Artificial Intelligence and Data Mining to Assess Lung Cancer Risk: Challenges and Opportunities
Authors: Paul Pinsky
Journal: Annals of Internal Medicine (2020)

Editorials3 November 2020Artificial Intelligence and Data Mining to Assess Lung Cancer Risk: Challenges and OpportunitiesPaul Pinsky, PhDPaul Pinsky, PhDNational Cancer Institute, Bethesda, Maryland (P.P.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-5673 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The article by Lu and colleagues describes a creative use of artificial intelligence (AI) and stored patient data to assess risk for lung cancer, with the aim of identifying persons potentially eligible for screening (1). This research brings up several points of interest. Most germane to the article are how to assess eligibility for low-dose computed tomography (LDCT) lung cancer screening and how to understand the nature of AI in the context of lung cancer detection and prediction. However, viewing the research in its wider context spotlights many issues associated with the use of AI and, more generally, data mining ...References1. Lu MT, Raghu VK, Mayrhofer T, et al. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: development and validation of a prediction model. Ann Intern Med. 2020;173:704-13. doi:10.7326/M20-1868 LinkGoogle Scholar2. Moyer VA; U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160:330-8. doi:10.7326/M13-2771 LinkGoogle Scholar3. U.S. Preventive Services Task Force. Draft recommendation statement. Lung cancer: screening. Accessed at www.uspreventiveservicestaskforce.org/uspstf/draft-recommendation/lung-cancer-screening-2020 on 30 July 2020. Google Scholar4. Gould MK, Sakoda LC, Ritzwoller DP, et al. Monitoring lung cancer screening use and outcomes at four Cancer Research Network sites. Ann Am Thorac Soc. 2017;14:1827-1835. [PMID: 28683215] doi:10.1513/AnnalsATS.201703-237OC CrossrefMedlineGoogle Scholar5. Ali MU, Miller J, Peirson L, et al. Screening for lung cancer: a systematic review and meta-analysis. Prev Med. 2016;89:301-314. [PMID: 27130532] doi:10.1016/j.ypmed.2016.04.015 CrossrefMedlineGoogle Scholar6. Billatos E, Vick JL, Lenburg ME, et al. The airway transcriptome as a biomarker for early lung cancer detection. Clin Cancer Res. 2018;24:2984-2992. [PMID: 29463557] doi:10.1158/1078-0432.CCR-16-3187 CrossrefMedlineGoogle Scholar7. Rühl R, Wozniak MM, Werk M, et al. CsI-detector-based dual-exposure dual energy in chest radiography for lung nodule detection: results of an international multicenter trial. Eur Radiol. 2008;18:1831-9. [PMID: 18425520] doi:10.1007/s00330-008-0945-6 CrossrefMedlineGoogle Scholar8. Vellido A. Societal issues concerning the application of artificial intelligence in medicine. Kidney Dis (Basel). 2019;5:11-17. [PMID: 30815459] doi:10.1159/000492428 CrossrefMedlineGoogle Scholar9. National Lung Screening Trial Research Team. Lung cancer incidence and mortality with extended follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019;14:1732-1742. [PMID: 31260833] doi:10.1016/j.jtho.2019.05.044 CrossrefMedlineGoogle Scholar10. McKernan LC, Clayton EW, Walsh CG. Protecting life while preserving liberty: ethical recommendations for suicide prevention with artificial intelligence. Front Psychiatry. 2018;9:650. [PMID: 30559686] doi:10.3389/fpsyt.2018.00650 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: National Cancer Institute, Bethesda, Maryland (P.P.)Disclosures: The author has disclosed no conflicts of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-5673.Corresponding Author: Paul Pinsky, PhD, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892; e-mail, [email protected]nih.gov.This article was published at Annals.org on 1 September 2020. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoDeep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model Michael T. Lu , Vineet K. Raghu , Thomas Mayrhofer , Hugo J.W.L. Aerts , and Udo Hoffmann Metrics Cited byComputational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target PredictionElectronic Health Records and Machine Learning for Early Detection of Lung Cancer and Other Conditions: Thinking about the Path Ahead 3 November 2020Volume 173, Issue 9Page: 760-761KeywordsAlgorithmsArtificial intelligenceCarcinogenesisElectronic medical recordsForecastingLung and intrathoracic tumorsLung cancer screeningPatientsSimulation and modelingThorax ePublished: 1 September 2020 Issue Published: 3 November 2020 PDF downloadLoading ...