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A narrative review of deep learning applications in lung cancer research: from screening to prognostication
BACKGROUND AND OBJECTIVE: Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271435/ https://www.ncbi.nlm.nih.gov/pubmed/35832457 http://dx.doi.org/10.21037/tlcr-21-1012 |
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author | Lee, Jong Hyuk Hwang, Eui Jin Kim, Hyungjin Park, Chang Min |
author_facet | Lee, Jong Hyuk Hwang, Eui Jin Kim, Hyungjin Park, Chang Min |
author_sort | Lee, Jong Hyuk |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases. METHODS: we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques. KEY CONTENT AND FINDINGS: DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers. CONCLUSIONS: DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness. |
format | Online Article Text |
id | pubmed-9271435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-92714352022-07-12 A narrative review of deep learning applications in lung cancer research: from screening to prognostication Lee, Jong Hyuk Hwang, Eui Jin Kim, Hyungjin Park, Chang Min Transl Lung Cancer Res Review Article BACKGROUND AND OBJECTIVE: Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases. METHODS: we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques. KEY CONTENT AND FINDINGS: DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers. CONCLUSIONS: DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness. AME Publishing Company 2022-06 /pmc/articles/PMC9271435/ /pubmed/35832457 http://dx.doi.org/10.21037/tlcr-21-1012 Text en 2022 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Review Article Lee, Jong Hyuk Hwang, Eui Jin Kim, Hyungjin Park, Chang Min A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title | A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title_full | A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title_fullStr | A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title_full_unstemmed | A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title_short | A narrative review of deep learning applications in lung cancer research: from screening to prognostication |
title_sort | narrative review of deep learning applications in lung cancer research: from screening to prognostication |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271435/ https://www.ncbi.nlm.nih.gov/pubmed/35832457 http://dx.doi.org/10.21037/tlcr-21-1012 |
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