Benign and malignant thyroid classification using computed tomography radiomics

Bang Jun Guo; Xiuxiu He; Tonghe Wang; Yang Lei; Walter J. Curran; Tian Liu; Long Jiang Zhang; Xiaofeng Yang

doi:10.1117/12.2549087

16 March 2020 Benign and malignant thyroid classification using computed tomography radiomics

Bang Jun Guo, Xiuxiu He, Tonghe Wang, Yang Lei, Walter J. Curran, Tian Liu, Long Jiang Zhang, Xiaofeng Yang

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131440 (2020) https://doi.org/10.1117/12.2549087
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Bang Jun Guo, Xiuxiu He, Tonghe Wang, Yang Lei, Walter J. Curran, Tian Liu, Long Jiang Zhang, and Xiaofeng Yang "Benign and malignant thyroid classification using computed tomography radiomics", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131440 (16 March 2020); https://doi.org/10.1117/12.2549087

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KEYWORDS

Computed tomography

Image segmentation

Cancer

Medical imaging

Magnetic resonance imaging

Machine learning

Feature extraction

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