title: Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography.
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.
authors.affiliation: Department of Radiology, Hanyang University Hospital, Seoul, South Korea; Department of Radiology, University of Ulsan, Asan Medical Center, Seoul, South Korea.
authors.forename: Woo Jung
authors.initials: WJ
authors.lastname: Choi
authors.affiliation: Department of Radiology, Hanyang University Hospital, Seoul, South Korea. Electronic address: jsp@hanyang.ac.kr.
authors.forename: Jeong Seon
authors.initials: JS
authors.lastname: Park
authors.affiliation: Department of Biomedical Engineering Branch, National Cancer Center, Gyeonggi-do, South Korea.
authors.forename: Kwang Gi
authors.initials: KG
authors.lastname: Kim
authors.affiliation: Department of Radiology, Hanyang University Guri Hospital, Gyeonggi-do, South Korea.
authors.forename: Soo-Yeon
authors.initials: SY
authors.lastname: Kim
authors.affiliation: Department of Radiology, Hanyang University Hospital, Seoul, South Korea.
authors.forename: Hye Ryoung
authors.initials: HR
authors.lastname: Koo
authors.affiliation: Department of Radiology, Hanyang University Hospital, Seoul, South Korea.
authors.forename: Young-Jun
authors.initials: YJ
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002114:Calcinosis / Q000000981:diagnostic imaging*; D002291:Carcinoma, Papillary / Q000000981:diagnostic imaging; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D006965:Hyperplasia; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000000981:diagnostic imaging*; D016606:Thyroid Nodule / Q000000981:diagnostic imaging*; D013966:Thyroiditis / Q000000981:diagnostic imaging; D014463:Ultrasonography
keywords: Computerized analysis; Neural network; Thyroid; Ultrasonography