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http://dx.doi.org/10.3348/kjr.2013.14.1.110

Image Reporting and Characterization System for Ultrasound Features of Thyroid Nodules: Multicentric Korean Retrospective Study  

Kwak, Jin Young (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine)
Jung, Inkyung (Department of Biostatistics, Yonsei University College of Medicine)
Baek, Jung Hwan (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
Baek, Seon Mi (Department of Radiology, Haeundae Healings Hospital)
Choi, Nami (Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine)
Choi, Yoon Jung (Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University)
Jung, So Lyung (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
Kim, Eun-Kyung (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine)
Kim, Jeong-Ah (Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine)
Kim, Ji-Hoon (Department of Radiology, Seoul National University Hospital)
Kim, Kyu Sun (Department of Radiology, Thyroid Center, Daerim St. Mary's Hospital)
Lee, Jeong Hyun (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
Lee, Joon Hyung (Department of Radiology, Dong-A University Medical Center)
Moon, Hee Jung (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine)
Moon, Won-Jin (Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University)
Park, Jeong Seon (Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine)
Ryu, Ji Hwa (Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine)
Shin, Jung Hee (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine)
Son, Eun Ju (Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine)
Sung, Jin Yong (Department of Radiology, Thyroid Center, Daerim St. Mary's Hospital)
Na, Dong Gyu (Department of Radiology, Human Medical Imaging and Intervention Center)
Korean Society of Thyroid Radiology (KSThR) (Korean Society of Thyroid Radiology (KSThR))
Korean Society of Radiology (Korean Society of Radiology)
Publication Information
Korean Journal of Radiology / v.14, no.1, 2013 , pp. 110-117 More about this Journal
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. Materials and 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. Conclusion: The predictor model using suspicious malignant US features may be helpful in risk stratification of thyroid nodules.
Keywords
Thyroid; Ultrasound; Thyroid cancer;
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