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Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images
BACKGROUND: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for det...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748811/ https://www.ncbi.nlm.nih.gov/pubmed/36531076 http://dx.doi.org/10.3389/fonc.2022.1065934 |
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author | Zeng, Qingwen Feng, Zongfeng Zhu, Yanyan Zhang, Yang Shu, Xufeng Wu, Ahao Luo, Lianghua Cao, Yi Xiong, Jianbo Li, Hong Zhou, Fuqing Jie, Zhigang Tu, Yi Li, Zhengrong |
author_facet | Zeng, Qingwen Feng, Zongfeng Zhu, Yanyan Zhang, Yang Shu, Xufeng Wu, Ahao Luo, Lianghua Cao, Yi Xiong, Jianbo Li, Hong Zhou, Fuqing Jie, Zhigang Tu, Yi Li, Zhengrong |
author_sort | Zeng, Qingwen |
collection | PubMed |
description | BACKGROUND: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. METHODS: We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. RESULTS: The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. CONCLUSIONS: These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients. |
format | Online Article Text |
id | pubmed-9748811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97488112022-12-15 Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images Zeng, Qingwen Feng, Zongfeng Zhu, Yanyan Zhang, Yang Shu, Xufeng Wu, Ahao Luo, Lianghua Cao, Yi Xiong, Jianbo Li, Hong Zhou, Fuqing Jie, Zhigang Tu, Yi Li, Zhengrong Front Oncol Oncology BACKGROUND: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. METHODS: We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. RESULTS: The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. CONCLUSIONS: These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748811/ /pubmed/36531076 http://dx.doi.org/10.3389/fonc.2022.1065934 Text en Copyright © 2022 Zeng, Feng, Zhu, Zhang, Shu, Wu, Luo, Cao, Xiong, Li, Zhou, Jie, Tu and Li https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Oncology Zeng, Qingwen Feng, Zongfeng Zhu, Yanyan Zhang, Yang Shu, Xufeng Wu, Ahao Luo, Lianghua Cao, Yi Xiong, Jianbo Li, Hong Zhou, Fuqing Jie, Zhigang Tu, Yi Li, Zhengrong Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title | Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title_full | Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title_fullStr | Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title_full_unstemmed | Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title_short | Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
title_sort | deep learning model for diagnosing early gastric cancer using preoperative computed tomography images |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748811/ https://www.ncbi.nlm.nih.gov/pubmed/36531076 http://dx.doi.org/10.3389/fonc.2022.1065934 |
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