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Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer
BACKGROUND: Endocytoscopy (ECS) aids early gastric cancer (EGC) diagnosis by visualization of cells. However, it is difficult for non-experts to accurately diagnose EGC using ECS. In this study, we developed and evaluated a convolutional neural network (CNN)-based system for ECS-aided EGC diagnosis....
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102244/ https://www.ncbi.nlm.nih.gov/pubmed/35549679 http://dx.doi.org/10.1186/s12876-022-02312-y |
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author | Noda, Hiroto Kaise, Mitsuru Higuchi, Kazutoshi Koizumi, Eriko Yoshikata, Keiichiro Habu, Tsugumi Kirita, Kumiko Onda, Takeshi Omori, Jun Akimoto, Teppei Goto, Osamu Iwakiri, Katsuhiko Tada, Tomohiro |
author_facet | Noda, Hiroto Kaise, Mitsuru Higuchi, Kazutoshi Koizumi, Eriko Yoshikata, Keiichiro Habu, Tsugumi Kirita, Kumiko Onda, Takeshi Omori, Jun Akimoto, Teppei Goto, Osamu Iwakiri, Katsuhiko Tada, Tomohiro |
author_sort | Noda, Hiroto |
collection | PubMed |
description | BACKGROUND: Endocytoscopy (ECS) aids early gastric cancer (EGC) diagnosis by visualization of cells. However, it is difficult for non-experts to accurately diagnose EGC using ECS. In this study, we developed and evaluated a convolutional neural network (CNN)-based system for ECS-aided EGC diagnosis. METHODS: We constructed a CNN based on a residual neural network with a training dataset comprising 906 images from 61 EGC cases and 717 images from 65 noncancerous gastric mucosa (NGM) cases. To evaluate diagnostic ability, we used an independent test dataset comprising 313 images from 39 EGC cases and 235 images from 33 NGM cases. The test dataset was further evaluated by three endoscopists, and their findings were compared with CNN-based results. RESULTS: The trained CNN required 7.0 s to analyze the test dataset. The area under the curve of the total ECS images was 0.93. The CNN produced 18 false positives from 7 NGM lesions and 74 false negatives from 28 EGC lesions. In the per-image analysis, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 83.2%, 76.4%, 92.3%, 93.0%, and 74.6%, respectively, with the CNN and 76.8%, 73.4%, 81.3%, 83.9%, and 69.6%, respectively, for the endoscopist-derived values. The CNN-based findings had significantly higher specificity than the findings determined by all endoscopists. In the per-lesion analysis, the accuracy, sensitivity, specificity, PPV, and NPV of the CNN-based findings were 86.1%, 82.1%, 90.9%, 91.4%, and 81.1%, respectively, and those of the results calculated by the endoscopists were 82.4%, 79.5%, 85.9%, 86.9%, and 78.0%, respectively. CONCLUSIONS: Compared with three endoscopists, our CNN for ECS demonstrated higher specificity for EGC diagnosis. Using the CNN in ECS-based EGC diagnosis may improve the diagnostic performance of endoscopists. |
format | Online Article Text |
id | pubmed-9102244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91022442022-05-14 Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer Noda, Hiroto Kaise, Mitsuru Higuchi, Kazutoshi Koizumi, Eriko Yoshikata, Keiichiro Habu, Tsugumi Kirita, Kumiko Onda, Takeshi Omori, Jun Akimoto, Teppei Goto, Osamu Iwakiri, Katsuhiko Tada, Tomohiro BMC Gastroenterol Research BACKGROUND: Endocytoscopy (ECS) aids early gastric cancer (EGC) diagnosis by visualization of cells. However, it is difficult for non-experts to accurately diagnose EGC using ECS. In this study, we developed and evaluated a convolutional neural network (CNN)-based system for ECS-aided EGC diagnosis. METHODS: We constructed a CNN based on a residual neural network with a training dataset comprising 906 images from 61 EGC cases and 717 images from 65 noncancerous gastric mucosa (NGM) cases. To evaluate diagnostic ability, we used an independent test dataset comprising 313 images from 39 EGC cases and 235 images from 33 NGM cases. The test dataset was further evaluated by three endoscopists, and their findings were compared with CNN-based results. RESULTS: The trained CNN required 7.0 s to analyze the test dataset. The area under the curve of the total ECS images was 0.93. The CNN produced 18 false positives from 7 NGM lesions and 74 false negatives from 28 EGC lesions. In the per-image analysis, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 83.2%, 76.4%, 92.3%, 93.0%, and 74.6%, respectively, with the CNN and 76.8%, 73.4%, 81.3%, 83.9%, and 69.6%, respectively, for the endoscopist-derived values. The CNN-based findings had significantly higher specificity than the findings determined by all endoscopists. In the per-lesion analysis, the accuracy, sensitivity, specificity, PPV, and NPV of the CNN-based findings were 86.1%, 82.1%, 90.9%, 91.4%, and 81.1%, respectively, and those of the results calculated by the endoscopists were 82.4%, 79.5%, 85.9%, 86.9%, and 78.0%, respectively. CONCLUSIONS: Compared with three endoscopists, our CNN for ECS demonstrated higher specificity for EGC diagnosis. Using the CNN in ECS-based EGC diagnosis may improve the diagnostic performance of endoscopists. BioMed Central 2022-05-12 /pmc/articles/PMC9102244/ /pubmed/35549679 http://dx.doi.org/10.1186/s12876-022-02312-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Noda, Hiroto Kaise, Mitsuru Higuchi, Kazutoshi Koizumi, Eriko Yoshikata, Keiichiro Habu, Tsugumi Kirita, Kumiko Onda, Takeshi Omori, Jun Akimoto, Teppei Goto, Osamu Iwakiri, Katsuhiko Tada, Tomohiro Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title | Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title_full | Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title_fullStr | Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title_full_unstemmed | Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title_short | Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
title_sort | convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102244/ https://www.ncbi.nlm.nih.gov/pubmed/35549679 http://dx.doi.org/10.1186/s12876-022-02312-y |
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