Computer-aided diagnosis system versus conventional reading system in low-dose (< 2 mSv) computed tomography
comparative study for patients at risk of lung cancer
Keywords:
Diagnostic imaging, Early detection of cancer, Lung neoplasmsAbstract
BACKGROUND: Computer-aided diagnosis in low-dose (≤ 3 mSv) computed tomography (CT) is a potential screening tool for lung nodules, with quality interpretation and less inter-observer variability among readers. Therefore, we aimed to determine the screening potential of CT using a radiation dose that does not exceed 2 mSv. OBJECTIVE: We aimed to compare the diagnostic parameters of low-dose (< 2 mSv) CT interpretation results using a computer-aided diagnosis system for lung cancer screening with those of a conventional reading system used by radiologists. DESIGN AND SETTING: We conducted a comparative study of chest CT images for lung cancer screening at three private institutions. METHODS: A database of low-dose (< 2 mSv) chest CT images of patients at risk of lung cancer was viewed with the conventional reading system (301 patients and 226 nodules) or computer-aided diagnosis system without any subsequent radiologist review (944 patients and 1,048 nodules). RESULTS: The numbers of detected and solid nodules per patient (both P < 0.0001) were higher using the computer-aided diagnosis system than those using the conventional reading system. The nodule size was reported as the maximum size in any plane in the computer-aided diagnosis system. Higher numbers of patients (102 [11%] versus 20 [7%], P = 0.0345) and nodules (154 [15%] versus 17 [8%], P = 0.0035) were diagnosed with cancer using the computer-aided diagnosis system. CONCLUSIONS: The computer-aided diagnosis system facilitates the diagnosis of cancerous nodules, especially solid nodules, in low-dose (< 2 mSv) CT among patients at risk for lung cancer.
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References
National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. PMID: 21714641; https://doi.org/10.1056/NEJMoa1102873.
Pastorino U, Silva M, Sestini S, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol. 2019;30(7):1162-9. PMID: 30937431; https://doi.org/10.1093/annonc/mdz117.
de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503-13. PMID: 31995683; https://doi.org/10.1056/NEJMoa1911793.
Pinsky PF. Lung cancer screening with low-dose CT: A world-wide view. Transl Lung Cancer Res. 2018;7(3):234-42. PMID: 30050762; https://doi.org/10.21037/tlcr.2018.05.12.
Hwang EJ, Goo JM, Kim HY, et al. Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: Comparison with the conventional reading system. Eur Radiol. 2021;31(1):475-85. PMID: 32797309; https://doi.org/10.1007/s00330-020-07151-7.
Fintelmann FJ, Bernheim A, Digumarthy SR, et al. The 10 pillars of lung cancer screening: Rationale and logistics of a lung cancer screening program. Radiographics. 2015;35(7):1893-908. PMID: 26495797; https://doi.org/10.1148/rg.2015150079.
McKee BJ, McKee AB, Kitts AB, Regis SM, Wald C. Low-dose computed tomography screening for lung cancer in a clinical setting: Essential elements of a screening program. J Thorac Imaging. 2015;30(2):115-29. PMID: 25658476; https://doi.org/10.1097/RTI.0000000000000139.
Mitchell EP. U.S. preventive services task force final recommendation statement, evidence summary, and modeling studies on screening for lung cancer. J Natl Med Assoc. 2021;113(3):239-40. PMID: 34274036; https://doi.org/10.1016/j.jnma.2021.05.012.
Tanoue LT, Tanner NT, Gould MK, Silvestri GA. Lung cancer screening. Am J Respir Crit Care Med. 2015;191(1):19-33. PMID: 25369325; https://doi.org/10.1164/rccm.201410-1777CI.
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-57. PMID: 30720861; https://doi.org/10.3322/caac.21552.
Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: Computer-aided diagnosis of missed lung cancers. Radiology. 2016;281(1):279-88. PMID: 27019363; https://doi.org/10.1148/radiol.2016150063.
Jeon KN, Goo JM, Lee CH, et al. Computer-aided nodule diagnosis and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography. Invest Radiol. 2012;47(8):457-61. PMID: 22717879; https://doi.org/10.1097/RLI.0b013e318250a5aa.
Field JK, Duffy SW, Baldwin DR, et al. The UK Lung Cancer Screening Trial: A pilot randomised controlled trial of low-dose computed tomography screening for the early diagnosis of lung cancer. Health Technol Assess. 2016;20(40):1-146. PMID: 27224642; https://doi.org/10.3310/hta20400.
Kauczor HU, Bonomo L, Gaga M, et al. ESR/ERS white paper on lung cancer screening. Eur Radiol. 2015;25(9):2519-31. PMID: 25929939; https://doi.org/10.1007/s00330-015-3697-0.
Brown MS, Lo P, Goldin JG, et al. Toward clinically usable CAD for lung cancer screening with computed tomography. Eur Radiol. 2014;24(11):2719-28. PMID: 25052078; https://doi.org/10.1007/s00330-014-3329-0.
Setio AA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic diagnosis of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal. 2017;42:1-13. PMID: 28732268; https://doi.org/10.1016/j. media.2017.06.015.
Lyu Z, Li N, Chen S, et al. Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population. Cancer Med. 2020;9(11):3983-94. PMID: 32253829; https://doi.org/10.1002/cam4.3025.
American College of Radiology. Lung-RADS® Version 1.1 Assessment Categories Release date: 2019. Available from: https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/LungRADSAssessmentCategoriesv1-1.pdf. Accessed in 2022 (May 3).
Jacobs C, van Rikxoort EM, Murphy K, et al. Computer-aided diagnosis of pulmonary nodules: A comparative study using the public LIDC/IDRI database. Eur Radiol 2016;26(7):2139-47. PMID: 26443601; https://doi.org/10.1007/s00330-015-4030-7.
Pinsky PF, Gierada DS, Nath PH, Kazerooni E, Amorosa J. National lung screening trial: Variability in nodule diagnosis rates in chest CT studies. Radiology. 2013;268(3):865-73. PMID: 23592767; https://doi.org/10.1148/radiol.13121530.
van Riel SJ, Jacobs C, Scholten ET, et al. Observer variability for Lung-RADS categorisation of lung cancer screening CTs: Impact on patient management. Eur Radiol. 2019;29(2):924-31. PMID: 30066248; https://doi.org/10.1007/s00330-018-5599-4.
Marshall HM, Zhao H, Bowman RV, et al. The effect of different radiological models on diagnostic accuracy and lung cancer screening performance. Thorax. 2017;72(12):1147-50. PMID: 28331076; https://doi.org/10.1136/thoraxjnl-2016-209624.
Bankier AA, MacMahon H, Goo JM, et al. Recommendations for measuring pulmonary nodules at CT: A statement from the Fleischner Society. Radiology. 2017;285(2):584-600. PMID: 28650738; https://doi.org/10.1148/radiol.2017162894.
Silva M, Schaefer-Prokop CM, Jacobs C, et al. Detection of subsolid nodules in lung cancer screening: Complementary sensitivity of visual reading and computer-aided diagnosis. Invest Radiol. 2018;53(8):441-9. PMID: 29543693; https://doi.org/10.1097/RLI.0000000000000464.
Tammemagi M, Ritchie AJ, Atkar-Khattra S, et al, Predicting malignancy risk of screen-detected lung nodules-mean diameter or volume. J Thorac Oncol. 2019;14(2):203-11. PMID: 30368011; https://doi.org/10.1016/j.jtho.2018.10.006.
