title: Quantitative analysis of dynamic power Doppler sonograms for patients with thyroid nodules.
abstract: To clarify and determine whether power Doppler sonograms are useful for the detection of malignant thyroid nodules, a computerized quantification method was used to evaluate the vascular density of a thyroid nodule in a prospective setting. Sonographic power Doppler images were collected in consecutive frames (45 frames of images), and a proprietary program (AmCAD-UV) was implemented using methods proposed in this article automatically calculated a quantified power Doppler vascular index (PDVI). The minimum PDVI value (PDVImin) was suggested as a measure of the vascular density of the nodule. The vascular densities of the peripheral and central areas of the nodule, referred to as central PDVImin and Ring PDVImin, respectively, were also evaluated. For 238 tumors (79 malignant and 159 benign) from 208 patients, all of the proposed indices of benign lesions were significantly higher than those of the malignant lesions. The area under the receiver operating characteristic curve (AUC) reaches 71% with the PDVImin. When the vascular patterns were further classified into intra-nodular and peripheral vascularity types, no vascularity type was observed significantly more frequently in malignant nodules than in benign nodules. These proposed computerized vascular indices provide a quantification method to objectively evaluate thyroid nodules and have potential as predictors of thyroid malignancy. The conventional vascular characterizations of malign nodules, that is, more vessels are observed in malignant nodules than in benign nodules, are shown to be unreliable in our study. Instead, a higher value of the quantified power Doppler vascular density was observed in benign nodules.
authors.affiliation: Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Ming-Hsun
authors.initials: MH
authors.lastname: Wu
authors.forename: Chiung-Nien
authors.initials: CN
authors.lastname: Chen
authors.forename: Kuen-Yuan
authors.initials: KY
authors.lastname: Chen
authors.forename: Ming-Chih
authors.initials: MC
authors.lastname: Ho
authors.forename: Hao-Chih
authors.initials: HC
authors.lastname: Tai
authors.forename: Yuan-Chang
authors.initials: YC
authors.lastname: Chung
authors.forename: Chan-Peng
authors.initials: CP
authors.lastname: Lo
authors.forename: Argon
authors.initials: A
authors.lastname: Chen
authors.forename: King-Jen
authors.initials: KJ
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D013624:Taiwan / Q000453:epidemiology; D016606:Thyroid Nodule / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D018608:Ultrasonography, Doppler / Q000379:methods*
keywords: Computer-aided detection; Power Doppler; Thyroid cancer; Vascular index; Vascularity