Li Ying-Ying, Sun Wen-Xuan, Liao Xian-Dong, Zhang Ming-Bo, Xie Fang, Chen Dong-Hao, Zhang Yan, Luo Yu-Kun
Department of Ultrasound,the First Medical Center of PLA General Hospital,Beijing 100853,China.
School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2021 Dec 30;43(6):911-916. doi: 10.3881/j.issn.1000-503X.13823.
Objective To establish an artificial intelligence model based on B-mode thyroid ultrasound images to predict central compartment lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods We retrieved the clinical manifestations and ultrasound images of the tumors in 309 patients with surgical histologically confirmed PTC and treated in the First Medical Center of PLA General Hospital from January to December in 2018.The datasets were split into the training set and the test set.We established a deep learning-based computer-aided model for the diagnosis of CLNM in patients with PTC and then evaluated the diagnosis performance of this model with the test set. Result The accuracy,sensitivity,specificity,and area under receiver operating characteristic curve of our model for predicting CLNM were 80%,76%,83%,and 0.794,respectively. Conclusion Deep learning-based radiomics can be applied in predicting CLNM in patients with PTC and provide a basis for therapeutic regimen selection in clinical practice.
目的 建立基于B超甲状腺图像的人工智能模型,以预测甲状腺乳头状癌(PTC)患者的中央区淋巴结转移(CLNM)。方法 回顾性分析2018年1月至12月在解放军总医院第一医学中心接受手术治疗且术后病理确诊为PTC的309例患者的临床表现及肿瘤超声图像。将数据集分为训练集和测试集。建立基于深度学习的PTC患者CLNM诊断计算机辅助模型,并用测试集评估该模型的诊断性能。结果 该模型预测CLNM的准确率、灵敏度、特异度及受试者工作特征曲线下面积分别为80%、76%、83%和0.794。结论 基于深度学习的影像组学可用于预测PTC患者的CLNM,为临床治疗方案的选择提供依据。