Development and clinical application of deep learning model for lung nodules screening on CT images

Abstract Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small n...

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Autores principales:	Sijia Cui, Shuai Ming, Yi Lin, Fanghong Chen, Qiang Shen, Hui Li, Gen Chen, Xiangyang Gong, Haochu Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/594791c38bab43e9997acdf8270a1a5e
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spelling	oai:doaj.org-article:594791c38bab43e9997acdf8270a1a5e2021-12-02T18:50:57ZDevelopment and clinical application of deep learning model for lung nodules screening on CT images10.1038/s41598-020-70629-32045-2322https://doaj.org/article/594791c38bab43e9997acdf8270a1a5e2020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70629-3https://doaj.org/toc/2045-2322Abstract Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland–Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.Sijia CuiShuai MingYi LinFanghong ChenQiang ShenHui LiGen ChenXiangyang GongHaochu WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sijia Cui Shuai Ming Yi Lin Fanghong Chen Qiang Shen Hui Li Gen Chen Xiangyang Gong Haochu Wang Development and clinical application of deep learning model for lung nodules screening on CT images
description	Abstract Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland–Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.
format	article
author	Sijia Cui Shuai Ming Yi Lin Fanghong Chen Qiang Shen Hui Li Gen Chen Xiangyang Gong Haochu Wang
author_facet	Sijia Cui Shuai Ming Yi Lin Fanghong Chen Qiang Shen Hui Li Gen Chen Xiangyang Gong Haochu Wang
author_sort	Sijia Cui
title	Development and clinical application of deep learning model for lung nodules screening on CT images
title_short	Development and clinical application of deep learning model for lung nodules screening on CT images
title_full	Development and clinical application of deep learning model for lung nodules screening on CT images
title_fullStr	Development and clinical application of deep learning model for lung nodules screening on CT images
title_full_unstemmed	Development and clinical application of deep learning model for lung nodules screening on CT images
title_sort	development and clinical application of deep learning model for lung nodules screening on ct images
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/594791c38bab43e9997acdf8270a1a5e
work_keys_str_mv	AT sijiacui developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT shuaiming developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT yilin developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT fanghongchen developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT qiangshen developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT huili developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT genchen developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT xiangyanggong developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages AT haochuwang developmentandclinicalapplicationofdeeplearningmodelforlungnodulesscreeningonctimages
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Development and clinical application of deep learning model for lung nodules screening on CT images

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