Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks
10.3971/j.issn.1000-8578.2025.24.0617
- VernacularTitle:基于超声影像组学及深度神经网络预测甲状腺乳头状癌pN分期
- Author:
Jieli ZHOU
1
;
Linjuan WU
2
;
Pengtian ZHANG
3
;
Yanxia PENG
4
;
Dong HAN
3
Author Information
1. Department of Ultrasound, The First Affiliated Hospital of Air Force Medical University, Xi’an 710032, China.
2. Department of Ultrasound, Xi’an Fengcheng Hospital, Xi’an 710018, China.
3. Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China.
4. Department of Ultrasound, Northwest Women’s and Children’s Hospital, Xi’an 710061, China.
- Publication Type:CLINICALRESEARCH
- Keywords:
Thyroid Cancer;
Papillary;
Ultrasonography;
pN staging;
Predicting;
Lymph nodes
- From:
Cancer Research on Prevention and Treatment
2025;52(2):151-155
- CountryChina
- Language:Chinese
-
Abstract:
Objective To assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). Methods A retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 in the test set. Staging was categorized as pN0 (no cervical lymph node metastasis), pN1a (central neck lymph node metastasis), and pN1b (lateral neck lymph node metastasis). An ultrasound physician manually segmented the regions of interest (ROIs) for PTC, extracting 1899 radiomic features. Dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) regression. A DNN model for predicting PTC pN staging was developed using the H2O deep learning platform, trained on the training set, and validated on the test set to assess the accuracy of the optimal model. Results A total of 153 patients were in the pN0 stage, 131 patients in the pN1a stage, and 91 patients in the pN1b stage. LASSO regression selected 15 radiomic features for each PTC. The optimal DNN model, constructed using these 15 features, achieved accuracies of 85.82% on the training set and 81.57% on the test set. Conclusion Ultrasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients.
