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基于甲状腺超声图像建立甲状腺乳头状癌中央区淋巴结转移人工智能诊断模型
A Thyroid Ultrasound Image-based Artificial Intelligence Model for Diagnosis of Central Compartment Lymph Node Metastasis in Papillary Thyroid Carcinoma

查看参考文献28篇

李盈盈 1   孙文轩 2   廖献东 2   张明博 1   谢芳 1   陈东浩 2   张艳 1   罗渝昆 1 *  
文摘 目的基于甲状腺超声图像建立甲状腺乳头状癌中央区淋巴结转移人工智能诊断模型。方法回顾性分析2018年1至12月于中国人民解放军总医院第一医学中心行甲状腺切除及颈部中央区淋巴结清扫的309例甲状腺乳头状癌(PTC)患者的临床资料及超声图像,病理结果为金标准。所有病例被分为训练集(265例)、测试集(44例) 。基于深度学习方法建立甲状腺超声图像预测PTC患者中央区淋巴结转移的计算机辅助诊断系统。在测试集中评估该系统的诊断性能。结果在测试集中,本模型预测PTC患者中央区淋巴结转移的准确性、敏感性、特异性和受试者工作特征曲线下面积可达80%、76%、83%、0.794。结论基于深度学习的人工智能诊断模型可用于诊断甲状腺乳头状癌患者中央区淋巴结转移,可为临床选择治疗方案提供依据。
其他语种文摘 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.
来源 中国医学科学院学报 ,2021,43(6):911-916 【核心库】
DOI 10.3881/j.issn.1000-503X.13823
关键词 甲状腺乳头状癌 ; 中央区淋巴结转移 ; 超声 ; 人工智能
地址

1. 中国人民解放军总医院第一医学中心超声科, 北京, 100853  

2. 北京邮电大学人工智能学院, 北京, 100876

语种 中文
文献类型 研究性论文
ISSN 1000-503X
学科 临床医学
文献收藏号 CSCD:7126854

参考文献 共 28 共2页

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引证文献 2 篇

1 孙斌 超声新技术预测甲状腺乳头状癌颈部淋巴结转移的研究现状与展望 中国医学科学院学报,2023,45(4):672-676
CSCD被引 1 次

2 刘杰 甲状腺乳头状癌超声图像表现在预测颈部VI区淋巴结转移危险度的临床价值 中国耳鼻咽喉头颈外科,2024,31(7):470-473
CSCD被引 0 次

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