title: Web-Based Malignancy Risk Estimation for Thyroid Nodules Using Ultrasonography Characteristics: Development and Validation of a Predictive Model.
abstract: BACKGROUND
  To establish a practical and simplified method for analyzing thyroid nodules in a clinical setting, the development of a new practical prediction model was required. This study aimed to construct and validate a simple and reliable web-based predictive model using the ultrasonography characteristics of thyroid nodules to stratify the risk of malignancy.
  
  
  METHODS
  To analyze ultrasonography images, radiologists were asked to assess thyroid nodules according to the following criteria: internal content, echogenicity of the solid portion, shape, margin, and calcifications. Multivariate logistic regression was performed to predict whether nodules were diagnosed as malignant or benign. The developmental data set included 849 nodules (January-June 2003). The validation set included different data (n = 453, June 2008-February 2009).
  
  
  RESULTS
  Ultrasonography features, including solid content, taller-than-wide shape, spiculated margin, ill-defined margin, hypoechogenicity, marked hypoechogenicity, microcalicifications, and rim calcifications, were selected as predictors for malignant nodules in the development set. A 14-point risk scoring system was developed. Malignancy risk ranged from 3.8% to 97.4%, and the risk of malignancy was positively associated with increases in risk scores. The areas under the receiver operating characteristic curve of the development and validation sets were 0.903 and 0.897, respectively.
  
  
  CONCLUSIONS
  A simple and reliable web-based predictive model was designed using ultrasonography characteristics to stratify thyroid nodules according to the probability of malignancy.
authors.affiliation: 1 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Seoul, Korea.
authors.forename: Young Jun
authors.initials: YJ
authors.lastname: Choi
authors.affiliation: 1 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Seoul, Korea.
authors.forename: Jung Hwan
authors.initials: JH
authors.lastname: Baek
authors.affiliation: 2 Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
authors.forename: Seung Hee
authors.initials: SH
authors.lastname: Baek
authors.affiliation: 1 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Seoul, Korea.
authors.forename: Woo Hyun
authors.initials: WH
authors.lastname: Shim
authors.affiliation: 3 Department of Otolaryngology-Head and Neck Surgery, Kosin University College of Medicine , Busan, Korea.
authors.forename: Kang Dae
authors.initials: KD
authors.lastname: Lee
authors.affiliation: 3 Department of Otolaryngology-Head and Neck Surgery, Kosin University College of Medicine , Busan, Korea.
authors.forename: Hyoung Shin
authors.initials: HS
authors.lastname: Lee
authors.affiliation: 4 Department of Endocrinology and Metabolism, University of Ulsan College of Medicine , Asan Medical Center, Seoul, Korea.
authors.forename: Young Kee
authors.initials: YK
authors.lastname: Shong
authors.affiliation: 5 Department of Radiology, Ajou University School of Medicine , Suwon, Korea.
authors.forename: Eun Ju
authors.initials: EJ
authors.lastname: Ha
authors.affiliation: 1 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Seoul, Korea.
authors.forename: Jeong Hyun
authors.initials: JH
authors.lastname: Lee
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002114:Calcinosis / Q000000981:diagnostic imaging*; D002277:Carcinoma / Q000000981:diagnostic imaging* / Q000473:pathology; D002648:Child; D015331:Cohort Studies; D005260:Female; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D012189:Retrospective Studies; D018570:Risk Assessment; D013964:Thyroid Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D016606:Thyroid Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D047368:Tumor Burden; D014463:Ultrasonography; D055815:Young Adult