机构：[1]Departments of Ultrasound, Sichuan Cancer Hospital & Institute, Cancer Hospital Affiliated to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China四川省人民医院四川省肿瘤医院[2]College of Information Science & Technology, Chengdu University of Technology, Chengdu 610059, China[3]Department of Pathology, Sichuan Cancer Hospital & Institute, Cancer Hospital Affiliated to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China四川省人民医院四川省肿瘤医院[4]Department of Mathematics, University of Oklahoma, Oklahoma, OK, USA[5]East River Medical Imaging, New York, NY, USA[6]Department of Radiology, Cornell Medicine, New York Hospital, New York, NY, USA
Background: Ultrasound (US) is the most commonly used radiologic modality to identify and characterize thyroid nodules. Many nodules subsequently undergo fine needle aspiration to further characterize the nodule and determine appropriate treatment. The fine needle aspirate is most commonly classified using the Bethesda System for Reporting Thyroid Cytology (TBSRTC). It can sometimes be difficult to differentiate Bethesda class III lesions (atypia of undetermined significance/follicular lesion of undetermined significance) from Bethesda class IV, V and VI (malignant nodules). However, differentiation is important as clinical management differs between the two groups. The purpose of this study was to introduce machine learning methods to help radiologists differentiate Bethesda class III from Bethesda class VI, V and VI lesions. Methods: The authors collected 467 thyroid nodules with cytopathology results. US features were summarized using the 2017 ACR (American College of Radiology) Thyroid Imaging Reporting And Data System (TIRADS). Machine learning models [logistic regression, gradient boost, support vector machine (SVM), random forest and deep neural networks (DNN)] were created to classify Bethesda class III vs class IV/V/VI. Results: DNN outperformed other machine learning classifiers and obtained the highest accuracy and specificity to classify thyroid nodules as either Bethesda III or IVN/VI nodules using multiple US features. Conclusions: Machine learning/deep learning approaches could help differentiate Bethesda III nodules from IV/V/VI using US features which may benefit treatment decisions.
第一作者机构：[1]Departments of Ultrasound, Sichuan Cancer Hospital & Institute, Cancer Hospital Affiliated to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China
通讯作者：
通讯机构：[2]College of Information Science & Technology, Chengdu University of Technology, Chengdu 610059, China[*1]College of Information Science & Technology, Chengdu University of Technology, Chengdu 610059, China
推荐引用方式(GB/T 7714)：
Zhu Yi,Sang Qiang,Jia Shijun,et al.Deep neural networks could differentiate Bethesda class III versus class IV/V/VI[J].ANNALS OF TRANSLATIONAL MEDICINE.2019,7(11):doi:10.21037/atm.2018.07.03.
APA：
Zhu, Yi,Sang, Qiang,Jia, Shijun,Wang, Ying&Deyer, Timothy.(2019).Deep neural networks could differentiate Bethesda class III versus class IV/V/VI.ANNALS OF TRANSLATIONAL MEDICINE,7,(11)
MLA：
Zhu, Yi,et al."Deep neural networks could differentiate Bethesda class III versus class IV/V/VI".ANNALS OF TRANSLATIONAL MEDICINE 7..11(2019)
