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The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer

BACKGROUND: The incidence and mortality of lung cancer ranks first in China. Bronchoscopy is one of the most common diagnostic methods for lung cancer. In recent years, image recognition technology(IRT) has been more and more widely studied and applied in the medical field. We developed a diagnostic...

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Autores principales: Deng, Yihong, Chen, Yuan, Xie, Lihua, Wang, Liansheng, Zhan, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9647035/
https://www.ncbi.nlm.nih.gov/pubmed/36387178
http://dx.doi.org/10.3389/fonc.2022.1001840
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author Deng, Yihong
Chen, Yuan
Xie, Lihua
Wang, Liansheng
Zhan, Juan
author_facet Deng, Yihong
Chen, Yuan
Xie, Lihua
Wang, Liansheng
Zhan, Juan
author_sort Deng, Yihong
collection PubMed
description BACKGROUND: The incidence and mortality of lung cancer ranks first in China. Bronchoscopy is one of the most common diagnostic methods for lung cancer. In recent years, image recognition technology(IRT) has been more and more widely studied and applied in the medical field. We developed a diagnostic model of lung cancer under bronchoscopy based on deep learning method and tried to classify pathological types. METHODS: A total of 2238 lesion images were collected retrospectively from 666 cases of lung cancer diagnosed by pathology in the bronchoscopy center of the Third Xiangya Hospital from Oct.01 2017 to Dec.31 2020 and 152 benign cases from Jun.01 2015 to Dec.31 2020. The benign and malignant images were divided into training, verification and test set according to 7:1:2 respectively. The model was trained and tested based on deep learning method. We also tried to classify different pathological types of lung cancer using the model. Furthermore, 9 clinicians with different experience were invited to diagnose the same test images and the results were compared with the model. RESULTS: The diagnostic model took a total of 30s to diagnose 467 test images. The overall accuracy, sensitivity, specificity and area under curve (AUC) of the model to differentiate benign and malignant lesions were 0.951, 0.978, 0.833 and 0.940, which were equivalent to the judgment results of 2 doctors in the senior group and higher than those of other doctors. In the classification of squamous cell carcinoma (SCC) and adenocarcinoma (AC), the overall accuracy was 0.745, including 0.790 for SCC, 0.667 for AC and AUC was 0.728. CONCLUSION: The performance of our diagnostic model to distinguish benign and malignant lesions in bronchoscopy is roughly the same as that of experienced clinicians and the efficiency is much higher than manually. Our study verifies the possibility of applying IRT in diagnosis of lung cancer during white light bronchoscopy.
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spelling pubmed-96470352022-11-15 The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer Deng, Yihong Chen, Yuan Xie, Lihua Wang, Liansheng Zhan, Juan Front Oncol Oncology BACKGROUND: The incidence and mortality of lung cancer ranks first in China. Bronchoscopy is one of the most common diagnostic methods for lung cancer. In recent years, image recognition technology(IRT) has been more and more widely studied and applied in the medical field. We developed a diagnostic model of lung cancer under bronchoscopy based on deep learning method and tried to classify pathological types. METHODS: A total of 2238 lesion images were collected retrospectively from 666 cases of lung cancer diagnosed by pathology in the bronchoscopy center of the Third Xiangya Hospital from Oct.01 2017 to Dec.31 2020 and 152 benign cases from Jun.01 2015 to Dec.31 2020. The benign and malignant images were divided into training, verification and test set according to 7:1:2 respectively. The model was trained and tested based on deep learning method. We also tried to classify different pathological types of lung cancer using the model. Furthermore, 9 clinicians with different experience were invited to diagnose the same test images and the results were compared with the model. RESULTS: The diagnostic model took a total of 30s to diagnose 467 test images. The overall accuracy, sensitivity, specificity and area under curve (AUC) of the model to differentiate benign and malignant lesions were 0.951, 0.978, 0.833 and 0.940, which were equivalent to the judgment results of 2 doctors in the senior group and higher than those of other doctors. In the classification of squamous cell carcinoma (SCC) and adenocarcinoma (AC), the overall accuracy was 0.745, including 0.790 for SCC, 0.667 for AC and AUC was 0.728. CONCLUSION: The performance of our diagnostic model to distinguish benign and malignant lesions in bronchoscopy is roughly the same as that of experienced clinicians and the efficiency is much higher than manually. Our study verifies the possibility of applying IRT in diagnosis of lung cancer during white light bronchoscopy. Frontiers Media S.A. 2022-10-27 /pmc/articles/PMC9647035/ /pubmed/36387178 http://dx.doi.org/10.3389/fonc.2022.1001840 Text en Copyright © 2022 Deng, Chen, Xie, Wang and Zhan 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
Deng, Yihong
Chen, Yuan
Xie, Lihua
Wang, Liansheng
Zhan, Juan
The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title_full The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title_fullStr The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title_full_unstemmed The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title_short The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
title_sort investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9647035/
https://www.ncbi.nlm.nih.gov/pubmed/36387178
http://dx.doi.org/10.3389/fonc.2022.1001840
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