Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography

Highlights • Computerized analysis of calcification feature in thyroid US was feasible. • Analyzed calcification features enhanced diagnostic performance in the differential diagnosis of malignant thyroid nodules. • The calcification within malignant thyroid nodules showed different characteristics...

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Published in	European journal of radiology Vol. 84; no. 10; pp. 1949 - 1953
Main Authors	Choi, Woo Jung, Park, Jeong Seon, Kim, Kwang Gi, Kim, Soo-Yeon, Koo, Hye Ryoung, Lee, Young-Jun
Format	Journal Article
Language	English
Published	Ireland Elsevier Ireland Ltd 01.10.2015
Subjects	Adult Aged Aged, 80 and over Area Under Curve Calcinosis - diagnostic imaging Carcinoma, Papillary - diagnostic imaging Computerized analysis Diagnosis, Differential Feasibility Studies Female Humans Hyperplasia Image Processing, Computer-Assisted - methods Male Middle Aged Neural network Neural Networks (Computer) Radiology Retrospective Studies ROC Curve Sensitivity and Specificity Thyroid Thyroid Neoplasms - diagnostic imaging Thyroid Nodule - diagnostic imaging Thyroiditis - diagnostic imaging Ultrasonography Computerized analysis Neural network Ultrasonography Thyroid
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Abstract	Highlights • Computerized analysis of calcification feature in thyroid US was feasible. • Analyzed calcification features enhanced diagnostic performance in the differential diagnosis of malignant thyroid nodules. • The calcification within malignant thyroid nodules showed different characteristics compared with calcifications of benign thyroid nodules.
AbstractList	Highlights • Computerized analysis of calcification feature in thyroid US was feasible. • Analyzed calcification features enhanced diagnostic performance in the differential diagnosis of malignant thyroid nodules. • The calcification within malignant thyroid nodules showed different characteristics compared with calcifications of benign thyroid nodules. The purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine the ability to differentiate between malignant and benign thyroid nodules. We designed and implemented a computerized analysis scheme to quantitatively analyze the US features of the calcified thyroid nodules from 99 pathologically determined calcified thyroid nodules. Univariate analysis was used to identify features that were significantly associated with tumor malignancy, and neural-network analysis was performed to classify tumors as benign or malignant. The diagnostic performance of the neural network was evaluated using receiver operating characteristic (ROC) analysis, where in the area under the ROC curve (Az) summarized the diagnostic performance of specific calcification features. The performance values for each calcification feature were as follows: ratio of calcification distance=0.80, number of calcifications=0.68, skewness=0.82, and maximum intensity=0.75. The combined value of the four features was 0.84.With a threshold of 0.64, the Az value of calcification features was 0.83 with a sensitivity of 83.0%, specificity of 82.4%, and accuracy of 82.8%. These results support the clinical feasibility of using computerized analysis of calcification features from thyroid US for differentiating between malignant and benign nodules. •Computerized analysis of calcification feature in thyroid US was feasible.•Analyzed calcification features enhanced diagnostic performance in the differential diagnosis of malignant thyroid nodules.•The calcification within malignant thyroid nodules showed different characteristics compared with calcifications of benign thyroid nodules. The purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine the ability to differentiate between malignant and benign thyroid nodules. We designed and implemented a computerized analysis scheme to quantitatively analyze the US features of the calcified thyroid nodules from 99 pathologically determined calcified thyroid nodules. Univariate analysis was used to identify features that were significantly associated with tumor malignancy, and neural-network analysis was performed to classify tumors as benign or malignant. The diagnostic performance of the neural network was evaluated using receiver operating characteristic (ROC) analysis, where in the area under the ROC curve (Az) summarized the diagnostic performance of specific calcification features. The performance values for each calcification feature were as follows: ratio of calcification distance=0.80, number of calcifications=0.68, skewness=0.82, and maximum intensity=0.75. The combined value of the four features was 0.84.With a threshold of 0.64, the Az value of calcification features was 0.83 with a sensitivity of 83.0%, specificity of 82.4%, and accuracy of 82.8%. These results support the clinical feasibility of using computerized analysis of calcification features from thyroid US for differentiating between malignant and benign nodules. OBJECTIVEThe purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine the ability to differentiate between malignant and benign thyroid nodules.METHODSWe designed and implemented a computerized analysis scheme to quantitatively analyze the US features of the calcified thyroid nodules from 99 pathologically determined calcified thyroid nodules. Univariate analysis was used to identify features that were significantly associated with tumor malignancy, and neural-network analysis was performed to classify tumors as benign or malignant. The diagnostic performance of the neural network was evaluated using receiver operating characteristic (ROC) analysis, where in the area under the ROC curve (Az) summarized the diagnostic performance of specific calcification features.RESULTSThe performance values for each calcification feature were as follows: ratio of calcification distance=0.80, number of calcifications=0.68, skewness=0.82, and maximum intensity=0.75. The combined value of the four features was 0.84.With a threshold of 0.64, the Az value of calcification features was 0.83 with a sensitivity of 83.0%, specificity of 82.4%, and accuracy of 82.8%.CONCLUSIONSThese results support the clinical feasibility of using computerized analysis of calcification features from thyroid US for differentiating between malignant and benign nodules.
Author	Kim, Soo-Yeon Choi, Woo Jung Kim, Kwang Gi Koo, Hye Ryoung Park, Jeong Seon Lee, Young-Jun
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Snippet	Highlights • Computerized analysis of calcification feature in thyroid US was feasible. • Analyzed calcification features enhanced diagnostic performance in... •Computerized analysis of calcification feature in thyroid US was feasible.•Analyzed calcification features enhanced diagnostic performance in the differential... The purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine the... OBJECTIVEThe purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine...
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SubjectTerms	Adult Aged Aged, 80 and over Area Under Curve Calcinosis - diagnostic imaging Carcinoma, Papillary - diagnostic imaging Computerized analysis Diagnosis, Differential Feasibility Studies Female Humans Hyperplasia Image Processing, Computer-Assisted - methods Male Middle Aged Neural network Neural Networks (Computer) Radiology Retrospective Studies ROC Curve Sensitivity and Specificity Thyroid Thyroid Neoplasms - diagnostic imaging Thyroid Nodule - diagnostic imaging Thyroiditis - diagnostic imaging Ultrasonography
Title	Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography
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