DOI QR코드

DOI QR Code

A Computer-Aided Diagnosis for Evaluating Lung Nodules on Chest CT: the Current Status and Perspective

  • Goo, Jin-Mo (Department of Radiology, Seoul National University College of Medicine and the Institute of Radiation Medicine, Seoul National University Medical Research Center)
  • Published : 2011.04.01

Abstract

As the detection and characterization of lung nodules are of paramount importance in thoracic radiology, various tools for making a computer-aided diagnosis (CAD) have been developed to improve the diagnostic performance of radiologists in clinical practice. Numerous studies over the years have shown that the CAD system can effectively help readers identify more nodules. Moreover, nodule malignancy and the response of malignant lung tumors to treatment can also be assessed using nodule volumetry. CAD also has the potential to objectively analyze the morphology of nodules and enhance the workflow during the assessment of follow-up studies. Therefore, understanding the current status and limitations of CAD for evaluating lung nodules is essential to effectively apply CAD in clinical practice.

Keywords

References

  1. Goo JM, Lee JW, Lee HJ, Kim S, Kim JH, Im JG. Automated lung nodule detection at low-dose CT: preliminary experience. Korean J Radiol 2003;4:211-216 https://doi.org/10.3348/kjr.2003.4.4.211
  2. Awai K, Murao K, Ozawa A, Komi M, Hayakawa H, Hori S, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. Radiology 2004;230:347-352 https://doi.org/10.1148/radiol.2302030049
  3. Beigelman-Aubry C, Raffy P, Yang W, Castellino RA, Grenier PA. Computer-aided detection of solid lung nodules on followup MDCT screening: evaluation of detection, tracking, and reading time. AJR Am J Roentgenol 2007;189:948-955 https://doi.org/10.2214/AJR.07.2302
  4. Goo JM, Kim HY, Lee JW, Lee HJ, Lee CH, Lee KW, et al. Is the computer-aided detection scheme for lung nodule also useful in detecting lung cancer? J Comput Assist Tomogr 2008;32:570-575 https://doi.org/10.1097/RCT.0b013e318146261c
  5. Park EA, Goo JM, Lee JW, Kang CH, Lee HJ, Lee CH, et al. Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases. Invest Radiol 2009;44:105-113 https://doi.org/10.1097/RLI.0b013e318190fcfc
  6. Hirose T, Nitta N, Shiraishi J, Nagatani Y, Takahashi M, Murata K. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy. Acad Radiol 2008;15:1505- 1512 https://doi.org/10.1016/j.acra.2008.06.009
  7. Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Kazerooni EA, Chughtai AR, et al. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Acad Radiol 2009;16:1518- 1530 https://doi.org/10.1016/j.acra.2009.08.006
  8. McCulloch CC, Kaucic RA, Mendonça PR, Walter DJ, Avila RS. Model-based detection of lung nodules in computed tomography exams. Thoracic computer-aided diagnosis. Acad Radiol 2004;11:258-266 https://doi.org/10.1016/S1076-6332(03)00729-3
  9. Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. Radiology 2005;236:286-293 https://doi.org/10.1148/radiol.2361041286
  10. Lee IJ, Gamsu G, Czum J, Wu N, Johnson R, Chakrapani S. Lung nodule detection on chest CT: evaluation of a computeraided detection (CAD) system. Korean J Radiol 2005;6:89-93 https://doi.org/10.3348/kjr.2005.6.2.89
  11. Marten K, Engelke C, Seyfarth T, Grillhösl A, Obenauer S, Rummeny EJ. Computer-aided detection of pulmonary nodules: influence of nodule characteristics on detection performance. Clin Radiol 2005;60:196-206 https://doi.org/10.1016/j.crad.2004.05.014
  12. Das M, Mühlenbruch G, Mahnken AH, Flohr TG, Gündel L, Stanzel S, et al. Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology 2006;241:564-571 https://doi.org/10.1148/radiol.2412051139
  13. Yuan R, Vos PM, Cooperberg PL. Computer-aided detection in screening CT for pulmonary nodules. AJR Am J Roentgenol 2006;186:1280-1287 https://doi.org/10.2214/AJR.04.1969
  14. Armato SG 3rd, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, et al. Lung image database consortium: developing a resource for the medical imaging research community. Radiology 2004;232:739-748 https://doi.org/10.1148/radiol.2323032035
  15. Lee JW, Goo JM, Lee HJ, Kim JH, Kim S, Kim YT. The potential contribution of a computer-aided detection system for lung nodule detection in multidetector row computed tomography. Invest Radiol 2004;39:649-655 https://doi.org/10.1097/00004424-200411000-00001
  16. Rubin GD, Lyo JK, Paik DS, Sherbondy AJ, Chow LC, Leung AN, et al. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 2005;234:274-283 https://doi.org/10.1148/radiol.2341040589
  17. Goo JM. Computer-aided detection of lung nodules on chest CT: issues to be solved before clinical use. Korean J Radiol 2005;6:62-63 https://doi.org/10.3348/kjr.2005.6.2.62
  18. Li F, Arimura H, Suzuki K, Shiraishi J, Li Q, Abe H, et al. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005;237:684-690
  19. Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results. Radiology 2005;236:295-299 https://doi.org/10.1148/radiol.2361041288
  20. Marten K, Grillhösl A, Seyfarth T, Obenauer S, Rummeny EJ, Engelke C. Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings. Eur Radiol 2005;15:203-212 https://doi.org/10.1007/s00330-004-2544-5
  21. Lee JY, Chung MJ, Yi CA, Lee KS. Ultra-low-dose MDCT of the chest: infl uence on automated lung nodule detection. Korean J Radiol 2008;9:95-101 https://doi.org/10.3348/kjr.2008.9.2.95
  22. Hein PA, Rogalla P, Klessen C, Lembcke A, Romano VC. Computer-aided pulmonary nodule detection - performance of two CAD systems at different CT dose levels. Rofo 2009;181:1056-1064 https://doi.org/10.1055/s-0028-1109394
  23. Beyer F, Zierott L, Fallenberg EM, Juergens KU, Stoeckel J, Heindel W, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol 2007;17:2941-2947 https://doi.org/10.1007/s00330-007-0667-1
  24. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS. CT screening for lung cancer: frequency and signifi cance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002;178:1053-1057 https://doi.org/10.2214/ajr.178.5.1781053
  25. Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, et al. Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 2005;237:657- 661 https://doi.org/10.1148/radiol.2372041461
  26. Yanagawa M, Honda O, Yoshida S, Ono Y, Inoue A, Daimon T, et al. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases. Acad Radiol 2009;16:924-933 https://doi.org/10.1016/j.acra.2009.01.030
  27. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217:251-256 https://doi.org/10.1148/radiology.217.1.r00oc33251
  28. Revel MP, Lefort C, Bissery A, Bienvenu M, Aycard L, Chatellier G, et al. Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004;231:459-466 https://doi.org/10.1148/radiol.2312030241
  29. Revel MP, Merlin A, Peyrard S, Triki R, Couchon S, Chatellier G, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol 2006;187:135-142 https://doi.org/10.2214/AJR.05.1228
  30. Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT. Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology 2005;235:850-856 https://doi.org/10.1148/radiol.2353040737
  31. Zhao B, Schwartz LH, Moskowitz CS, Wang L, Ginsberg MS, Cooper CA, et al. Pulmonary metastases: effect of CT section thickness on measurement--initial experience. Radiology 2005;234:934-939 https://doi.org/10.1148/radiol.2343040020
  32. Das M, Ley-Zaporozhan J, Gietema HA, Czech A, Mühlenbruch G, Mahnken AH, et al. Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur Radiol 2007;17:1979-1984 https://doi.org/10.1007/s00330-006-0562-1
  33. Petrou M, Quint LE, Nan B, Baker LH. Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology. AJR Am J Roentgenol 2007;188:306-312 https://doi.org/10.2214/AJR.05.1063
  34. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 2006;25:417-434 https://doi.org/10.1109/TMI.2006.871547
  35. Ravenel JG, Leue WM, Nietert PJ, Miller JV, Taylor KK, Silvestri GA. Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study. Radiology 2008;247:400-408 https://doi.org/10.1148/radiol.2472070868
  36. Honda O, Sumikawa H, Johkoh T, Tomiyama N, Mihara N, Inoue A, et al. Computer-assisted lung nodule volumetry from multi-detector row CT: infl uence of image reconstruction parameters. Eur J Radiol 2007;62:106-113 https://doi.org/10.1016/j.ejrad.2006.11.017
  37. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, Macmahon H, et al. Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol 2006;13:1254-1265 https://doi.org/10.1016/j.acra.2006.07.012
  38. de Hoop B, Gietema H, van Ginneken B, Zanen P, Groenewegen G, Prokop M. A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 2009;19:800- 808 https://doi.org/10.1007/s00330-008-1229-x
  39. Ko JP, Rusinek H, Jacobs EL, Babb JS, Betke M, McGuinness G, et al. Small pulmonary nodules: volume measurement at chest CT--phantom study. Radiology 2003;228:864-870 https://doi.org/10.1148/radiol.2283020059
  40. Goo JM, Kim KG, Gierada DS, Castro M, Bae KT. Volumetric measurements of lung nodules with multi-detector row CT: effect of changes in lung volume. Korean J Radiol 2006;7:243- 248 https://doi.org/10.3348/kjr.2006.7.4.243
  41. Petkovska I, Brown MS, Goldin JG, Kim HJ, McNitt-Gray MF, Abtin FG, et al. The effect of lung volume on nodule size on CT. Acad Radiol 2007;14:476-485 https://doi.org/10.1016/j.acra.2007.01.008
  42. Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI. Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 2004;231:446-452 https://doi.org/10.1148/radiol.2312030553
  43. Wormanns D, Kohl G, Klotz E, Marheine A, Beyer F, Heindel W, et al. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 2004;14:86-92 https://doi.org/10.1007/s00330-003-2132-0
  44. Zhao B, James LP, Moskowitz CS, Guo P, Ginsberg MS, Lefkowitz RA, et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 2009;252:263-272 https://doi.org/10.1148/radiol.2522081593
  45. Goodman LR, Gulsun M, Washington L, Nagy PG, Piacsek KL. Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol 2006;186:989-994 https://doi.org/10.2214/AJR.04.1821
  46. Gietema HA, Schaefer-Prokop CM, Mali WP, Groenewegen G, Prokop M. Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT-- influence of inspiration level, nodule size, and segmentation performance. Radiology 2007;245:888-894 https://doi.org/10.1148/radiol.2452061054
  47. Zhao B, Schwartz LH, Moskowitz CS, Ginsberg MS, Rizvi NA, Kris MG. Lung cancer: computerized quantification of tumor response--initial results. Radiology 2006;241:892-898 https://doi.org/10.1148/radiol.2413051887
  48. Erasmus JJ, Gladish GW, Broemeling L, Sabloff BS, Truong MT, Herbst RS, et al. Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. J Clin Oncol 2003;21:2574-2582 https://doi.org/10.1200/JCO.2003.01.144
  49. Marten K, Auer F, Schmidt S, Kohl G, Rummeny EJ, Engelke C. Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria. Eur Radiol 2006;16:781-790 https://doi.org/10.1007/s00330-005-0036-x
  50. Buckler AJ, Mulshine JL, Gottlieb R, Zhao B, Mozley PD, Schwartz L. The use of volumetric CT as an imaging biomarker in lung cancer. Acad Radiol 2010;17:100-106 https://doi.org/10.1016/j.acra.2009.07.030
  51. Oda S, Awai K, Murao K, Ozawa A, Yanaga Y, Kawanaka K, et al. Computer-aided volumetry of pulmonary nodules exhibiting ground-glass opacity at MDCT. AJR Am J Roentgenol 2010;194:398-406 https://doi.org/10.2214/AJR.09.2583
  52. Park CM, Goo JM, Lee HJ, Kim KG, Kang MJ, Shin YH. Persistent pure ground-glass nodules in the lung: interscan variability of semiautomated volume and attenuation measurements. AJR Am J Roentgenol 2010;195:W408-W414 https://doi.org/10.2214/AJR.09.4157
  53. Park CM, Goo JM, Lee HJ, Lee CH, Chun EJ, Im JG. Nodular ground-glass opacity at thin-section CT: histologic correlation and evaluation of change at follow-up. Radiographics 2007;27:391-408 https://doi.org/10.1148/rg.272065061
  54. Lee KW, Im JG, Kim TJ, Dae CM. A new method of measuring the amount of soft tissue in pulmonary ground-glass opacity nodules: a phantom study. Korean J Radiol 2008;9:219-225 https://doi.org/10.3348/kjr.2008.9.3.219
  55. van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, Vernhout R, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009;361:2221-2229 https://doi.org/10.1056/NEJMoa0906085
  56. Lee HJ, Goo JM, Lee CH, Park CM, Kim KG, Park EA, et al. Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: the effects on radiologist performance. Eur Radiol 2009;19:552-560 https://doi.org/10.1007/s00330-008-1188-2
  57. Li F, Aoyama M, Shiraishi J, Abe H, Li Q, Suzuki K, et al. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computerestimated likelihood of malignancy. AJR Am J Roentgenol 2004;183:1209-1215 https://doi.org/10.2214/ajr.183.5.1831209
  58. Awai K, Murao K, Ozawa A, Nakayama Y, Nakaura T, Liu D, et al. Pulmonary nodules: estimation of malignancy at thinsection helical CT--effect of computer-aided diagnosis on performance of radiologists. Radiology 2006;239:276-284 https://doi.org/10.1148/radiol.2383050167
  59. Lee KW, Kim M, Gierada DS, Bae KT. Performance of a computer-aided program for automated matching of metastatic pulmonary nodules detected on follow-up chest CT. AJR Am J Roentgenol 2007;189:1077-1081 https://doi.org/10.2214/AJR.07.2057
  60. Tao C, Gierada DS, Zhu F, Pilgram TK, Wang JH, Bae KT. Automated matching of pulmonary nodules: evaluation in serial screening chest CT. AJR Am J Roentgenol 2009;192:624- 628 https://doi.org/10.2214/AJR.08.1307

Cited by

  1. Computer-Aided Detection of Malignant Lung Nodules on Chest Radiographs: Effect on Observers' Performance vol.13, pp.5, 2011, https://doi.org/10.3348/kjr.2012.13.5.564
  2. THE CUT-OFF VALUES FOR AUTO-DETECTION OF LUNG CANCER IN CHEST RADIOGRAPHY: AN EXAMPLE USING AN UNSUPERVISED METHOD vol.24, pp.6, 2011, https://doi.org/10.4015/s1016237212500482
  3. Computer-Aided Nodule Detection and Volumetry to Reduce Variability Between Radiologists in the Interpretation of Lung Nodules at Low-Dose Screening Computed Tomography vol.47, pp.8, 2011, https://doi.org/10.1097/rli.0b013e318250a5aa
  4. Pure and Part-Solid Pulmonary Ground-Glass Nodules: Measurement Variability of Volume and Mass in Nodules with a Solid Portion Less than or Equal to 5 mm vol.269, pp.2, 2011, https://doi.org/10.1148/radiol.13121849
  5. Machine Learning in Computer-Aided Diagnosis of the Thorax and Colon in CT: A Survey vol.ed96, pp.4, 2013, https://doi.org/10.1587/transinf.e96.d.772
  6. Computer-aided detection for pulmonary nodule identification: improving the radiologist‘s performance? vol.5, pp.3, 2011, https://doi.org/10.2217/iim.13.24
  7. An Engineering View on Megatrends in Radiology: Digitization to Quantitative Tools of Medicine vol.14, pp.2, 2011, https://doi.org/10.3348/kjr.2013.14.2.139
  8. A Comparison of Two Commercial Volumetry Software Programs in the Analysis of Pulmonary Ground-Glass Nodules: Segmentation Capability and Measurement Accuracy vol.14, pp.4, 2011, https://doi.org/10.3348/kjr.2013.14.4.683
  9. Advanced functional thoracic imaging in children: from basic concepts to clinical applications vol.43, pp.3, 2013, https://doi.org/10.1007/s00247-012-2466-3
  10. Extracting Fuzzy Classification Rules from Texture Segmented HRCT Lung Images vol.26, pp.2, 2011, https://doi.org/10.1007/s10278-012-9514-2
  11. Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT vol.33, pp.2, 2011, https://doi.org/10.1109/tmi.2013.2287374
  12. How Using Dedicated Software Can Improve RECIST Readings vol.1, pp.2, 2014, https://doi.org/10.3390/informatics1020160
  13. Fate of pulmonary nodules detected by computer-aided diagnosis and physician review on the computed tomography simulation images for hepatocellular carcinoma vol.32, pp.3, 2011, https://doi.org/10.3857/roj.2014.32.3.116
  14. Toward clinically usable CAD for lung cancer screening with computed tomography vol.24, pp.11, 2014, https://doi.org/10.1007/s00330-014-3329-0
  15. Pulmonary adenocarcinomas appearing as part-solid ground-glass nodules: Is measuring solid component size a better prognostic indicator? vol.25, pp.2, 2015, https://doi.org/10.1007/s00330-014-3441-1
  16. Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times vol.16, pp.2, 2015, https://doi.org/10.3348/kjr.2015.16.2.430
  17. Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas vol.26, pp.12, 2016, https://doi.org/10.1007/s00330-016-4317-3
  18. Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography vol.33, pp.4, 2011, https://doi.org/10.4103/0970-2113.184872
  19. The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer vol.20, pp.40, 2011, https://doi.org/10.3310/hta20400
  20. Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement vol.28, pp.5, 2018, https://doi.org/10.1007/s00330-017-5171-7
  21. Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma : A preliminary study vol.98, pp.25, 2011, https://doi.org/10.1097/md.0000000000016119
  22. Quantitative Computed Tomography Imaging of Lung Cancer vol.59, pp.1, 2011, https://doi.org/10.2482/haigan.59.29
  23. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies vol.29, pp.1, 2019, https://doi.org/10.1007/s00330-018-5528-6
  24. Solid Indeterminate Pulmonary Nodules of Less Than 300 mm3: Application of Different Volume Doubling Time Cut-offs in Clinical Practice vol.9, pp.2, 2019, https://doi.org/10.3390/diagnostics9020062
  25. Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors vol.9, pp.16, 2011, https://doi.org/10.3390/app9163428
  26. Lung Nodule: Imaging Features and Evaluation in the Age of Machine Learning vol.8, pp.3, 2011, https://doi.org/10.1007/s13665-019-00229-8
  27. Effect of Slab Thickness on the Detection of Pulmonary Nodules by Use of CT Maximum and Minimum Intensity Projection vol.213, pp.3, 2019, https://doi.org/10.2214/ajr.19.21325
  28. Measurement accuracy of lung nodule volumetry in a phantom study : Effect of axial-volume scan and iterative reconstruction algorithm vol.99, pp.23, 2020, https://doi.org/10.1097/md.0000000000020543
  29. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges vol.21, pp.5, 2011, https://doi.org/10.3348/kjr.2019.0821
  30. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications vol.75, pp.1, 2020, https://doi.org/10.1016/j.crad.2019.04.017
  31. Künstliche Intelligenz in der Bildgebung der Lunge vol.60, pp.1, 2020, https://doi.org/10.1007/s00117-019-00611-2
  32. What’s New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review vol.10, pp.2, 2011, https://doi.org/10.3390/diagnostics10020055
  33. Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units vol.8, pp.None, 2011, https://doi.org/10.3389/fmed.2021.676277
  34. Deep Learning-based Super-Resolution Algorithm: Potential in the Management of Subsolid Nodules vol.299, pp.1, 2011, https://doi.org/10.1148/radiol.2021204463