Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images

Abstract

The objective of this study is to evaluate the diagnostic value of combination of artificial neural networks (ANN) and support vector machine (SVM)-based CAD systems in differentiating malignant from benign thyroid nodes with gray-scale ultrasound images. Two morphological and 65 texture features extracted from regions of interest in 610 2D-ultrasound thyroid node images from 543 patients (207 malignant, 403 benign) were used to develop the ANN and SVM models. Tenfold cross validation evaluated their performance; the best models showed accuracy of 99% for ANN and 100% for SVM. From 50 thyroid node ultrasound images from 45 prospectively enrolled patients, the ANN model showed sensitivity, specificity, positive and negative predictive values, Youden index, and accuracy of 88.24, 90.91, 83.33, 93.75, 79.14, and 90.00%, respectively, the SVM model 76.47, 90.91, 81.25, 88.24, 67.38, and 86.00%, respectively, and in combination 100.00, 87.88, 80.95, 100.00, 87.88, and 92.00%, respectively. Both ANN and SVM had high value in classifying thyroid nodes. In combination, the sensitivity increased but specificity decreased. This combination might provide a second opinion for radiologists dealing with difficult to diagnose thyroid node ultrasound images.

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

“…The quality of the included studies is summarized in online supplementary Table 2. The risk of bias from patient selection was judged to be high or unclear in 13 of the included studies: 4 studies limited the nodule size within a certain scope [16,17,21,25]; 5 studies excluded difficult-to-diagnose nodules [15,[25][26][27]31]; and 4 studies were unclear about whether there were selected co-horts and inappropriate exclusions [14,19,23,29]. The risk of bias from the reference standard was considered to be unclear in 2 of the included studies [14,23].…”

“…The flow diagram of the literature search is shown in Figure 1. Nineteen studies with 4,781 nodules used in external validation sets were included in the study, including 6 studies on classic machine learning-based CAD sys-DOI: 10.1159/000504390 tems [14][15][16][17][18][19] and 13 studies on deep learning-based CAD systems [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The general characteristics of the included studies are shown in Table 1, and the detailed characteristics are demonstrated in online supplementary Table 1 (see www.karger.com/doi/10.1159/000504390 for all online suppl.…”

“…There were 6 studies which investigated the performance of classic machine learning-based CAD systems [14][15][16][17][18][19]. The CAD systems in 4 studies were developed according to similar parameters, such as shape, margin, composition, echogenicity, internal composition, microcalcification, and peripheral halo [15,[17][18][19].…”

Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

“…Their radiomics score model had an AUC of 0.921 for predicting malignancy, showing better performance than experienced and junior radiologists referring to the 2017 Thyroid Imaging, Reporting, and Data System scoring criteria [36]. Another study by Yu et al [17] included 610 thyroid nodules (403 benign and 207 malignant). Texture features were extracted from each nodule and were used to train ANN and SVM-based classifier models to predict malignancy.…”

“…Machine learning is a collective term comprising multiple computational methods and models that extract meaningful features from medical images, and it has been increasingly applied in the field of radiology [12,13]. Several classifier models using various machine learning algorithms have also been applied to thyroid US imaging [14][15][16][17]. However, previous studies using classic radiologic lexicons as input variables for several classifier models showed contradictory diagnostic performance in differentiating benign and malignant thyroid nodules compared to experienced radiologists [14,15].…”