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

作　　者：周洁丽 武林娟 张鹏天 彭艳侠 韩冬 ZHOU Jieli;WU Linjuan;ZHANG Pengtian;Peng Yanxia;HAN Dong(Department of Ultrasound,The First Affiliated Hospital of Air Force Medical University,Xi’an 710032,China;Department of Ultrasound,Xi’an Fengcheng Hospital,Xi’an 710018,China;Department of Medical Imaging,Affiliated Hospital of Shaanxi University of Chinese Medicine,Xianyang 712000,China;Department of Ultrasound,Northwest Women’s and Children’s Hospital,Xi’an 710061,China)

机构地区：[1]空军军医大学第一附属医院超声科,西安710032 [2]西安市凤城医院超声科,西安710018 [3]陕西中医药大学附属医院医学影像科,咸阳712000 [4]西北妇女儿童医院超声科,西安710061

出　　处：《肿瘤防治研究》2025年第2期151-155,共5页Cancer Research on Prevention and Treatment

摘　　要：目的基于超声影像组学及深度神经网络(DNN)预测甲状腺乳头状癌(PTC)pN分期的准确性。方法回顾性收集经病理确诊的PTC患者375例(训练集261例,测试集114例)。将无颈部淋巴结转移定义为pN0,中央区淋巴结转移定义为pN1a,颈侧区淋巴结转移定义为pN1b。由超声科医师手动分割PTC的感兴趣区(ROI)并提取1899个影像组学特征。采用最小绝对收缩与选择算子(LASSO)对上述影像组学特征进行降维。基于H2O深度学习平台在训练集构建预测PTC pN分期的DNN模型,并在测试集验证最优模型准确性。结果pN0期患者153例,pN1a期131例,pN1b期91例。每个PTC的影像组学特征经LASSO回归筛选出15个影像组学特征。基于该15个影像组学特征构建的最优DNN模型在训练集及测试集的准确性分别为85.82%及81.57%。结论PTC的超声影像组学预测pN分期的准确性较高,有为患者自动化N分期的潜力。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.

关 键 词：甲状腺癌 乳头状 超声检查 pN分期 预测 淋巴结 

分 类 号：R445.1[医药卫生—影像医学与核医学] R736.1[医药卫生—诊断学]