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      Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto’s Thyroiditis

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          Abstract

          Objective

          The aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto’s Thyroiditis.

          Methods

          In this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set. Nodules suspected for malignancy underwent FNA or surgery for pathological results. Two DNN models were trained to diagnose thyroid nodules, and we chose the one with better performance. The features of nodules as well as parenchyma around nodules will be learned by the model to achieve better performance under diffused parenchyma. 10-fold cross-validation and an independent test set were used to evaluate the performance of the algorithm. The performance of the model was compared with that of the three groups of radiologists with clinical experience of <5 years, 5–10 years, >10 years respectively.

          Results

          In total, 9,127 images were collected from 2,932 patients with 7,301 images for the training set and 1,806 for the test set. 56% of the patients enrolled had Hashimoto’s Thyroiditis. The model achieved an AUC of 0.924 for distinguishing malignant and benign nodules in the test set. It showed similar performance under diffused thyroid parenchyma and normal parenchyma with sensitivity of 0.881 versus 0.871 (p = 0.938) and specificity of 0.846 versus 0.822 (p = 0.178). In patients with HT, the model achieved an AUC of 0.924 to differentiate malignant and benign nodules which was significantly higher than that of the three groups of radiologists (AUC = 0.824, 0.857, 0.863 respectively, p < 0.05).

          Conclusion

          The model showed high performance in diagnosing thyroid nodules under both normal and diffused parenchyma. In patients with Hashimoto’s Thyroiditis, the model showed a better performance compared to radiologists with various years of experience.

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          Most cited references27

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Cancer statistics in China, 2015.

            With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.
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              Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                16 March 2021
                2021
                : 11
                : 614172
                Affiliations
                [1] 1 Department of Ultrasound Diagnosis, Ruijin Hospital Affiliated to Shanghai Jiaotong University , Shanghai, China
                [2] 2 Ping An Technology (Shenzhen) Co., Ltd. , Shenzhen, China
                [3] 3 Computer Centre, Ruijin Hospital Affiliated to Shanghai Jiaotong University , Shanghai, China
                Author notes

                Edited by: Katsutoshi Sugimoto, Tokyo Medical University, Japan

                Reviewed by: Hersh Sagreiya, University of Pennsylvania, United States; Yu Yoshimasu, Tokyo Medical University, Japan

                *Correspondence: Weiwei Zhan, shanghairuijin@ 123456126.com ; Lingyun Huang, huanglingyun691@ 123456pingan.com.cn

                †These authors have contributed equally to this work

                This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2021.614172
                8008116
                33796455
                ad35172a-43c6-43bf-ae28-8ecc3f85d3a9
                Copyright © 2021 Hou, Chen, Zhang, Zhou, Lu, Jia, Zhang, Guo, Qin, Zhu, Zuo, Xiao, Huang and Zhan

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 October 2020
                : 28 January 2021
                Page count
                Figures: 5, Tables: 5, Equations: 0, References: 27, Pages: 9, Words: 5368
                Funding
                Funded by: Shanghai Municipal Health and Family Planning Commission 10.13039/501100014175
                Award ID: 2018ZHYL0106
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81671688, 82071923
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                thyroid nodule,ultrasound,deep learning,hashimoto’s thyroiditis,diagnosis

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