CoChat
Sign Up
Cited by 59AI in cancer detectionLung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging

Artificial Intelligence and its future potential in lung cancer screening

Open access
Joy Mathew, Christopher, David, Ashwini Maria, Joy Mathew, Chris Mariya·2020·IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund
DOI 10.17179/excli2020-3095PMC PMC7783473
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist’s expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

DataCite · 10.17179/excli2020-3095

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 40

  1. [3]Baldwin DR Gustafson J Pickup L Arteta C Novotny P Declerck J External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules Thorax 2020 75 306 312 32139611 PMC7231457
  2. [5]Chassagnon G Vakalopoulou M Paragios N Revel MP Artificial intelligence applications for thoracic imaging Eur J Radiol 2020 123 108774 31841881
  3. [6]Chen K Li B Tian LF Zhu WB Bao YH Fuzzy speed function based active contour model for segmentation of pulmonary nodules Biomed Mater Eng 2014 24 539 547 24211937
  4. [7]Choi W Oh JH Riyahi S Liu CJ Jiang F Chen W Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer Med Phys 2018 45 1537 1549 29457229 PMC5903960
  5. [8]Cruickshank A Stieler G Ameer F Evaluation of the solitary pulmonary nodule Intern Med J 2019 49 306 315 30897667
  6. [9]Field JK Smith RA Aberle DR Oudkerk M Baldwin DR Yankelevitz D International Association for the Study of Lung Cancer Computed Tomography Screening Workshop 2011 report J Thorac Oncol 2012 7 10 19 22173661
  7. [10]Geary B Walker MJ Snow JT Lee DCH Pernemalm M Maleki-Dizaji S Identification of a biomarker panel for early detection of lung cancer patients J Proteome Res 2019 18 3369 3382 31408348
  8. [11]Hart GR Roffman DA Decker R Deng J A multi-parameterized artificial neural network for lung cancer risk prediction PLoS One 2018 13 e0205264 30356283 PMC6200229
  9. [12]Hirsch FR Scagliotti GV Mulshine JL Kwon R Curran WJ Jr Wu YL Lung cancer: current therapies and new targeted treatments Lancet 2017 389 299 311 27574741
  10. [13]Huang CH Zeng C Wang YC Peng HY Lin CS Chang CJ A study of diagnostic accuracy using a chemical sensor array and a machine learning technique to detect lung cancer Sensors (Basel) 2018 18 2845 PMC6164114 30154385
  11. [14]Huang L Wang L Hu X Chen S Tao Y Su H Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma Nat Commun 2020 11 3556 32678093 PMC7366718
  12. [15]Huang W Xue Y Wu Y A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning PLoS One 2019 14 e0219369 31299053 PMC6625700
  13. [16]International Early Lung Cancer Action Program Investigators, Henschke CI, Yankelevitz DF, Libby DM, Pasmantier MW, Smith JP, Survival of patients with stage I lung cancer detected on CT screening N Engl J Med 2006 355 1763 1771 17065637
  14. [17]Jacobs C van Ginneken B Google's lung cancer AI: A promising tool that needs further validation Nat Rev Clin Oncol 2019 16 532 533 31249401
  15. [18]Kailasam SP Sathik MM A novel hybrid feature extraction model for classification on pulmonary nodules Asian Pac J Cancer Prev 2019 20 457 468 30803208 PMC6897038
  16. [19]Khosravan N Celik H Turkbey B Jones EC Wood B Bagci U A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning Med Image Anal 2019 51 101 115 30399507 PMC6407631
  17. [20]Li L Liu Z Huang H Lin M Luo D Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists Thorac Cancer 2019 10 183 192 30536611 PMC6360226
  18. [21]Li S Xu P Li B Chen L Zhou Z Hao H Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features Phys Med Biol 2019 64 175012 31307017 PMC7106773
  19. [22]Lin Y Leng Q Jiang Z Guarnera MA Zhou Y Chen X A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules Int J Cancer 2017 141 1240 1248 28580707 PMC5526452
  20. [23]Liu H Zhang CM Su ZY Wang K Deng K Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification Comput Math Methods Med 2015 2015 185726 25945120 PMC4405023
  21. [24]Liu WT Wang Y Zhang J Ye F Huang XH Li B A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma Cancer Lett 2018 425 43 53 29608985
  22. [25]Mao Y Yang D He J Krasna MJ Epidemiology of lung cancer Surg Oncol Clin N Am 2016 25 439 445 27261907
  23. [26]Murphy A Skalski M Gaillard F The utilisation of convolutional neural networks in detecting pulmonary nodules: a review Br J Radiol 2018 91 20180028 29869919 PMC6350496
  24. [27]Nasrullah N Sang J Alam MS Mateen M Cai B Hu H Automated lung nodule detection and classification using deep learning combined with multiple strategies Sensors (Basel) 2019 19 3722 PMC6749467 31466261
  25. [28]Nikolaev AE Chernina VY Blokhin IA Shapiev AN Gonchar AP Gombolevskiy VA The future of computer-aided diagnostics in chest computed tomography (in russ 2019 12 91 99 31825348
  26. [29]Onishi Y Teramoto A Tsujimoto M Tsukamoto T Saito K Toyama H Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks Int J Comput Assist Radiol Surg 2020 15 173 178 31732864
  27. [30]Prayer F Röhrich S Pan J Hofmanninger J Langs G Prosch H Künstliche Intelligenz in der Bildgebung der Lunge Artificial intelligence in lung imaging Radiologe 2020 60 42 47 31754738
  28. [31]Randerath W Dreher M Gompelmann D Held M Koczulla R Köhnlein T Technological innovations in pulmonology - examples from diagnostics and therapy Pneumologie 2020 74 585 600 32663891
  29. [32]Reddy UJ Ramana Reddy BV Reddy BE Categorization & recognition of lung tumor using machine learning representations Curr Med Imaging Rev 2019 15 405 413 31989910
  30. [33]Rubin GD Lung nodule and cancer detection in computed tomography screening J Thorac Imaging 2015 30 130 138 25658477 PMC4654704
  31. [34]Schwyzer M Ferraro DA Muehlematter UJ Curioni-Fontecedro A Huellner MW von Schulthess GK Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results Lung Cancer 2018 126 170 173 30527183
  32. [35]Seijo LM Peled N Ajona D Boeri M Field JK Sozzi G Biomarkers in lung cancer screening: achievements, promises, and challenges J Thorac Oncol 2019 14 343 357 30529598 PMC6494979
  33. [36]Shaffie A Soliman A Fraiwan L Ghazal M Taher F Dunlap N A Generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules Technol Cancer Res Treat 2018 17 1533033818798800 30244648 PMC6153532
  34. [37]Song J Wang W Wang Y Qin Y Wang Y Zhou J Epithelial-mesenchymal transition markers screened in a cell-based model and validated in lung adenocarcinoma BMC Cancer 2019 19 680 31296175 PMC6624955
  35. [38]Sun T Wang J Li X Lv P Liu F Luo Y Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set Comput Methods Programs Biomed 2013 111 519 524 23727300
  36. [39]Tammemägi MC Application of risk prediction models to lung cancer screening: a review J Thorac Imaging 2015 30 88 100 25692785
  37. [40]Tang A Tam R Cadrin-Chênevert A Guest W Chong J Barfett J Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology Can Assoc Radiol J 2018 69 120 135 29655580
  38. [41]Tran GS Nghiem TP Nguyen VT Luong CM Burie JC Improving accuracy of lung nodule classification using deep learning with focal loss J Healthc Eng 2019 2019 5156416 30863524 PMC6378763
  39. [42]Tu SJ Wang CW Pan KT Wu YC Wu CT Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening Phys Med Biol 2018 63 065005 29446758
  40. [43]Yu KH Beam AL Kohane IS Artificial intelligence in healthcare Nat Biomed Eng 2018 2 719 731 31015651

Cited by

Sorted by citations

Authors
Joy Mathew, C., David, A. M., & Joy Mathew, C. M.
Venue
IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund
Publisher
IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund
Published
2020
Volume
19
Pages
1552-1562
ISSN
1611-2156 (ISSN-L)
DOI
10.17179/excli2020-3095
PMC
PMC7783473
OpenAlex
W3119122098
Cited by
59
References
1
Citations by year
2026: 4 · 2025: 12 · 2024: 19 · 2023: 9 · 2022: 15
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • EXCLI journal (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (submittedVersion, cc-by)
  • Leibniz-Institut für Arbeitsforschung an der TU Dortmund (IfADo)
  • Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
artificial intelligence, lung cancer screening, nodule classification, deep learning, machine learning, computer-aided detection, radiology, model validation, lung nodule characterization

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review