title: Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.
abstract: In this study, the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules was objectively quantified to facilitate clinical diagnosis and management. A total of 157 regions-of-interest thyroid ultrasound image was recruited in the study. The sonographic system used was the GE LOGIQ 700), (General Electric Healthcare, Chalfant St. Giles, UK). The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, ReliefF was used as a feature selector. Among the various textural features, the sum average value derived from co-occurrence matrix can well reflect echogenicity and can effectively differentiate between follicles and fibrosis base thyroid nodules. Fibrosis shows lowest echogenicity and lowest difference sum average value. Enlarged follicles show highest echogenicity and difference sum average values. Papillary cancer or follicular tumors show the difference sum average values and echogenicity between. The rule of thumb for the echogenicity is that the more follicles are mixed in, the higher the echo of the follicular tumor and papillary cancer will be and vice versa for fibrosis mixed. Areas with intermediate and lower echo should address the possibility of follicular or papillary neoplasm mixed with either follicles or fibrosis. These areas provide more cellular information for ultrasound guided aspiration
authors.affiliation: Department of Radiology, Buddhist Dalin Tzu Chi General Hospital, Chia-Yi, Taiwan.
authors.forename: Shao-Jer
authors.initials: SJ
authors.lastname: Chen
authors.forename: Sung-Nien
authors.initials: SN
authors.lastname: Yu
authors.forename: Jeh-En
authors.initials: JE
authors.lastname: Tzeng
authors.forename: Yen-Ting
authors.initials: YT
authors.lastname: Chen
authors.forename: Ku-Yaw
authors.initials: KY
authors.lastname: Chang
authors.forename: Kuo-Sheng
authors.initials: KS
authors.lastname: Cheng
authors.forename: Fu-Tsung
authors.initials: FT
authors.lastname: Hsiao
authors.forename: Chang-Kuo
authors.initials: CK
authors.lastname: Wei
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002291:Carcinoma, Papillary / Q000000981:diagnostic imaging / Q000473:pathology; D018265:Carcinoma, Papillary, Follicular / Q000000981:diagnostic imaging / Q000473:pathology; D003937:Diagnosis, Differential; D005260:Female; D005355:Fibrosis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D013961:Thyroid Gland / Q000000981:diagnostic imaging* / Q000473:pathology; D013964:Thyroid Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D016606:Thyroid Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014463:Ultrasonography