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Huang J, Zhao J. Quantitative Diagnosis Progress of Ultrasound Imaging Technology in Thyroid Diffuse Diseases. Diagnostics (Basel) 2023; 13:diagnostics13040700. [PMID: 36832188 PMCID: PMC9954877 DOI: 10.3390/diagnostics13040700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
High-frequency ultrasound (HFUS), the imaging modality of choice for thyroid screening, is most commonly used in the study of diffuse thyroid disease (DTD) with Hashimoto's thyroiditis (HT) and Graves' disease (GD). DTD can involve thyroid function and severely affect life quality, so early diagnosis is important for the development of timely clinical intervention strategies. Previously, the diagnosis of DTD relied on qualitative ultrasound imaging and related laboratory tests. In recent years, with the development of multimodal imaging and intelligent medicine, ultrasound and other diagnostic imaging techniques have gradually become more widely used for quantitative assessment of the structure and function of DTD. In this paper, we review the current status and progress of quantitative diagnostic ultrasound imaging techniques for DTD.
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Affiliation(s)
- Jing Huang
- Department of Ultrasound, Changzheng Hospital, Naval Medical University (Second Military Medical University), Shanghai 200003, China
| | - Jiaqi Zhao
- Department of Ultrasound, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
- Correspondence: ; Tel.: +86-21-5560-3999
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Higher EU-TIRADS-Score Correlated with BRAF V600E Positivity in the Early Stage of Papillary Thyroid Carcinoma. J Clin Med 2021; 10:jcm10112304. [PMID: 34070605 PMCID: PMC8199205 DOI: 10.3390/jcm10112304] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/10/2021] [Accepted: 05/20/2021] [Indexed: 01/21/2023] Open
Abstract
The data demonstrating a correlation between sonographic markers of malignancy of thyroid cancer (TC) and its genetic status are scarce. This study aimed to assess whether the addition of genetic analysis at the preoperative step of TC patients' stratification could aid their clinical management. The material consisted of formalin-fixed paraffin-embedded tumor fragments of 49 patients who underwent thyroidectomy during the early stages of papillary TC (PTC). Tumor DNA and RNA were subjected to next-generation sequencing (NGS) on Ion Proton using the Oncomine™ Comprehensive Assay panel. We observed a significant correlation between BRAF V600E and a higher EU-TIRADS score (p-value = 0.02) with a correlation between hypoechogenicity and taller-than-wide tumor shape in analysed patients. There were no other significant associations between the identified genetic variants and other clinicopathological features. For TC patient's stratification, a strong suspicion of BRAF V600E negativity in preoperative management of TC patients could limit the over-treatment of asymptomatic, very low-risk, indolent disease and leave room for active surveillance.
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Peng W, Qian Y, Shi Y, Chen S, Chen K, Xiao H. Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three
patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists
in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning,
support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were
selected after feature optimization by logistic regression analysis (P <0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture
features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.
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Affiliation(s)
- Wenxian Peng
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
| | - Yijia Qian
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
| | - Yingying Shi
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
| | - Shuyun Chen
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
| | - Kexin Chen
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
| | - Han Xiao
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Zhoupu Town, Pudong New Area, Shanghai, 201318, China
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