The role of artificial intelligence in the management of lung cancer: a narrative review
DOI:
https://doi.org/10.57125/FEM.2023.03.30.04Keywords:
artificial intelligence, lung cancer, individual treatment, and early diagnosisAbstract
Background: Lung cancer is the primary reason for cancer-related fatalities worldwide. The majority of lung cancer patients experience delayed diagnosis and ineffective treatment. Artificial intelligence is the term used to describe a computer or robot that enables certain tasks under the supervision of a computer in a way that satisfies human demands.
Aim: This study was evaluated in order to comprehensively estimate the advancements achieved by artificial intelligence technology in pathological diagnosis, early detection of cancer through screening based on imaging, prognostic evaluation, genomes examination, and lung cancer treatment.
Methods: In this review, English studies from common databases such as Pubmed/MEDLINE, Web of Science, Scopus, and the Cochrane Library with the keywords “Artificial intelligence,” “Machine learning,” “treatment,” “Diagnosis,” combined with keywords, involving “Lung cancer”, were involved. The end date for this review is March 2022.
Scientific novelty: Most of the previous studies focused on the diagnostic value of AI evaluation in lung cancer cases without assessing the role of AI in the treatment of lung cancer. The given article evaluated AI's therapeutic and diagnostic advantages for patients with lung cancer.
The practical significance of the result obtained: The results can help physicians to determine the best ways to manage lung cancer and diagnose it early to avoid complications. Additionally, the value of developing AI focused in early lung cancer diagnostic and providing more options for the treatment of lung cancer was explained.
Conclusion: Imaging, histopathological, and genetic analyses of lung cancer all significantly benefit from artificial intelligence. Additionally, artificial intelligence can identify a small number of biomarkers, which is helpful for lung tumor surveillance. Moreover, whether by internal medicine or surgical intervention, the intelligent management of lung tumors has progressively grown to represent the future development trend. AI is anticipated to aid in the early detection of lung tumors and help medical professionals treat each patient uniquely.
References
Chaitanya Thandra K, Barsouk A, Saginala K, Sukumar Aluru J, Barsouk A. Epidemiology of lung cancer. Współczesna Onkol [Internet]. 2021;25(1):45–52. Available from: https://www.termedia.pl/doi/10.5114/wo.2021.103829
Nasim F, Sabath BF, Eapen GA. Lung Cancer. Med Clin North Am [Internet]. 2019 May;103(3):463–73. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0025712518301718
O’Keeffe M, Barratt A, Maher C, Zadro J, Fabbri A, Jones M, Moynihan R. Media Coverage of the Benefits and Harms of Testing the Healthy: a protocol for a descriptive study. BMJ Open [Internet]. 2019 Aug 24;9(8):e029532. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2019-029532
Viscaino M, Maass JC, Delano PH, Torrente M, Stott C, Auat Cheein F. Computer-aided diagnosis of external and middle ear conditions: A machine learning approach. Malmierca MS, editor. PLoS One [Internet]. 2020 Mar 12;15(3):e0229226. Available from: https://dx.plos.org/10.1371/journal.pone.0229226
Li SM, Chen CH, Chen YW, Yen YC, Fang WT, Tsai FY, Chang JL, Shen YY, Huang SF, Chuu CP, Chang IS, Hsiung CA, Jiang SS. Upregulation of CISD2 augments ROS homeostasis and contributes to tumorigenesis and poor prognosis of lung adenocarcinoma. Sci Rep [Internet]. 2017 Dec 19;7(1):11893. Available from: http://www.nature.com/articles/s41598-017-12131-x
Sonego M, Pellizzari I, Dall’Acqua A, Pivetta E, Lorenzon I, Benevol S, Bomben R, Spessotto P, Sorio R, Gattei V, Belletti B, Schiappacassi M, Baldassarre G. Common biological phenotypes characterize the acquisition of platinum-resistance in epithelial ovarian cancer cells. Sci Rep [Internet]. 2017 Dec 2;7(1):7104. Available from: http://www.nature.com/articles/s41598-017-07005-1
Krasnov GS, Puzanov GA, Afanasyeva MA, Dashinimaev EB, Vishnyakova KS, Beniaminov AD, Adzhubei AA, Kondratieva TT, Yegorov YE, Senchenko VN. Tumor suppressor properties of the small C-terminal domain phosphatases in non-small cell lung cancer. Biosci Rep [Internet]. 2019 Dec 20;39(12). Available from: https://portlandpress.com/bioscirep/article/39/12/BSR20193094/221348/Tumor-suppressor-properties-of-the-small-C
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer [Internet]. 2015 Mar 1;136(5):E359–86. Available from: https://onlinelibrary.wiley.com/doi/10.1002/ijc.29210
Roth C, Kasimir-Bauer S, Pantel K, Schwarzenbach H. Screening for circulating nucleic acids and caspase activity in the peripheral blood as potential diagnostic tools in lung cancer. Mol Oncol [Internet]. 2011 Jun;5(3):281–91. Available from: http://doi.wiley.com/10.1016/j.molonc.2011.02.002
Guo H, Chen X, Su C, Liu Y, Wang H, Sun C, Chen P, Jiang M, Xu Y, Wu S, Jia K, Zhao S, Li W, Chen B, Wang L, Yu J, Xiong A, Gao G, Wu F, Li J, Ye L, Bo B, Chen S, Ren S, He Y, Zhou C. Challenges and countermeasures of thoracic oncology in the epidemic of COVID-19. Transl Lung Cancer Res [Internet]. 2020 Apr;9(2):337–47. Available from: http://tlcr.amegroups.com/article/view/38747/html
Rabbani M, Kanevsky J, Kafi K, Chandelier F, Giles FJ. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer. Eur J Clin Invest [Internet]. 2018 Apr;48(4):e12901. Available from: https://onlinelibrary.wiley.com/doi/10.1111/eci.12901
Shi Z, Song T, Wan Y, Xie J, Yan Y, Shi K, Du Y, Shang L. A systematic review and meta-analysis of traditional insect Chinese medicines combined chemotherapy for non-surgical hepatocellular carcinoma therapy. Sci Rep [Internet]. 2017 Dec 28;7(1):4355. Available from: http://www.nature.com/articles/s41598-017-04351-y
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin [Internet]. 2019 Feb 5;caac.21552. Available from: https://onlinelibrary.wiley.com/doi/abs/10.3322/caac.21552
Fraioli F, Serra G, Passariello R. CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects. Radiol Med [Internet]. 2010 Apr 15;115(3):385–402. Available from: http://link.springer.com/10.1007/s11547-010-0507-2
Brinkløv S, Kalko EK V., Surlykke A. Intense echolocation calls from two `whispering’ bats, Artibeus jamaicensis and Macrophyllum macrophyllum (Phyllostomidae). J Exp Biol [Internet]. 2009 Jan 1;212(1):11–20. Available from: https://journals.biologists.com/jeb/article/212/1/11/18204/Intense-echolocation-calls-from-two-whispering
Keshani M, Azimifar Z, Tajeripour F, Boostani R. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system. Comput Biol Med [Internet]. 2013 May;43(4):287–300. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0010482512002089
Gong J, Liu J, Hao W, Nie S, Wang S, Peng W. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis. Phys Med Biol [Internet]. 2019 Jul 5;64(13):135015. Available from: https://iopscience.iop.org/article/10.1088/1361-6560/ab2757
Matsuguma H, Mori K, Nakahara R, Suzuki H, Kasai T, Kamiyama Y, Igarashi S, Kodama T, Yokoi K. Characteristics of Subsolid Pulmonary Nodules Showing Growth During Follow-up With CT Scanning. Chest [Internet]. 2013 Feb;143(2):436–43. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0012369213600926
Henschke CI, Yip R, Smith JP, Wolf AS, Flores RM, Liang M, Salvatore MM, Liu Y, Xu DM, Yankelevitz DF. CT Screening for Lung Cancer: Part-Solid Nodules in Baseline and Annual Repeat Rounds. Am J Roentgenol [Internet]. 2016 Dec;207(6):1176–84. Available from: https://www.ajronline.org/doi/10.2214/AJR.16.16043
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N Engl J Med [Internet]. 2011 Aug 4;365(5):395–409. Available from: http://www.nejm.org/doi/10.1056/NEJMoa1102873
Goo JM. Computer-Aided Detection of Lung Nodules on Chest CT: Issues to be Solved before Clinical Use. Korean J Radiol [Internet]. 2005;6(2):62. Available from: https://www.kjronline.org/DOIx.php?id=10.3348/kjr.2005.6.2.62
Lo SB, Freedman MT, Gillis LB, White CS, Mun SK. JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. Am J Roentgenol [Internet]. 2018 Mar;210(3):480–8. Available from: https://www.ajronline.org/doi/10.2214/AJR.17.18718
Milanese G, Eberhard M, Martini K, Vittoria De Martini I, Frauenfelder T. Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules. Eur J Radiol [Internet]. 2018 Apr;101:97–102. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0720048X18300585
Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules. J Thorac Dis [Internet]. 2020 Nov;12(11):6954–65. Available from: http://jtd.amegroups.com/article/view/42777/html
Yu-Jen Chen YJ, Hua KL, Hsu CH, Cheng WH, Hidayati SC. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther [Internet]. 2015 Aug;2015. Available from: http://www.dovepress.com/computer-aided-classification-of-lung-nodules-on-computed-tomography-i-peer-reviewed-article-OTT
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med [Internet]. 2019 Jun 20;25(6):954–61. Available from: http://www.nature.com/articles/s41591-019-0447-x
Wan YL, Wu P, Huang PC, Tsay PK, Pan KT, Trang N, Chuang WY, Wu CY, Lo S. The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology. Cancers (Basel) [Internet]. 2020 Aug 7;12(8):2211. Available from: https://www.mdpi.com/2072-6694/12/8/2211
Yang D, Li R, Zhang XH, Tang CL, Ma KS, Guo DY, Yan XC. Perfusion Characteristics of Hepatocellular Carcinoma at Contrast-enhanced Ultrasound: Influence of the Cellular differentiation, the Tumor Size and the Underlying Hepatic Condition. Sci Rep [Internet]. 2018 Dec 16;8(1):4713. Available from: http://www.nature.com/articles/s41598-018-23007-z
Yuan Z, Quan J, Yunxiao Z, Jian C, Zhu H, Liping G. Diagnostic Value of Contrast-Enhanced Ultrasound Parametric Imaging in Breast Tumors. J Breast Cancer [Internet]. 2013;16(2):208. Available from: https://ejbc.kr/DOIx.php?id=10.4048/jbc.2013.16.2.208
Liang H, Fu M, Zhou J, Song L. Evaluation of 3D-CPA, HR-HPV, and TCT joint detection on cervical disease screening. Oncol Lett [Internet]. 2016 Aug;12(2):887–92. Available from: https://www.spandidos-publications.com/10.3892/ol.2016.4677
Shulimzon TR. Endomicroscopy, Not “Optical Biopsy” (Yet). Am J Respir Crit Care Med [Internet]. 2017 Apr 1;195(7):962–962. Available from: https://www.atsjournals.org/doi/10.1164/rccm.201608-1616LE
Djuric U, Zadeh G, Aldape K, Diamandis P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. npj Precis Oncol [Internet]. 2017 Dec 19;1(1):22. Available from: http://www.nature.com/articles/s41698-017-0022-1
Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: a systematic review. Crit Care [Internet]. 2019 Dec 22;23(1):284. Available from: https://ccforum.biomedcentral.com/articles/10.1186/s13054-019-2564-9
Jia K, He Y, Dziadziuszko R, Zhao S, Zhang X, Deng J, Wang H, Hirsch FR, Zhou C, Yu H, Zhang L. T cell immunoglobulin and mucin-domain containing-3 in non-small cell lung cancer. Transl Lung Cancer Res [Internet]. 2019 Dec;8(6):895–906. Available from: http://tlcr.amegroups.com/article/view/34623/23736
Koh J, Go H, Kim MY, Jeon YK, Chung JH, Chung DH. A comprehensive immunohistochemistry algorithm for the histological subtyping of small biopsies obtained from non-small cell lung cancers. Histopathology [Internet]. 2014 Dec;65(6):868–78. Available from: https://onlinelibrary.wiley.com/doi/10.1111/his.12507
Revelo AE, Martin A, Velasquez R, Kulandaisamy PC, Bustamante J, Keshishyan S, Otterson G. Liquid biopsy for lung cancers: an update on recent developments. Ann Transl Med [Internet]. 2019 Aug;7(15):349–349. Available from: http://atm.amegroups.com/article/view/24685/24897
Chew RF, Amer S, Jones K, Unangst J, Cajka J, Allpress J, Bruhn M. Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery. Int J Health Geogr [Internet]. 2018 Dec 9;17(1):12. Available from: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0132-1
Lim ZF, Ma PC. Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy. J Hematol Oncol [Internet]. 2019 Dec 9;12(1):134. Available from: https://jhoonline.biomedcentral.com/articles/10.1186/s13045-019-0818-2
Klarenbeek SE, Weekenstroo HHA, Sedelaar JPM, Fütterer JJ, Prokop M, Tummers M. The Effect of Higher Level Computerized Clinical Decision Support Systems on Oncology Care: A Systematic Review. Cancers (Basel) [Internet]. 2020 Apr 22;12(4):1032. Available from: https://www.mdpi.com/2072-6694/12/4/1032
Kim MS, Park HY, Kho BG, Park CK, Oh IJ, Kim YC, Kim S, Yun JS, Song SY, Na KJ, Jeong JU, Yoon MS, Ahn SJ, Yoo SW, Kang SR, Kwon SY, Bom HS, Jang WY, Kim IY, Lee JE, Jeong WG, Kim YH, Lee T, Choi YD. Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res [Internet]. 2020 Jun;9(3):507–14. Available from: http://tlcr.amegroups.com/article/view/39131/html
Raman V, Christopher JK, Jawitz OK, Klapper JA. Robot- vs Video-Assisted Thoracoscopic Lobectomy for Early Lung Cancer. JNCI Cancer Spectr [Internet]. 2020 Oct 1;4(5). Available from: https://academic.oup.com/jncics/article/doi/10.1093/jncics/pkaa031/5820520
Wu L, Wang H, Cai H, Fan J, Jiang G, He Y, Jiang L. Comparison of Double Sleeve Lobectomy by Uniportal Video-Assisted Thoracic Surgery (VATS) and Thoracotomy for NSCLC Treatment. Cancer Manag Res [Internet]. 2019 Dec;Volume 11:10167–74. Available from: https://www.dovepress.com/comparison-of-double-sleeve-lobectomy-by-uniportal-video-assisted-thor-peer-reviewed-article-CMAR
Paul S, Altorki NK, Sheng S, Lee PC, Harpole DH, Onaitis MW, Stiles BM, Port JL, D’Amico TA. Thoracoscopic lobectomy is associated with lower morbidity than open lobectomy: A propensity-matched analysis from the STS database. J Thorac Cardiovasc Surg [Internet]. 2010 Feb;139(2):366–78. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022522309010800
Arad T, Levi-Faber D, Nir RR, Kremer R. [The learning curve of video-assisted thoracoscopic surgery (VATS) for lung lobectomy--a single Israeli center experience]. Harefuah [Internet]. 2012 May;151(5):261–5, 320. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22844727
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg [Internet]. 2018 Jul;268(1):70–6. Available from: https://journals.lww.com/00000658-201807000-00013
Veronesi G, Galetta D, Maisonneuve P, Melfi F, Schmid RA, Borri A, Vannucci F, Spaggiari L. Four-arm robotic lobectomy for the treatment of early-stage lung cancer. J Thorac Cardiovasc Surg [Internet]. 2010 Jul;140(1):19–25. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022522309013907
Guo F, Ma D, Li S. Compare the prognosis of Da Vinci robot-assisted thoracic surgery (RATS) with video-assisted thoracic surgery (VATS) for non-small cell lung cancer. Medicine (Baltimore) [Internet]. 2019 Sep;98(39):e17089. Available from: https://journals.lww.com/10.1097/MD.0000000000017089
Liu X, Xu S, Liu B, Xu W, Ding R, Wang T, Li B, Wang X, Wu Q, Teng H, Wang S. [Survival Analysis of Stage I Non-small Cell Lung Cancer Patients Treated with Da Vinci Robot-assisted Thoracic Surgery]. Zhongguo Fei Ai Za Zhi [Internet]. 2018 Nov 20;21(11):849–56. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30454547
Wang CH, Lin CY, Chen JS, Ho CL, Rau KM, Tsai JT, Chang CS, Yeh SP, Cheng CF, Lai YL. Karnofsky Performance Status as A Predictive Factor for Cancer-Related Fatigue Treatment with Astragalus Polysaccharides (PG2) Injection—A Double Blind, Multi-Center, Randomized Phase IV Study. Cancers (Basel) [Internet]. 2019 Jan 22;11(2):128. Available from: http://www.mdpi.com/2072-6694/11/2/128
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol [Internet]. 2017 Dec 4;14(12):749–62. Available from: http://www.nature.com/articles/nrclinonc.2017.141
Amit G, Purdie TG, Levinshtein A, Hope AJ, Lindsay P, Marshall A, Jaffray DA, Pekar V. Automatic learning-based beam angle selection for thoracic IMRT. Med Phys [Internet]. 2015 Mar 30;42(4):1992–2005. Available from: http://doi.wiley.com/10.1118/1.4908000
Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics [Internet]. 2019;9(5):1303–22. Available from: http://www.thno.org/v09p1303.htm
He Y, Zhou C. Tyrosine kinase inhibitors interstitial pneumonitis: diagnosis and management. Transl Lung Cancer Res [Internet]. 2019 Nov;8(S3):S318–20. Available from: http://tlcr.amegroups.com/article/view/28967/23075
Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya A V., Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov RR, Zhebrak A, Minaeva LI, Zagribelnyy BA, Lee LH, Soll R, Madge D, Xing L, Guo T, Aspuru-Guzik A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol [Internet]. 2019 Sep 2;37(9):1038–40. Available from: http://www.nature.com/articles/s41587-019-0224-x
Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke Vasc Neurol [Internet]. 2019 Dec;4(4):206–13. Available from: https://svn.bmj.com/lookup/doi/10.1136/svn-2019-000290
Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med [Internet]. 2019 Apr 4;380(14):1347–58. Available from: http://www.nejm.org/doi/10.1056/NEJMra1814259
Schmidt C. M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in Oncology. JNCI J Natl Cancer Inst [Internet]. 2017 May;109(5). Available from: https://academic.oup.com/jnci/article-lookup/doi/10.1093/jnci/djx113
Leyens L, Reumann M, Malats N, Brand A. Use of big data for drug development and for public and personal health and care. Genet Epidemiol [Internet]. 2017 Jan;41(1):51–60. Available from: https://onlinelibrary.wiley.com/doi/10.1002/gepi.22012
Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, i Màs JR, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno JA, Wubbe JPM, Zanetti G. Making sense of big data in health research: Towards an EU action plan. Genome Med [Internet]. 2016 Dec 23;8(1):71. Available from: http://genomemedicine.biomedcentral.com/articles/10.1186/s13073-016-0323-y
Liu M, Wu J, Wang N, Zhang X, Bai Y, Guo J, Zhang L, Liu S, Tao K. The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis. Gomes R, editor. PLoS One [Internet]. 2022 Mar 23;18(3):e0273445. Available from: https://dx.plos.org/10.1371/journal.pone.0273445
THONG LT, CHOU HS, CHEW HSJ, LAU Y. Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis. Lung Cancer [Internet]. 2022 Feb;176:4–13. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0169500222007115
Abadia AF, Yacoub B, Stringer N, Snoddy M, Kocher M, Schoepf UJ, Aquino GJ, Kabakus I, Dargis D, Hoelzer P, Sperl JI, Sahbaee P, Vingiani V, Mercer M, Burt JR. Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease. J Thorac Imaging [Internet]. 2022 May;37(3):154–61. Available from: https://journals.lww.com/10.1097/RTI.0000000000000613
Chen Y, Tian X, Fan K, Zheng Y, Tian N, Fan K. The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study. Tang M, editor. Comput Math Methods Med [Internet]. 2022 Mar 22;2022:1–8. Available from: https://www.hindawi.com/journals/cmmm/2022/2864170/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 author

This work is licensed under a Creative Commons Attribution 4.0 International License.
