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Proceedings Paper

Differential diagnosis of thyroid nodules with virtual touch tissue imaging of ARFI elastography
Author(s): Tao Li; Pei Zhou; Mingyue Ding; Yongwei Mi; Yiyong Li; Ji Zhang
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Paper Abstract

The aim of this study was to evaluate the diagnostic performance of virtual touch tissue imaging (VTI) based on ARFI elastography technique for differentiating malignant from benign thyroid nodules. One hundred pathologically proven thyroid nodules (80 benign, 20 malignant) in 76 participants were recruited in this study. The likelihood of malignancy in the light of VTI features was scored into 6 levels by one experienced sonogist who was blinded to pathological results. In addition, the mean gray value within the thyroid nodule (mGVTN) derived from VTI image was calculated for quantitative analysis. Receiver-operating characteristic curve (ROC) analyses were performed to assess the diagnostic performance of VTI score and mGVTN. The frequency of malignant nodules (11/20) classified between VTI levels 4 to 6 was more than that of benign nodules (6/80) (p <0.001). The mGVTN of malignant nodules (45±23) was significantly lower than that of benign nodules (115±58) (p <0.001), where the range of mGVTN was from 0 to 255. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of VTI score were 55.0%, 92.5%, 85.0%, 64.7% and 89.2%, respectively. For mGVTN, those values were 70.0%, 90.0%, 86.0%, 63.6% and 92.3%, respectively. In conclusion, the VTI image seemed to be an effective tool in the differential diagnosis of thyroid nodules. The diagnosis performance of mGVTN was almost consistent with that of VTI score, which indicated that the mGVTN as a quantitative parameter might facilitate doctors diagnosing malignant thyroid nodules by VTI.

Paper Details

Date Published: 1 April 2016
PDF: 6 pages
Proc. SPIE 9790, Medical Imaging 2016: Ultrasonic Imaging and Tomography, 97901I (1 April 2016); doi: 10.1117/12.2216190
Show Author Affiliations
Tao Li, Wuhan General Hospital of Guangzhou Command (China)
Key Lab. of Image Processing and Intelligent Control, Ministry of Education (China)
Huazhong Univ. of Science and Technology (China)
Pei Zhou, Wuhan General Hospital of Guangzhou Command (China)
Mingyue Ding, Key Lab. of Image Processing and Intelligent Control, Ministry of Education (China)
Huazhong Univ. of Science and Technology (China)
Yongwei Mi, Wuhan General Hospital of Guangzhou Command (China)
Yiyong Li, Wuhan General Hospital of Guangzhou Command (China)
Ji Zhang, Wuhan General Hospital of Guangzhou Command (China)


Published in SPIE Proceedings Vol. 9790:
Medical Imaging 2016: Ultrasonic Imaging and Tomography
Neb Duric; Brecht Heyde, Editor(s)

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