Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks

• Published in: Zhongliu fangzhi yanjiu Vol. 52; no. 2; pp. 151 - 155
• Main Authors: Zhou, Jieli, Wu, Linjuan, Zhang, Pengtian, Peng, Yanxia, Han, Dong
• Format: Journal Article
• Language: Chinese; English
• Published: Tianjin China Anti-Cancer Association 01.02.2025; Magazine House of Cancer Research on Prevention and Treatment
• Subjects: Accuracy; Artificial neural networks; Deep learning; Lymph nodes; Lymphatic system; Machine learning; Metastases; Metastasis; Neural networks; papillary; Papillary thyroid carcinoma; Patients; pn staging; predicting; Radiomics; Test sets; Thyroid; Thyroid cancer; Ultrasonic imaging; ultrasonography; Ultrasound
• ISSN: 1000-8578
• DOI: 10.3971/j.issn.1000-8578.2025.24.0617

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 1 899 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.