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Proceedings Paper

Benign and malignant thyroid classification using computed tomography radiomics
Author(s): Bang Jun Guo; Xiuxiu He; Tonghe Wang; Yang Lei; Walter J. Curran; Tian Liu; Long Jiang Zhang; Xiaofeng Yang
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Paper Abstract

Thyroid cancer (TC) is a prevalent malignancy with a high predicated new case number and estimated death in 2019. Although four to seven percent of the adult population has a palpable thyroid nodule, however only one of twenty clinically identified TNs is malignant. Imaging modalities, including US, CT, and magnetic resonance (MRI), have been widely used for thyroid nodule evaluation, but the reliability is low. We propose a learning method for the classification of thyroid using thyroid non-enhanced thyroid computed tomography and radiomics study. Ninety-two patients with suspected or known to have abnormal thyroid nodules in their thyroid were enrolled. The thyroid on the non-enhanced thyroid CT was manually segmented. One hundred radiomic features of the thyroid were extracted. The most informative and nonredundant features were selected to train a Support Vector Machine (SVM) to differentiate benign thyroid and malignant thyroid (with malignant TNs). Analysis of the predictions showed that the reported method has accuracy 0.8185 ± 0.0366 and area under the receiver operating characteristic curve (AUC) 0.8376 ± 0.0343. This study shows that thyroidradiomic features derived from non-enhanced thyroid CT data can be used to classify benign vs. malignant thyroid. The radiomic features of thyroid from non-enhanced thyroid CT could be a useful tool for determining benign or malignant thyroid.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131440 (16 March 2020); doi: 10.1117/12.2549087
Show Author Affiliations
Bang Jun Guo, Emory Univ. (United States)
Southern Medical Univ. (China)
Nanjing Univ. (China)
Xiuxiu He, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Long Jiang Zhang, Southern Medical Univ. (China)
Nanjing Univ. (China)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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