A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography

Song, Gesheng; Xue, Fuzhong; Zhang, Chengqi

doi:10.7863/ultra.15.14.10045

Cited by 39 publications

(27 citation statements)

References 31 publications

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“…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].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…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 risk of bias from flow and timing was considered to be high or unclear in 7 of the included studies, and these studies adopted pathological examination, fine needle aspiration, and ultrasound as reference standards for diagnosing benign nodules [19,22,25,27,28,30,31].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…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.…”

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

“…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].…”

Section: Diagnostic Performance Of Classic Machine Learning-based Cadmentioning

confidence: 99%

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

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning-and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

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Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

Section: Diagnostic Performance Of Classic Machine Learning-based Cadmentioning

confidence: 99%

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

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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“…15 Previous studies have reported that histograms and texture analyses of US are useful for differentiating benign and malignant thyroid nodules. [16][17][18][19][20][21] To our knowledge, there have been no published studies aimed at identifying the presence of BRAF mutation using radiomics features of US.…”

mentioning

confidence: 99%

Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results

Kwon

Shin

Park

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: It is not known how radiomics using ultrasound images contribute to the detection of BRAF mutation. This study aimed to evaluate whether a radiomics study of gray-scale ultrasound can predict the presence or absence of B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutation in papillary thyroid cancer. MATERIALS AND METHODS: The study retrospectively included 96 thyroid nodules that were surgically confirmed papillary thyroid cancers between January 2012 and June 2013. BRAF mutation was positive in 48 nodules and negative in 48 nodules. For analysis, ROIs from the nodules were demarcated manually on both longitudinal and transverse sonographic images. We extracted a total of 86 radiomics features derived from histogram parameters, gray-level co-occurrence matrix, intensity size zone matrix, and shape features. These features were used to build 3 different classifier models, including logistic regression, support vector machine, and random forest using 5-fold cross-validation. The performance including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve, of the different models was evaluated. RESULTS: The incidence of high-suspicion nodules diagnosed on ultrasound was higher in the BRAF mutation-positive group than in the mutation-negative group (P ¼ .004). The radiomics approach demonstrated that all classification models showed moderate performance for predicting the presence of BRAF mutation in papillary thyroid cancers with an area under the curve value of 0.651, accuracy of 64.3%, sensitivity of 66.8%, and specificity of 61.8%, on average, for the 3 models. CONCLUSIONS: Radiomics study using thyroid sonography is limited in predicting the BRAF mutation status of papillary thyroid carcinoma. Further studies will be needed to validate our results using various diagnostic methods. ABBREVIATIONS: AUC ¼ area under the receiver operating characteristic curve; GLCM ¼ gray-level co-occurrence matrix; ISZM ¼ intensity size zone matrix; K-TIRADS ¼ Korean Thyroid Imaging Reporting and Data System; mRMR ¼ minimum redundancy maximum relevance; PTC ¼ papillary thyroid carcinoma; US ¼ ultrasound

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Thyroid Ultrasound Physics

Levine¹

2017

Thyroid and Parathyroid Ultrasound and Ultrasound-Guided FNA

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A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography

Abstract: Gray-level co-occurrence matrix texture features extracted from sonograms of thyroid nodules coupled with a logistic model are useful for differentiating between benign and malignant thyroid nodules.

Cited by 39 publications

References 31 publications

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

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

Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results

Thyroid Ultrasound Physics

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