Woo Jung Choi1, Jeong Seon Park2, Kwang Gi Kim3, Soo-Yeon Kim4, Hye Ryoung Koo5, Young-Jun Lee5. 1. Department of Radiology, Hanyang University Hospital, Seoul, South Korea; Department of Radiology, University of Ulsan, Asan Medical Center, Seoul, South Korea. 2. Department of Radiology, Hanyang University Hospital, Seoul, South Korea. Electronic address: jsp@hanyang.ac.kr. 3. Department of Biomedical Engineering Branch, National Cancer Center, Gyeonggi-do, South Korea. 4. Department of Radiology, Hanyang University Guri Hospital, Gyeonggi-do, South Korea. 5. Department of Radiology, Hanyang University Hospital, Seoul, South Korea.
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 (Az) 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 Az 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.
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 (Az) 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 Az 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.
Authors: Eoin F Cleere; Matthew G Davey; Shane O'Neill; Mel Corbett; John P O'Donnell; Sean Hacking; Ivan J Keogh; Aoife J Lowery; Michael J Kerin Journal: Diagnostics (Basel) Date: 2022-03-24
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