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      Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

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          Abstract

          Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

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          Most cited references 25

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          New sonographic criteria for recommending fine-needle aspiration biopsy of nonpalpable solid nodules of the thyroid.

          The purpose of our study was to provide new sonographic criteria for fine-needle aspiration biopsy of nonpalpable solid thyroid nodules. Sonographic scans of 155 nonpalpable thyroid nodules in 132 patients were prospectively classified as having positive or negative findings. Sonographic findings that suggested malignancy included microcalcifications, an irregular or microlobulated margin, marked hypoechogenicity, and a shape that was more tall than it was wide. If even one of these sonographic features was present, the nodule was classified as positive (malignant). If a nodule had none of the features described, it was classified as negative (benign). The final diagnosis of a lesion as benign (n = 106) or malignant (n = 49) was confirmed by fine-needle aspiration biopsy and follow-up (>6 months) in 83 benign nodules, by fine-needle aspiration biopsy and surgery in 44 malignant and 15 benign lesions, and by surgery alone in five malignant and eight benign lesions. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated on the basis of our proposed classification method. Of 82 lesions classified as positive, 46 were malignant. Of 73 lesions classified as negative, three were malignant. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy based on our sonographic classification method were 93.8%, 66%, 56.1%, 95.9%, and 74.8%, respectively. Considering the high level of sensitivity of our proposed sonographic classification, fine-needle aspiration biopsy should be performed on thyroid nodules classified as positive, regardless of palpability.
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            Risk of malignancy in nonpalpable thyroid nodules: predictive value of ultrasound and color-Doppler features.

            The aim of the study was to correlate the sonographic [ultrasound (US)] and color-Doppler (CFD) findings with the results of US-guided fine needle aspiration biopsy (FNA) and of pathologic staging of resected carcinomas to establish: 1) the relative importance of US features as risk factors of malignancy; and 2) a cost-effective management of nonpalpable thyroid nodules. Four hundred ninety-four consecutive patients with nonpalpable thyroid nodules (8-15 mm) were evaluated by US, CFD, and US-FNA. Ninety-two patients with inadequate cytology were excluded from the study. All patients with suspicious or malignant cytology underwent surgery, whereas subjects with benign cytology had clinical and US control 6 months later. Thyroid malignancies were observed in 18 of 195 (9.2%) solitary thyroid nodules and in 13 of 207 (6.3%) multinodular goiters. Cancer prevalence was similar in nodules greater or smaller than 10 mm (9.1 vs. 7.0%). Extracapsular growth (pT(4)) was present in 35.5%, and nodal involvement in 19.4% of neoplastic lesions, with no significant differences between tumors greater or smaller than 10 mm. At US cancers presented a solid hypoechoic appearance in 87% of cases, irregular or blurred margins in 77.4%, an intranodular vascular pattern in 74.2%, and microcalcifications in 29.0%. Irregular margins (RR 16.83), intranodular vascular spots (RR 14.29), and microcalcifications (RR 4.97) were independent risk factors of malignancy. FNA performed on hypoechoic nodules with at least one risk factor was able to identify 87% of the cancers at the expence of cytological evaluation of 38.4% of nonpalpable lesions. The majority of nonpalpable thyroid tumors can be identified by cytological evaluation of lesions presenting hypoechoic appearance in conjunction with one independent risk factor. Due to the nonnegligible prevalence of extracapsular growth and nodal metastasis, US-FNA should be performed on all 8-15 mm hypoechoic nodules with irregular margins, intranodular vascular spots or microcalcifications. Nonpalpable lesions of the thyroid without risk factors should be followed by means of clinical and US evaluation.
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              Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

              With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into “malignant” and “benign” cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.
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                Author and article information

                Journal
                ETJ
                ETJ
                10.1159/issn.2235-0640
                European Thyroid Journal
                S. Karger AG
                2235-0640
                2235-0802
                2020
                July 2020
                04 December 2019
                : 9
                : 4
                : 186-193
                Affiliations
                aKey Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
                bXi’an Hospital of Traditional Chinese Medicine, Xi’an, China
                cLaboratory of Surgical Oncology, Peking University People’s Hospital, Peking University, Beijing, China
                dXijing Hospital, Fourth Military Medical University, Xi’an, China
                Author notes
                *Mingxi Wan, Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xianningxi St. 28, Xi’an 710049 (China), E-Mail mxwan@mail.xjtu.edu.cn, , Shiqi Wang, Xijing Hospital, Fourth Military Medical University, Changlexi St. 127, Xi’an 710032 (China), E-Mail wsqfmmu@126.com
                Article
                504390 Eur Thyroid J 2020;9:186–193
                10.1159/000504390
                7445671
                32903956
                © 2019 European Thyroid Association Published by S. Karger AG, Basel

                Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher. Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

                Page count
                Figures: 2, Tables: 1, Pages: 8
                Categories
                Clinical Thyroidology / Review Article

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