Literature DB >> 25218450

Computerized quantification of ultrasonic heterogeneity in thyroid nodules.

Kuen-Yuan Chen1, Chiung-Nien Chen2, Ming-Hsun Wu2, Ming-Chih Ho2, Hao-Chih Tai2, Wen-Hong Kuo2, Wen-Chang Huang3, Yu-Hsin Wang4, Argon Chen5, King-Jen Chang6.   

Abstract

To test whether computerized quantification of ultrasonic heterogeneity can be of help in the diagnosis of thyroid malignancy, we evaluated ultrasonic heterogeneity with an objective and quantitative computerized method in a prospective setting. A total of 400 nodules including 271 benign thyroid nodules and 129 malignant thyroid nodules were evaluated. Patient clinical data were collected, and the grading of heterogeneity on conventional gray-scale ultrasound images was retrospectively reviewed by a thyroid specialist. Quantification of ultrasonic heterogeneity (heterogeneity index, HI) was performed by a proprietary program implemented with methods proposed in this article. HI values differed significantly between benign and malignant nodules, diagnosed by a combination of fine-needle aspiration and surgical pathology results (p < 0.001, area under the curve = 0.714). The ultrasonic heterogeneity of these samples, as assessed by an experienced clinician, could not significantly differentiate between benign and malignant thyroid nodules. However, nodules with marked ultrasonic heterogeneity had higher HI values than nodules with homogeneous nodules. These results indicate that the new computer-aided diagnosis method for evaluation of the ultrasonic heterogeneity of thyroid nodules is an objective and quantitative method that is correlated with conventional ultrasonic heterogeneity assessment, but can better aid in the diagnosis of thyroid malignancy.
Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computer-aided diagnosis; Heterogeneity index; Thyroid cancer; Thyroid nodule; Ultrasonic heterogeneity

Mesh:

Year:  2014        PMID: 25218450     DOI: 10.1016/j.ultrasmedbio.2014.06.009

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  9 in total

1.  Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.

Authors:  Qin Yu; Tao Jiang; Aiyun Zhou; Lili Zhang; Cheng Zhang; Pan Xu
Journal:  Eur Arch Otorhinolaryngol       Date:  2017-04-07       Impact factor: 2.503

2.  Software-Based Analysis of the Taller-Than-Wide Feature of High-Risk Thyroid Nodules.

Authors:  Ming-Hsun Wu; Kuen-Yuan Chen; Argon Chen; Chiung-Nien Chen
Journal:  Ann Surg Oncol       Date:  2021-01-03       Impact factor: 5.344

3.  Risk Stratification in Patients With Follicular Neoplasm on Cytology: Use of Quantitative Characteristics and Sonographic Patterns.

Authors:  Ming-Hsun Wu; Kuen-Yuan Chen; Min-Shu Hsieh; Argon Chen; Chiung-Nien Chen
Journal:  Front Endocrinol (Lausanne)       Date:  2021-04-30       Impact factor: 5.555

4.  Quantitative analysis of echogenicity for patients with thyroid nodules.

Authors:  Ming-Hsun Wu; Chiung-Nien Chen; Kuen-Yuan Chen; Ming-Chih Ho; Hao-Chih Tai; Yu-Hsin Wang; Argon Chen; King-Jen Chang
Journal:  Sci Rep       Date:  2016-10-20       Impact factor: 4.379

5.  Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions.

Authors:  Ying Chen; Jianwei Jiang; Jie Shi; Wanying Chang; Jun Shi; Man Chen; Qi Zhang
Journal:  Ann Transl Med       Date:  2020-06

6.  Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging.

Authors:  Xiaowen Liang; Jinsui Yu; Jianyi Liao; Zhiyi Chen
Journal:  Biomed Res Int       Date:  2020-01-10       Impact factor: 3.411

7.  A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.

Authors:  Xiaowen Liang; Yingmin Huang; Yongyi Cai; Jianyi Liao; Zhiyi Chen
Journal:  Front Oncol       Date:  2021-10-07       Impact factor: 6.244

8.  Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers.

Authors:  Hao-Chih Tai; Kuen-Yuan Chen; Ming-Hsun Wu; King-Jen Chang; Chiung-Nien Chen; Argon Chen
Journal:  Biomedicines       Date:  2022-06-26

9.  Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists.

Authors:  Tingting Li; Zirui Jiang; Man Lu; Shibin Zou; Minggang Wu; Ting Wei; Lu Wang; Juan Li; Ziyue Hu; Xueqing Cheng; Jifen Liao
Journal:  Medicine (Baltimore)       Date:  2020-06-05       Impact factor: 1.817

  9 in total

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