665 PTC patients who underwent US-guided fine-needle aspiration biopsy.
Univariable and multivariable logistic regression analyses to identify independent risk US features and develop a risk stratification model.
Performance assessment and validation of the model by the Korean Society of Thyroid Radiology and the European Thyroid Association.

Main Results:

Multivariate analysis identified absence of fatty hilum, cystic components, round shape, abundant vascularity, hyperechogenicity, and calcifications as independent risk US features for malignant LNs.
A risk stratification model was developed with well-predicted performance (C-index 0.840).

Conclusions:

A novel risk stratification system using US radiologic features was proposed to predict cervical LN metastasis in PTC patients.
Identified risk factors include absence of fatty hilum, cystic components, round shape, abnormal vascularity, and specific echogenicity and calcification patterns.
These US features serve as valuable indicators for accurate assessment of cervical LN status in PTC.