Lung nodule and cancer detection in computed tomography screening

Journal of Thoracic Imaging

Volumn 30, Issue 2, 2015, Pages 130-138

  Rubin, Geoffrey D ^a  

^a DUKE CLINICAL RESEARCH INSTITUTE   (United States)

Author keywords

computed tomography; computer Aided detection; eye tracking; image processing; lung cancer; lung nodule; pulmonary nodule

Indexed keywords

CANCER DIAGNOSIS; CANCER SCREENING; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONFERENCE PAPER; DIAGNOSTIC APPROACH ROUTE; DIAGNOSTIC IMAGING; HUMAN; IMAGE ANALYSIS; LUNG CANCER; LUNG NODULE; RADIOLOGIST; IMAGE PROCESSING; LUNG; LUNG TUMOR; RADIOGRAPHY; REPRODUCIBILITY;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG; LUNG NEOPLASMS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84924165241     PISSN: 08835993     EISSN: 15360237     Source Type: Journal    
DOI: 10.1097/RTI.0000000000000140     Document Type: Conference Paper

References (67)

1. Aberle DR, DeMello S, Berg CD, et al. Results of the two inciDence screenings in the National Lung Screening Trial. N Engl J Med. 2013;369:920-931
2. Rubin GD, Roos JE, Tall M, et al. Characterizing search, recognition, and Decision in the Detection of lung nodules on CT scans: elucidation with eye tracking. Radiology. 2015; 274:276-286
3. Fischbach F, Knollmann F, Griesshaber V, et al. Detection of pulmonary nodules by multislice computed tomography: improved Detection rate with reduced slice thickness. Eur Radiol. 2003;13:2378-2383
4. Sinsuat M, Saita S, Kawata Y, et al. Influence of slice thickness on diagnoses of pulmonary nodules using low-dose CT: potential DepenDence of Detection and diagnostic agreement on features and location of nodule. Acad Radiol. 2011;18:594-604
5. Kazerooni EA, Austin JHM, Black WC, et al. ACR-STR practice parameter for the performance and reporting of lung cancer screening thoracic computed tomography (CT): 2014 (Resolution 4). J Thorac Imaging. 2014;29:310-316
6. Xu DM, Gietema H, De Koning H, et al. Nodule management protocol of the NELSON randomised lung cancer screening trial. Lung Cancer. 2006;54:177-184
7. Aberle DR, Berg CD, et al. Team NLSTR. The National Lung Screening Trial: overview and study Design. Radiology. 2011;258:243-253
8. Wang XH, Durick JE, Lu A, et al. Compare display schemes for lung nodule CT screening. J Digit Imaging. 2011;24:478-484
9. Copley SJ, Bryant TH, Chambers AA, et al. Observer accuracy in the Detection of pulmonary nodules on CT: effect of cine frame rate. Clin Radiol. 2010;65:133-136
10. Seltzer SE, Judy PF, Feldman U, et al. Influence of CT image size and format on accuracy of lung nodule Detection. Radiology. 1998;206:617-622
11. Benjamin MS, Drucker EA, McLoud TC, et al. Small pulmonary nodules: Detection at chest CT and outcome. Radiology. 2003;226:489-493
12. MacMahon H, Austin JHM, Gamsu G, et al. GuiDelines for management of small pulmonary nodules Detected on CT scans: a statement from the Fleischner Society 1. Radiology. 2005;237:395-400
13. Naidich DP, Naidich DP, Bankier AA, et al. Recommendations for the management of subsolid pulmonary nodules Detected at CT: a statement from the Fleischner Society. Radiology. 2013;266:304-317
14. American College of Radiology. Lung CT Screening Reporting and Data System (Lung-RADS). Available at: http://www.acr.org/Quality-Safety/Resources/LungRADS. Accessed November 15, 2015
15. Horeweg N, Van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-Detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol. 2014. [Epub ahead of print].
16. Gierada DS, Pinsky P, Nath H, et al. Projected outcomes using different nodule sizes to Define a positive CT lung cancer screening examination. J Natl Cancer Inst. 2014; 106:11
17. Armato SG, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38:915-931
18. Armato SG, Roberts RY, Kocherginsky M, et al. Assessment of radiologist performance in the Detection of lung nodules: DepenDence on the Definition of ";truth". Acad Radiol. 2009;16:28-38
19. Naidich DP, Rusinek H, McGuinness G, et al. Variables affecting pulmonary nodule Detection with computed tomography: evaluation with three-dimensional computer simulation. J Thorac Imaging. 1993;8:291-299
20. Karadi C, Beaulieu CF, Jeffrey RB Jr, et al. Display moDes for CT colonography. Part I. Synthesis and insertion of polyps into patient CT data. Radiology. 1999;212:195-201
21. Kang MC, Kang CH, Lee HJ, et al. Accuracy of 16-channel multi-Detector row chest computed tomography with thin sections in the Detection of metastatic pulmonary nodules. Eur J Cardiothorac Surg. 2008;33:473-479
22. Cerfolio RJ, McCarty T, Bryant AS. Non-imaged pulmonary nodules discovered during thoracotomy for metastasectomy by lung palpation. Eur J Cardiothorac Surg. 2009;35:786-791; discussion 91
23. Ellis MC, Hessman CJ, Weerasinghe R, et al. Comparison of pulmonary nodule Detection rates between preoperative CT imaging and intraoperative lung palpation. Am J Surg. 2011;201:619-622
24. LeaDer JK, Warfel TE, Fuhrman CR, et al. Pulmonary nodule Detection with low-dose CT of the lung: agreement among radiologists. Am J Roentgenol. 2005;185:973-978
25. Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodules on multi-Detector row CT scans: performance comparison of radiologists and computer-AiDed Detection. Radiology. 2005; 234:274-283
26. Armato SG, McNitt-Gray MF, Reeves AP, et al. The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the iDentification of lung nodules on CT scans. Acad Radiol. 2007;14:1409-1421
27. Pinsky PF, Gierada DS, Nath PH, et al. National lung screening trial: variability in nodule Detection rates in chest CT studies. Radiology. 2013;268:865-873
28. DieDerich S, Semik M, Lentschig MG, et al. Helical CT of pulmonary nodules in patients with extrathoracic malignancy: CT-surgical correlation. Am J Roentgenol. 1999;172:353-360
29. Ko JP, Rusinek H, Naidich DP, et al. Wavelet compression of low-dose chest CT data: effect on lung nodule Detection. Radiology. 2003;228:70-75
30. Roos JE, Paik D, Olsen D, et al. Computer-AiDed Detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance. Eur Radiol. 2010;20:549-557
31. Van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules Detected by volume CT scanning. N Engl J Med. 2009;361:2221-2229
32. Horeweg N, Scholten ET, De Jong PA, et al. Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers. Lancet Oncol. 2014. [Epub ahead of print].
33. Gierada DS, Pilgram TK, Ford M, et al. Lung cancer: interobserver agreement on interpretation of pulmonary findings at low-dose CT screening. Radiology. 2008;246:265-272
34. Xu DM, Yip R, Smith JP, et al. I-ELCAP Investigators. Retrospective review of lung cancers diagnosed in annual rounds of CT screening. Am J Roentgenol. 2014;203:1-8
35. KunDel HL, Nodine CF, Carmody D. Visual scanning, pattern recognition and Decision-making in pulmonary nodule Detection. Invest Radiol. 1978;13:175-181
36. Gamer M, Hecht H. Are you looking at me? Measuring the cone of gaze. J Exp Psychol Hum Percept Perform. 2007;33:705-715
37. KunDel HL, Nodine CF, Thickman D, et al. Searching for lung nodules. A comparison of human performance with random and systematic scanning moDels. Invest Radiol. 1987;22:417-422
38. KunDel HL, Nodine CF, Toto L. Searching for lung nodules. The guidance of visual scanning. Invest Radiol. 1991;26: 777-781
39. Drew T, ML-H Vo, Olwal A, et al. Scanners and drillers: characterizing expert visual search through volumetric images. J Vis. 2013;13:10
40. Andriole KP, Wolfe JM, Khorasani R, et al. Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day. Radiology. 2011;259:346-362
41. Nodine CF, KunDel HL. Using eye movements to study visual search and to improve tumor Detection. Radiographics. 1987;7: 1241-1250
42. Tillich M, Kammerhuber F, Reittner P, et al. Detection of pulmonary nodules with helical CT: comparison of cine and film-based viewing. Am J Roentgenol. 1997;169:1611-1614
43. Rubin GD, Napel S, Leung AN. Volumetric analysis of volumetric data: achieving a paradigm shift. Radiology. 1996;200:312-317
44. Wang XH, Durick JE, Lu A, et al. Characterization of radiologists search strategies for lung nodule Detection: slicebased versus volumetric displays. J Digit Imaging. 2008; 21(suppl 1):S39-S49
45. Napel S, Rubin GD, Jeffrey RB Jr. STS-MIP: a new reconstruction technique for CT of the chest. J Comput Assist Tomogr. 1993;17:832-838
46. GruDen JF, Ouanounou S, Tigges S, et al. Incremental benefit of maximum-intensity-projection images on observer Detection of small pulmonary nodules revealed by multiDetector CT. Am J Roentgenol. 2002;179:149-157
47. Valencia R, Denecke T, Lehmkuhl L, et al. Value of axial and coronal maximum intensity projection (MIP) images in the Detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices. Eur Radiol. 2006;16:325-332
48. Scholten ET, WPTM Mali, Prokop M, et al. Non-solid lung nodules on low-dose computed tomography: comparison of Detection rate between 3 visualization techniques. Cancer Imaging. 2013;13:150-154
49. Peloschek P, Sailer J, Weber M, et al. Pulmonary nodules: sensitivity of maximum intensity projection versus that of volume renDering of 3D multiDetector CT data. Radiology. 2007;243:561-569
50. Ko JP, Betke M, Chest CT. Automated nodule Detection and assessment of change over time-preliminary experience 1. Radiology. 2001;218:267-273
51. Sun S, Rubin GD, Paik D, et al. Registration of lung nodules using a semi-rigid moDel: method and preliminary results. Med Phys. 2007;34:613-626
52. Lee KW, Kim M, Gierada DS, et al. Performance of a computer-AiDed program for automated matching of metastatic pulmonary nodules Detected on follow-up chest CT. Am J Roentgenol. 2007;189:1077-1081
53. Tao C, Gierada DS, Zhu F, et al. Automated matching of pulmonary nodules: evaluation in serial screening chest CT. Am J Roentgenol. 2009;192:624-628
54. Aoki T, Murakami S, Kim H, et al. Temporal subtraction method for lung nodule Detection on successive thoracic CT soft-copy images. Radiology. 2014;271:255-261
55. Armato SG, Li F, Giger ML, et al. Lung cancer: performance of automated lung nodule Detection applied to cancers missed in a CT screening program. Radiology. 2002;225:685-692
56. Brown MS, Goldin JG, Rogers S, et al. Computer-AiDed lung nodule Detection in CT. Acad Radiol. 2005;12:681-686
57. Das M, Mühlenbruch G, Mahnken AH, et al. Small pulmonary nodules: effect of two computer-AiDed Detection systems on radiologist performance. Radiology. 2006;241:564-571
58. White CS, Pugatch R, Koonce T, et al. Lung nodule CAD software as a second reaDer: a multicenter study. Acad Radiol. 2008;15:326-333
59. Goldin JG, Brown MS, Petkovska I. Computer-AiDed diagnosis in lung nodule assessment. J Thorac Imaging. 2008;23:97-104
60. Sahiner B, Chan H-P, Hadjiiski LM, 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
61. 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 (Torino). 2010;115:385-402
62. Christe A, Leidolt L, Huber A, et al. Lung cancer screening with CT: evaluation of radiologists and different computer assisted Detection software (CAD) as first and second reaDers for lung nodule Detection at different dose levels. Eur J Radiol. 2013;82:e873-e878
63. Li Q, Li F, Doi K. Computerized Detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol. 2008;15:165-175
64. Godoy MCB, Kim TJ, White CS, et al. Benefit of computeraiDed Detection analysis for the Detection of subsolid and solid lung nodules on thin-And thick-section CT. Am J Roentgenol. 2013;200:74-83
65. Jacobs C, Van Rikxoort EM, Twellmann T, et al. Automatic Detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal. 2014;18:374-384
66. Jeon KN, Goo JM, Lee CH, et al. Computer-AiDed nodule Detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography. Invest Radiol. 2012;47:457-461
67. Matsumoto S, Ohno Y, Aoki T, et al. Computer-AiDed Detection of lung nodules on multiDetector CT in concurrentreaDer and second-reaDer moDes: a comparative study. Eur J Radiol. 2013;82:1332-1337.