Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs
Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer. Patients with pathologically proven resectable l...
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| Veröffentlicht in: | PloS one 2023-03, Vol.18 (3), p.e0281690-e0281690 |
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| creator | Kwak, Se Hyun Kim, Eun-Kyung Kim, Myung Hyun Lee, Eun Hye Shin, Hyun Joo |
| description | Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer.
Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.
Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.
This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs. |
| doi_str_mv | 10.1371/journal.pone.0281690 |
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Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.
Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.
This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0281690</identifier><identifier>PMID: 36897865</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Artificial Intelligence ; Body parts ; Cardiovascular disease ; Chest ; Clinical medicine ; Computer and Information Sciences ; Diagnosis ; Engineering and Technology ; Health surveillance ; Hospitals ; Humans ; Lesions ; Lung cancer ; Lung diseases ; Lung Neoplasms - pathology ; Medical imaging ; Medical prognosis ; Medical records ; Medical screening ; Medicine and Health Sciences ; Methods ; Mortality ; Nodules ; Patients ; People and Places ; Pneumothorax ; Pulmonology ; Radiographs ; Radiography ; Radiography, Medical ; Radiography, Thoracic - methods ; Retrospective Studies ; Software ; Thoracic surgery</subject><ispartof>PloS one, 2023-03, Vol.18 (3), p.e0281690-e0281690</ispartof><rights>Copyright: © 2023 Kwak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Kwak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Kwak et al 2023 Kwak et al</rights><rights>2023 Kwak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c693t-99f328e196e04be2546bae7deacdbffde65e5ea1e1d0df1eacb3f7981af7b44f3</citedby><cites>FETCH-LOGICAL-c693t-99f328e196e04be2546bae7deacdbffde65e5ea1e1d0df1eacb3f7981af7b44f3</cites><orcidid>0000-0002-7462-2609</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10004566/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10004566/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23871,27929,27930,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36897865$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Lim, Jun Hyeok</contributor><creatorcontrib>Kwak, Se Hyun</creatorcontrib><creatorcontrib>Kim, Eun-Kyung</creatorcontrib><creatorcontrib>Kim, Myung Hyun</creatorcontrib><creatorcontrib>Lee, Eun Hye</creatorcontrib><creatorcontrib>Shin, Hyun Joo</creatorcontrib><title>Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer.
Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.
Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.
This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.</description><subject>Artificial Intelligence</subject><subject>Body parts</subject><subject>Cardiovascular disease</subject><subject>Chest</subject><subject>Clinical medicine</subject><subject>Computer and Information Sciences</subject><subject>Diagnosis</subject><subject>Engineering and Technology</subject><subject>Health surveillance</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Lesions</subject><subject>Lung cancer</subject><subject>Lung diseases</subject><subject>Lung Neoplasms - pathology</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Medical records</subject><subject>Medical screening</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Mortality</subject><subject>Nodules</subject><subject>Patients</subject><subject>People and Places</subject><subject>Pneumothorax</subject><subject>Pulmonology</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiography, Medical</subject><subject>Radiography, Thoracic - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kwak, Se Hyun</au><au>Kim, Eun-Kyung</au><au>Kim, Myung Hyun</au><au>Lee, Eun Hye</au><au>Shin, Hyun Joo</au><au>Lim, Jun Hyeok</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-03-10</date><risdate>2023</risdate><volume>18</volume><issue>3</issue><spage>e0281690</spage><epage>e0281690</epage><pages>e0281690-e0281690</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer.
Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.
Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.
This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36897865</pmid><doi>10.1371/journal.pone.0281690</doi><tpages>e0281690</tpages><orcidid>https://orcid.org/0000-0002-7462-2609</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Artificial Intelligence Body parts Cardiovascular disease Chest Clinical medicine Computer and Information Sciences Diagnosis Engineering and Technology Health surveillance Hospitals Humans Lesions Lung cancer Lung diseases Lung Neoplasms - pathology Medical imaging Medical prognosis Medical records Medical screening Medicine and Health Sciences Methods Mortality Nodules Patients People and Places Pneumothorax Pulmonology Radiographs Radiography Radiography, Medical Radiography, Thoracic - methods Retrospective Studies Software Thoracic surgery |
| title | Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs |
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