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Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study
BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. METHODS: This...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668439/ https://www.ncbi.nlm.nih.gov/pubmed/37996814 http://dx.doi.org/10.1186/s12885-023-11456-3 |
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author | Chen, Chen Liu, Yuanzhen Yao, Jincao Wang, Kai Zhang, Maoliang Shi, Fang Tian, Yuan Gao, Lu Ying, Yajun Pan, Qianmeng Wang, Hui Wu, Jinxin Qi, Xiaoqing Wang, Yifan Xu, Dong |
author_facet | Chen, Chen Liu, Yuanzhen Yao, Jincao Wang, Kai Zhang, Maoliang Shi, Fang Tian, Yuan Gao, Lu Ying, Yajun Pan, Qianmeng Wang, Hui Wu, Jinxin Qi, Xiaoqing Wang, Yifan Xu, Dong |
author_sort | Chen, Chen |
collection | PubMed |
description | BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. METHODS: This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists. RESULTS: The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model’s parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data. CONCLUSIONS: This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL. |
format | Online Article Text |
id | pubmed-10668439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106684392023-11-23 Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study Chen, Chen Liu, Yuanzhen Yao, Jincao Wang, Kai Zhang, Maoliang Shi, Fang Tian, Yuan Gao, Lu Ying, Yajun Pan, Qianmeng Wang, Hui Wu, Jinxin Qi, Xiaoqing Wang, Yifan Xu, Dong BMC Cancer Research BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. METHODS: This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists. RESULTS: The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model’s parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data. CONCLUSIONS: This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL. BioMed Central 2023-11-23 /pmc/articles/PMC10668439/ /pubmed/37996814 http://dx.doi.org/10.1186/s12885-023-11456-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Chen Liu, Yuanzhen Yao, Jincao Wang, Kai Zhang, Maoliang Shi, Fang Tian, Yuan Gao, Lu Ying, Yajun Pan, Qianmeng Wang, Hui Wu, Jinxin Qi, Xiaoqing Wang, Yifan Xu, Dong Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title | Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title_full | Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title_fullStr | Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title_full_unstemmed | Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title_short | Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
title_sort | deep learning approaches for differentiating thyroid nodules with calcification: a two-center study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668439/ https://www.ncbi.nlm.nih.gov/pubmed/37996814 http://dx.doi.org/10.1186/s12885-023-11456-3 |
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