肿瘤防治研究2025，Vol.52Issue(2)：151-155,5.DOI:10.3971/j.issn.1000-8578.2025.24.0617
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
Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks
周洁丽 1武林娟 2张鹏天 3彭艳侠 4韩冬3
作者信息
- 1. 710032 西安,空军军医大学第一附属医院超声科
- 2. 710018 西安,西安市凤城医院超声科
- 3. 712000 咸阳,陕西中医药大学附属医院医学影像科
- 4. 710061 西安,西北妇女儿童医院超声科
- 折叠
摘要
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 Ult-rasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients.关键词
甲状腺癌/乳头状/超声检查/pN分期/预测/淋巴结Key words
Thyroid Cancer/Papillary/Ultrasonography/pN staging/Predicting/Lymph nodes分类
临床医学引用本文复制引用
周洁丽,武林娟,张鹏天,彭艳侠,韩冬..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[J].肿瘤防治研究,2025,52(2):151-155,5.
