The clinical value of ultrasound radiomics combined with clinical data nomogram in predicting cervical lymph node metastasis of papillary thyroid carcinoma.
- 1Department of Ultrasound, Yijishan Hospital, The First Affiliated Hospital of Wannan Medical University, Wuhu, China.
- 2The First Affiliated Hospital of Soochow University, Suzhou, China.
Abstract
Objectives
To evaluate the predictive value of a nomogram incorporating ultrasound and clinical features for cervical lymph node metastasis (CLNM) in patients with thyroid cancer.Methods
A retrospective analysis was conducted on patients pathologically diagnosed with papillary thyroid carcinoma (PTC) between April 2024 and January 2025. Radiomics features were extracted from two-dimensional thyroid ultrasound images, and feature selection was performed using the least absolute shrinkage and selection operator (LASSO). Five classifiers were compared, and logistic regression was selected to construct the radiomics model. A clinical-radiomics model integrating the radiomics score and clinical predictors, together with a standalone clinical model, was developed using multivariable logistic regression. Model discrimination, calibration, and clinical utility were evaluated in a held-out internal validation set.Results
Sex, Radscore, age, and thyroid stimulating hormone (TSH) levels were identified as independent predictors of CLNM in patients with PTC. Based on these variables, radiomics, clinical, and clinical radiomics models were constructed; the models achieved area under the curve (AUC) values of 0.735, 0.792, and 0.867, respectively, in the validation set. DeLong's test and decision curve analysis (DCA) demonstrated that the clinical radiomics model outperformed the radiomics model in the validation set, offering a higher net clinical benefit.Conclusion
A nomogram integrating ultrasound imaging and clinical data effectively predicts the risk of CLNM in patients with PTC and may assist clinicians in developing individualized treatment strategies.
Related
- Multimodal radiomics based on dynamic contrast-enhanced ultrasound for predicting cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto’s thyroiditis
- Predicting high-burden central compartment lymph node metastasis (≥ 3 positive nodes) in papillary thyroid carcinoma: a habitat radiomic framework based on reconstructed contrast-enhanced CT
- Prediction of gross extrathyroidal extension in papillary thyroid carcinoma using a deep learning radiomics model based on post-processing techniques for multi-phase contrast-enhanced MRI images
- Treatment strategies for unilateral papillary thyroid carcinoma with ipsilateral cervical lymph node metastasis: a narrative review
- Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study
BibTeX
@article{zhang2026clinical,
title = {{The clinical value of ultrasound radiomics combined with clinical data nomogram in predicting cervical lymph node metastasis of papillary thyroid carcinoma.}},
author = {Xuxia Zhang and Biyun Sun and Feng Jiang and Yuxin Chu and Xingyu Wang and Wenhui Sun and Tianjun Wei and Ruiyang Hong and Qianqing Ma},
journal = {Frontiers in oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1811434},
url = {https://europepmc.org/article/MED/42908715},
note = {CC BY},
}
RIS
TY - JOUR
TI - The clinical value of ultrasound radiomics combined with clinical data nomogram in predicting cervical lymph node metastasis of papillary thyroid carcinoma.
AU - Xuxia Zhang
AU - Biyun Sun
AU - Feng Jiang
AU - Yuxin Chu
AU - Xingyu Wang
AU - Wenhui Sun
AU - Tianjun Wei
AU - Ruiyang Hong
AU - Qianqing Ma
PY - 2026
DA - 2026/10/07
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1811434
UR - https://europepmc.org/article/MED/42908715
AB - Objectives To evaluate the predictive value of a nomogram incorporating ultrasound and clinical features for cervical lymph node metastasis (CLNM) in patients with thyroid cancer.Methods A retrospective analysis was conducted on patients pathologically diagnosed with papillary thyroid carcinoma (PTC) between April 2024 and January 2025. Radiomics features were extracted from two-dimensional thyroid ultrasound images, and feature selection was performed using the least absolute shrinkage and selection operator (LASSO). Five classifiers were compared, and logistic regression was selected to construct the radiomics model. A clinical-radiomics model integrating the radiomics score and clinical predictors, together with a standalone clinical model, was developed using multivariable logistic regression. Model discrimination, calibration, and clinical utility were evaluated in a held-out internal validation set.Results Sex, Radscore, age, and thyroid stimulating hormone (TSH) levels were identified as independent predictors of CLNM in patients with PTC. Based on these variables, radiomics, clinical, and clinical radiomics models were constructed; the models achieved area under the curve (AUC) values of 0.735, 0.792, and 0.867, respectively, in the validation set. DeLong's test and decision curve analysis (DCA) demonstrated that the clinical radiomics model outperformed the radiomics model in the validation set, offering a higher net clinical benefit.Conclusion A nomogram integrating ultrasound imaging and clinical data effectively predicts the risk of CLNM in patients with PTC and may assist clinicians in developing individualized treatment strategies.
SN - 2234-943X
ER -