Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography. Issue 10 (October 2015)

• Record Type: Journal Article
• Authors: Choi, Woo Jung; Park, Jeong Seon; Kim, Kwang Gi; Kim, Soo-Yeon; Koo, Hye Ryoung; Lee, Young-Jun
• Is Part Of: European journal of radiology. Volume 84:Issue 10(2015)
• Page Start: 1949
• Page End: 1953
• Publication Date: 2015-10
• Subjects: Thyroid -- Ultrasonography -- Computerized analysis -- Neural network; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2015.06.021
• Languages: English
• ISSNs: 0720-048X

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. Abstract: Objective: 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. Methods: 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 ( A z ) summarized the diagnostic performance of specific calcification features. Results: 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 A z value of calcification features was 0.83 with a sensitivity of 83.0%, specificity of 82.4%, and accuracy of 82.8%. Conclusions: These results support the clinical feasibility of using computerized analysis of calcification features from thyroid US for differentiating between malignant and benign nodules.