Artificial Intelligence Tools for Refining Lung Cancer Screening
Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for ear...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/9/12/3860 |
| _version_ | 1797546476528730112 |
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| author | J. Luis Espinoza Le Thanh Dong |
| author_facet | J. Luis Espinoza Le Thanh Dong |
| author_sort | J. Luis Espinoza |
| collection | DOAJ |
| description | Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening. |
| first_indexed | 2024-03-10T14:30:30Z |
| format | Article |
| id | doaj.art-9f4184df77954c98974b375aeeb59a54 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T14:30:30Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-9f4184df77954c98974b375aeeb59a542023-11-20T22:38:32ZengMDPI AGJournal of Clinical Medicine2077-03832020-11-01912386010.3390/jcm9123860Artificial Intelligence Tools for Refining Lung Cancer ScreeningJ. Luis Espinoza0Le Thanh Dong1Global Health Unit, Faculty of Health Sciences, Kanazawa University, Kanazawa 920-0942, Ishikawa, JapanCenter for Gene and Protein Research, Faculty of Medical Technology, Hanoi Medical University, Hanoi 100000, VietnamNearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.https://www.mdpi.com/2077-0383/9/12/3860lung cancer screeningearly cancer diagnosislung cancer imagingartificial intelligence and lung cancercomputers assisted diagnosis |
| spellingShingle | J. Luis Espinoza Le Thanh Dong Artificial Intelligence Tools for Refining Lung Cancer Screening Journal of Clinical Medicine lung cancer screening early cancer diagnosis lung cancer imaging artificial intelligence and lung cancer computers assisted diagnosis |
| title | Artificial Intelligence Tools for Refining Lung Cancer Screening |
| title_full | Artificial Intelligence Tools for Refining Lung Cancer Screening |
| title_fullStr | Artificial Intelligence Tools for Refining Lung Cancer Screening |
| title_full_unstemmed | Artificial Intelligence Tools for Refining Lung Cancer Screening |
| title_short | Artificial Intelligence Tools for Refining Lung Cancer Screening |
| title_sort | artificial intelligence tools for refining lung cancer screening |
| topic | lung cancer screening early cancer diagnosis lung cancer imaging artificial intelligence and lung cancer computers assisted diagnosis |
| url | https://www.mdpi.com/2077-0383/9/12/3860 |
| work_keys_str_mv | AT jluisespinoza artificialintelligencetoolsforrefininglungcancerscreening AT lethanhdong artificialintelligencetoolsforrefininglungcancerscreening |