Evaluation of wall configuration ultrasonogrophicin diagnosis of thyroid small nodules using binary logistic regression.

Qiaomei FU; Pengxi WU; Email: Wupx@wuxiphcom; Yan Ding

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Evaluation of wall configuration ultrasonogrophicin diagnosis of thyroid small nodules using binary logistic regression.

Author: Qiaomei FU ¹ ; Pengxi WU ² ; Email: WUPX@WUXIPH.COM. ; Yan DING
Author Information

1. Department of Ultrasound Medicine, the children's Hospital of Wuxi, Wuxi 214023, China.
2. Department of Ultrasound Medicine, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi 214023, China
Publication Type:Journal Article
MeSH: Biopsy, Fine-Needle; Calcinosis; Diagnosis, Differential; Humans; Logistic Models; Thyroid Nodule; diagnostic imaging; pathology; Ultrasonography
From: Chinese Journal of Otorhinolaryngology Head and Neck Surgery 2015;50(10):818-822
CountryChina
Language:Chinese
Abstract:
OBJECTIVETo screen out the sonogram features for the differential diagnosis of benign and malignant thyroid small nodules (≤ 1.0 cm) by Logistics regression analysis, to establish the binary Logistic regression model of sonogram features as independent variable and investigate the value of wall configuration of ultrasonogrophic nodules in the differential diagnosis of benign and malignant thyroid small nodules.
METHODSA total of 208 thyroid nodules ≤ 1.0 cm in diameter in 190 patients were evaluated. With postoperative pathological examination or fine needle aspiration biopsy, 106 nodules were confirmed as benign and 102 as malignant. Ultrasonic features of thyroid nodules were evaluated for the differential diagnosis of benign and malignant small thyroid nodules based on pathological diagnosis as a gold standard, a Logistic model was obtained, and the odds ratio of variables were compared. The margin of thyroid nodule was divided into regular or irregular margin, and the latter was divided further into four subtypes: strip, triangular, antler and papillary. The border was divided into clear, fuzzy or both. The periphery was divided into those with normal and abnormal echo;. The calcification included no calcification, microcalcification and non-microcalcification.
RESULTSFour statistically significant features were obtained finally by Logistics regression analysis, including margin, border, periphery and calcification. A formula was constructed by two-variables logistic regression analysis and probability of malignancy = 1/(1 + e - z), in which z = 5.026 × margin + 4.218 × border + 4.024 × periphery + 3.892 × calcification - 15.247. The odds ratio of margin was higher than the other independent variables.
CONCLUSIONSLogistics regression analysis indicates that the calcification, border, periphery, and especially margin of thyroid nodules are significant features for differentiating benign and malignant thyroid nodules. The margin score was more intuitionistic for the differentialtion of benign and malignant thyroid nodules.