Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder
Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression...
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| Vydané v: | International journal of endocrinology Ročník 2020; číslo 2020; s. 1 - 8 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-8337, 1687-8345 |
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| Abstract | Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use. |
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| AbstractList | Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility.PURPOSESeveral commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility.Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder.METHODSGene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder.The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638-0.931], accuracy of 92.9% [92.7-93.0%], sensitivity of 98.6% [95.9-101.3%], specificity of 58.3% [30.4-86.2%], positive likelihood ratio of 2.367 [1.211-4.625], and negative likelihood ratio of 0.024 [0.003-0.177]. In the cancer prevalence range of 20-40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37-61%, and the range of positive predictive value was 98-99%.RESULTSThe classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638-0.931], accuracy of 92.9% [92.7-93.0%], sensitivity of 98.6% [95.9-101.3%], specificity of 58.3% [30.4-86.2%], positive likelihood ratio of 2.367 [1.211-4.625], and negative likelihood ratio of 0.024 [0.003-0.177]. In the cancer prevalence range of 20-40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37-61%, and the range of positive predictive value was 98-99%.The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.CONCLUSIONThe classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638-0.931], accuracy of 92.9% [92.7-93.0%], sensitivity of 98.6% [95.9-101.3%], specificity of 58.3% [30.4-86.2%], positive likelihood ratio of 2.367 [1.211-4.625], and negative likelihood ratio of 0.024 [0.003-0.177]. In the cancer prevalence range of 20-40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37-61%, and the range of positive predictive value was 98-99%. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use. |
| Audience | Academic |
| Author | Zhang, Lili Xu, Wencan Yang, Peixuan Li, Zexin Wei, Chiju Yang, Kaiji |
| AuthorAffiliation | 2 Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China 4 Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China 3 Multidisciplinary Research Center, Shantou University, No. 243, Daxue Road, Shantou 515063, China 1 Health Care Center, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China |
| AuthorAffiliation_xml | – name: 4 Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China – name: 3 Multidisciplinary Research Center, Shantou University, No. 243, Daxue Road, Shantou 515063, China – name: 1 Health Care Center, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China – name: 2 Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China |
| Author_xml | – sequence: 1 fullname: Yang, Peixuan – sequence: 2 fullname: Wei, Chiju – sequence: 3 fullname: Zhang, Lili – sequence: 4 fullname: Yang, Kaiji – sequence: 5 fullname: Li, Zexin – sequence: 6 fullname: Xu, Wencan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33488708$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.suc.2014.02.004 10.20471/acc.2019.58.03.03 10.1002/cncr.29038 10.3390/cancers11040494 10.1136/jclinpath-2016-204089 10.1016/j.cmpb.2018.10.004 10.1001/jamaoto.2014.1 10.1056/nejmoa1203208 10.1001/jamaoncol.2017.1609 10.1159/000339959 10.1111/j.1365-2362.2009.02162.x 10.1002/cncr.31245 10.1016/j.ejso.2016.11.003 10.7326/0003-4819-126-3-199702010-00009 10.18632/oncotarget.14720 10.1136/bmj.f4706 10.1016/j.surg.2013.07.006 10.1210/jc.2015-1158 10.1007/bf03345725 10.1089/thy.2015.0020 10.5858/arpa.2017-0174-ra 10.1002/cncy.21612 10.1089/thy.2017.0067 10.1177/0194599815579877 |
| ContentType | Journal Article |
| Copyright | Copyright © 2020 Zexin Li et al. COPYRIGHT 2020 John Wiley & Sons, Inc. Copyright © 2020 Zexin Li et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2020 Zexin Li et al. 2020 |
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| References | 22 23 25 26 27 B. R. Haugen (6) 2015; 26 M. Sit (24) 2019; 38 L. Meng (17) 2016; 8 10 11 12 13 14 15 16 18 19 1 2 3 4 5 G. Aktas (21) 2017; 27 7 8 9 20 |
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| SubjectTerms | Corruption Datasets Endocrinology Gene expression Genes Genomes Neural networks Pathology Sparsity Thyroid cancer Thyroid diseases |
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| Title | Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder |
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