title: Image reporting and characterization system for ultrasound features of thyroid nodules: multicentric Korean retrospective study.
abstract: OBJECTIVE
  The objective of this retrospective study was to develop and validate a simple diagnostic prediction model by using ultrasound (US) features of thyroid nodules obtained from multicenter retrospective data.
  
  
  METHODS
  Patient data were collected from 20 different institutions and the data included 2000 thyroid nodules from 1796 patients. For developing a diagnostic prediction model to estimate the malignant risk of thyroid nodules using suspicious malignant US features, we developed a training model in a subset of 1402 nodules from 1260 patients. Several suspicious malignant US features were evaluated to create the prediction model using a scoring tool. The scores for such US features were estimated by calculating odds ratios, and the risk score of malignancy for each thyroid nodule was defined as the sum of these individual scores. Later, we verified the usefulness of developed scoring system by applying into the remaining 598 nodules from 536 patients.
  
  
  RESULTS
  Among 2000 tumors, 1268 were benign and 732 were malignant. In our multiple regression analysis models, the following US features were statistically significant for malignant nodules when using the training data set: hypoechogenicity, marked hypoechogenicity, non-parallel orientation, microlobulated or spiculated margin, ill-defined margins, and microcalcifications. The malignancy rate was 7.3% in thyroid nodules that did not have suspicious-malignant features on US. Area under the receiver operating characteristic (ROC) curve was 0.867, which shows that the US risk score help predict thyroid malignancy well. In the test data set, the malignancy rates were 6.2% in thyroid nodules without malignant features on US. Area under the ROC curve of the test set was 0.872 when using the prediction model.
  
  
  CONCLUSIONS
  The predictor model using suspicious malignant US features may be helpful in risk stratification of thyroid nodules.
authors: Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 120-752, Korea.
authors: Jin Young
authors: JY
authors: Kwak
authors: Inkyung
authors: I
authors: Jung
authors: Jung Hwan
authors: JH
authors: Baek
authors: Seon Mi
authors: SM
authors: Baek
authors: Nami
authors: N
authors: Choi
authors: Yoon Jung
authors: YJ
authors: Choi
authors: So Lyung
authors: SL
authors: Jung
authors: Eun-Kyung
authors: EK
authors: Kim
authors: Jeong-Ah
authors: JA
authors: Kim
authors: Ji-hoon
authors: JH
authors: Kim
authors: Kyu Sun
authors: KS
authors: Kim
authors: Jeong Hyun
authors: JH
authors: Lee
authors: Joon Hyung
authors: JH
authors: Lee
authors: Hee Jung
authors: HJ
authors: Moon
authors: Won-Jin
authors: WJ
authors: Moon
authors: Jeong Seon
authors: JS
authors: Park
authors: Ji Hwa
authors: JH
authors: Ryu
authors: Jung Hee
authors: JH
authors: Shin
authors: Eun Ju
authors: EJ
authors: Son
authors: Jin Yong
authors: JY
authors: Sung
authors: Dong Gyu
authors: DG
authors: Na
mesh_terms: D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007723:Korea; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D012044:Regression Analysis; D012189:Retrospective Studies; D012306:Risk; D016606:Thyroid Nodule / Q000000981:diagnostic imaging*; D014463:Ultrasonography
keywords: Thyroid; Thyroid cancer; Ultrasound