Yi Zhu1, Qiang Sang2, Shijun Jia3, Ying Wang4, Timothy Deyer5,6. 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. 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. 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.
Abstract
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 IV/V/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.
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 IV/V/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.
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Authors: Eoin F Cleere; Matthew G Davey; Shane O'Neill; Mel Corbett; John P O'Donnell; Sean Hacking; Ivan J Keogh; Aoife J Lowery; Michael J Kerin Journal: Diagnostics (Basel) Date: 2022-03-24