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Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study

Background: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. Objectives: To establ...

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Autores principales: Gong, Eun Jeong, Bang, Chang Seok, Jung, Kyoungwon, Kim, Su Jin, Kim, Jong Wook, Seo, Seung In, Lee, Uhmyung, Maeng, You Bin, Lee, Ye Ji, Lee, Jae Ick, Baik, Gwang Ho, Lee, Jae Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320232/
https://www.ncbi.nlm.nih.gov/pubmed/35887549
http://dx.doi.org/10.3390/jpm12071052
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author Gong, Eun Jeong
Bang, Chang Seok
Jung, Kyoungwon
Kim, Su Jin
Kim, Jong Wook
Seo, Seung In
Lee, Uhmyung
Maeng, You Bin
Lee, Ye Ji
Lee, Jae Ick
Baik, Gwang Ho
Lee, Jae Jun
author_facet Gong, Eun Jeong
Bang, Chang Seok
Jung, Kyoungwon
Kim, Su Jin
Kim, Jong Wook
Seo, Seung In
Lee, Uhmyung
Maeng, You Bin
Lee, Ye Ji
Lee, Jae Ick
Baik, Gwang Ho
Lee, Jae Jun
author_sort Gong, Eun Jeong
collection PubMed
description Background: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. Objectives: To establish a deep-learning model to diagnose esophageal cancers, precursor lesions, and non-neoplasms using endoscopic images. Additionally, a nationwide prospective multicenter performance verification was conducted to confirm the possibility of real-clinic application. Methods: A total of 5162 white-light endoscopic images were used for the training and internal test of the model classifying esophageal cancers, dysplasias, and non-neoplasms. A no-code deep-learning tool was used for the establishment of the deep-learning model. Prospective multicenter external tests using 836 novel images from five hospitals were conducted. The primary performance metric was the external-test accuracy. An attention map was generated and analyzed to gain the explainability. Results: The established model reached 95.6% (95% confidence interval: 94.2–97.0%) internal-test accuracy (precision: 78.0%, recall: 93.9%, F1 score: 85.2%). Regarding the external tests, the accuracy ranged from 90.0% to 95.8% (overall accuracy: 93.9%). There was no statistical difference in the number of correctly identified the region of interest for the external tests between the expert endoscopist and the established model using attention map analysis (P = 0.11). In terms of the dysplasia subgroup, the number of correctly identified regions of interest was higher in the deep-learning model than in the endoscopist group, although statistically insignificant (P = 0.48). Conclusions: We established a deep-learning model that accurately classifies esophageal cancers, precursor lesions, and non-neoplasms. This model confirmed the potential for generalizability through multicenter external tests and explainability through the attention map analysis.
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spelling pubmed-93202322022-07-27 Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study Gong, Eun Jeong Bang, Chang Seok Jung, Kyoungwon Kim, Su Jin Kim, Jong Wook Seo, Seung In Lee, Uhmyung Maeng, You Bin Lee, Ye Ji Lee, Jae Ick Baik, Gwang Ho Lee, Jae Jun J Pers Med Article Background: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. Objectives: To establish a deep-learning model to diagnose esophageal cancers, precursor lesions, and non-neoplasms using endoscopic images. Additionally, a nationwide prospective multicenter performance verification was conducted to confirm the possibility of real-clinic application. Methods: A total of 5162 white-light endoscopic images were used for the training and internal test of the model classifying esophageal cancers, dysplasias, and non-neoplasms. A no-code deep-learning tool was used for the establishment of the deep-learning model. Prospective multicenter external tests using 836 novel images from five hospitals were conducted. The primary performance metric was the external-test accuracy. An attention map was generated and analyzed to gain the explainability. Results: The established model reached 95.6% (95% confidence interval: 94.2–97.0%) internal-test accuracy (precision: 78.0%, recall: 93.9%, F1 score: 85.2%). Regarding the external tests, the accuracy ranged from 90.0% to 95.8% (overall accuracy: 93.9%). There was no statistical difference in the number of correctly identified the region of interest for the external tests between the expert endoscopist and the established model using attention map analysis (P = 0.11). In terms of the dysplasia subgroup, the number of correctly identified regions of interest was higher in the deep-learning model than in the endoscopist group, although statistically insignificant (P = 0.48). Conclusions: We established a deep-learning model that accurately classifies esophageal cancers, precursor lesions, and non-neoplasms. This model confirmed the potential for generalizability through multicenter external tests and explainability through the attention map analysis. MDPI 2022-06-27 /pmc/articles/PMC9320232/ /pubmed/35887549 http://dx.doi.org/10.3390/jpm12071052 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gong, Eun Jeong
Bang, Chang Seok
Jung, Kyoungwon
Kim, Su Jin
Kim, Jong Wook
Seo, Seung In
Lee, Uhmyung
Maeng, You Bin
Lee, Ye Ji
Lee, Jae Ick
Baik, Gwang Ho
Lee, Jae Jun
Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title_full Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title_fullStr Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title_full_unstemmed Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title_short Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study
title_sort deep-learning for the diagnosis of esophageal cancers and precursor lesions in endoscopic images: a model establishment and nationwide multicenter performance verification study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320232/
https://www.ncbi.nlm.nih.gov/pubmed/35887549
http://dx.doi.org/10.3390/jpm12071052
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