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Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer

  • Kenji SuzukiEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)

Abstract

Computer-aided diagnosis (CAD) for detection of lesions in medical images has been an active area of research. Machine learning plays an essential role in CAD, because representing lesions and organs requires a complex model that has a number of parameters to determine; thus, medical pattern recognition essentially requires “learning from examples” to determine the parameters of the model. Machine learning has been used to classify lesions into certain classes (e.g., abnormal or normal, lesions or non-lesions, and malignant or benign) in CAD. Recently, as available computational power increased dramatically, pixel/voxel-based machine learning (PML) has emerged in medical image processing/analysis, which uses pixel/voxel values in local regions (or patches) in images instead of features calculated from segmented regions as input information; thus, feature calculation or segmentation is not required. Because PML can avoid errors caused by inaccurate feature calculation and segmentation, the performance of PML can potentially be higher than that of common classifiers. In this chapter, MTANNs (a class of PML) in CAD schemes for detection of lung nodules in CT and for detection of polyps in CTC are presented.

Keywords

Artificial Neural Network Support Vector Regression Lung Nodule Nodule Candidate Lung Cancer Screening 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work would not have been possible without the help of countless individuals. The author acknowledges the invaluable assistance of all colleagues and support staff. The author is grateful to all members in the Suzuki Laboratory in the Department of Radiology at the University of Chicago, especially Ivan Sheu, Mark L Epstein, Jianwu Xu, and Sheng Chen, for their contributions to the studies, to colleagues and collaborators, especially Abraham H Dachman, Heber MacMahon, Kunio Doi, Samuel G Armato III, Feng Li, Shusuke Sone, Hiroyuki Abe, Qiang Li, Junji Shiraishi, Don C Rockey, Hiroyuki Yoshida, and Janne Nappi for their valuable suggestions, and to Ms. E. F. Lanzl for improving the chapter. The author is also grateful to his wife, Harumi Suzuki, for her assistance with the chapter and studies, and his daughters, Mineru Suzuki and Juno Suzuki, for cheering him up. This work was partly supported by Grant Number R01CA120549 from the National Cancer Institute/National Institutes of Health and by NIH S10 RR021039 and P30 CA14599.

References

  1. 1.
    Giger ML, Suzuki K (2007) Computer-aided diagnosis (CAD). In: Feng DD (ed) Biomedical information technology. Academic Press, New York, pp 359–374Google Scholar
  2. 2.
    Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78(1):S3–S19Google Scholar
  3. 3.
    Li F, Aoyama M, Shiraishi J, Abe H, Li Q et al (2004) Radiologists’ performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. Am J Roentgenol 183(5):1209–1215CrossRefGoogle Scholar
  4. 4.
    Li F, Arimura H, Suzuki K, Shiraishi J, Li Q et al (2005) Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 237(2):684–690CrossRefGoogle Scholar
  5. 5.
    Dean JC, Ilvento CC (2006) Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers. Am J Roentgenol 187(1):20–28CrossRefGoogle Scholar
  6. 6.
    Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K (2005) False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol 12(2):191–201Google Scholar
  7. 7.
    van Ginneken B, ter Haar Romeny BM, Viergever MA (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 20(12):1228–1241Google Scholar
  8. 8.
    Giger ML, Doi K, MacMahon H (1988) Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys 15(2):158–166Google Scholar
  9. 9.
    Suzuki K, Armato SG, Li F, Sone S, Doi K (2003) Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Med Phys 30(7):1602–1617Google Scholar
  10. 10.
    Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J et al (2004) Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol 11(6):617–629CrossRefGoogle Scholar
  11. 11.
    Armato SG 3rd, Giger ML, Moran CJ, Blackburn JT, Doi K et al (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19(5):1303–1311Google Scholar
  12. 12.
    Armato SG 3rd, Li F, Giger ML, MacMahon H, Sone S et al (2002) Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 225(3):685–692CrossRefGoogle Scholar
  13. 13.
    Chan HP, Doi K, Galhotra S, Vyborny CJ, MacMahon H et al (1987) Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med Phys 14(4):538–548Google Scholar
  14. 14.
    Gilhuijs KG, Giger ML, Bick U (1998) Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 25(9):1647–1654CrossRefGoogle Scholar
  15. 15.
    Horsch K, Giger ML, Vyborny CJ, Venta LA (2004) Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Acad Radiol 11(3):272–280CrossRefGoogle Scholar
  16. 16.
    Drukker K, Giger ML, Metz CE (2005) Robustness of computerized lesion detection and classification scheme across different breast US platforms. Radiology 237(3):834–840CrossRefGoogle Scholar
  17. 17.
    Yoshida H, Nappi J (2001) Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 20(12):1261–1274CrossRefGoogle Scholar
  18. 18.
    Suzuki K, Yoshida H, Nappi J, Dachman AH (2006) Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Med Phys 33(10):3814–3824CrossRefGoogle Scholar
  19. 19.
    Suzuki K, Yoshida H, Nappi J, Armato SG 3rd, Dachman AH (2008) Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med Phys 35(2):694–703CrossRefGoogle Scholar
  20. 20.
    Lostumbo A, Wanamaker C, Tsai J, Suzuki K, Dachman AH (2010) Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography. Acad Radiol 17(1):39–47CrossRefGoogle Scholar
  21. 21.
    Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, San DiegoGoogle Scholar
  22. 22.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536CrossRefGoogle Scholar
  23. 23.
    Vapnik N (1995) The nature of statistical learning theory. Springer, BerlinGoogle Scholar
  24. 24.
    Shiraishi J, Li Q, Suzuki K, Engelmann R, Doi K (2006) Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med Phys 33(7):2642–2653Google Scholar
  25. 25.
    Armato SG 3rd, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28(8):1552–1561CrossRefGoogle Scholar
  26. 26.
    Aoyama M, Li Q, Katsuragawa S, MacMahon H, Doi K (2002) Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med Phys 29(5):701–708Google Scholar
  27. 27.
    Aoyama M, Li Q, Katsuragawa S, Li F, Sone S et al (2003) Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med Phys 30(3):387–394CrossRefGoogle Scholar
  28. 28.
    Jerebko K, Summers RM, Malley JD, Franaszek M, Johnson CD (2003) Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. Med Phys 30(1):52–60CrossRefGoogle Scholar
  29. 29.
    Suzuki K, Horiba I, Sugie N (2002) Efficient approximation of neural filters for removing quantum noise from images. IEEE Trans Signal Process 50(7):1787–1799CrossRefGoogle Scholar
  30. 30.
    Suzuki K, Horiba I, Sugie N (2003) Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans Pattern Anal Mach Intell 25(12):1582–1596CrossRefGoogle Scholar
  31. 31.
    Suzuki K, Horiba I, Sugie N, Nanki M (2004) Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans Med Imaging 23(3):330–339CrossRefGoogle Scholar
  32. 32.
    Suzuki K, Li F, Sone S, Doi K (2005) Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging 24(9):1138–1150Google Scholar
  33. 33.
    Suzuki K, Abe H, MacMahon H, Doi K (2006) Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging 25(4):406–416Google Scholar
  34. 34.
    Suzuki K, Rockey DC, Dachman AH (2010) CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Med Phys 30:2–21Google Scholar
  35. 35.
    Suzuki K, Zhang J, Xu J (2010) Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 29(11):1907–1917CrossRefGoogle Scholar
  36. 36.
    Xu J, Suzuki K (2011) Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Med Phys 38(4):1888–1902 Google Scholar
  37. 37.
    Suzuki K, Horiba I, Sugie N, Nanki M (2002) Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Trans Inf Syst E85-D(10):1710–1718Google Scholar
  38. 38.
    Lo SB, Lou SA, Lin JS, Freedman MT, Chien MV et al (1995) Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging 14(4):711–718CrossRefGoogle Scholar
  39. 39.
    Lo SCB, Chan HP, Lin JS, Li H, Freedman MT et al (1995) Artificial convolution neural network for medical image pattern recognition. Neural Netw 8(7–8):1201–1214CrossRefGoogle Scholar
  40. 40.
    Lin JS, Lo SB, Hasegawa A, Freedman MT, Mun SK (1996) Reduction of false positives in lung nodule detection using a two-level neural classification. IEEE Trans Med Imaging 15(2):206–217CrossRefGoogle Scholar
  41. 41.
    Lo SC, Li H, Wang Y, Kinnard L, Freedman MT (2002) A multiple circular path convolution neural network system for detection of mammographic masses. IEEE Trans Med Imaging 21(2):150–158CrossRefGoogle Scholar
  42. 42.
    Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA et al (1996) Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5):598–610CrossRefGoogle Scholar
  43. 43.
    Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113CrossRefGoogle Scholar
  44. 44.
    Neubauer C (1998) Evaluation of convolutional neural networks for visual recognition. IEEE Trans Neural Netw 9(4):685–696CrossRefGoogle Scholar
  45. 45.
    Wei D, Nishikawa RM, Doi K (1996) Application of texture analysis and shift-invariant artificial neural network to microcalcification cluster detection. Radiology 201:696–696Google Scholar
  46. 46.
    Zhang W, Doi K, Giger ML, Nishikawa RM, Schmidt RA (1996) An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Med Phys 23(4):595–601Google Scholar
  47. 47.
    Zhang W, Doi K, Giger ML, Wu Y, Nishikawa RM et al (1994) Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med Phys 21(4):517–524Google Scholar
  48. 48.
    Suzuki K, Armato SG 3rd, Li F, Sone S, Doi K (2003) Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys 30(7):1602–1617Google Scholar
  49. 49.
    Oda S, Awai K, Suzuki K, Yanaga Y, Funama Y et al (2009) Performance of radiologists in detection of small pulmonary nodules on chest radiographs: effect of rib suppression with a massive-training artificial neural network. Am J Roentgenol 193(5):W397–402Google Scholar
  50. 50.
    Suzuki K (2009) A supervised ‘lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys Med Biol 54(18):S31–45Google Scholar
  51. 51.
    Xu JW, Suzuki K (2010 ) False-positive reduction in computer-aided detection of polyps in CT colonography: a massive-training support vector regression approach. In: MICCAI workshop on computational challenges and clinical opportunities in virtual colonoscopy and abdominal imaging, Beijing, China pp. 55–60Google Scholar
  52. 52.
    Mosier CI (1951) Problems and designs of cross-validation. Educ Psychol Meas 11:5–11CrossRefGoogle Scholar
  53. 53.
    Jemal A, Murray T, Ward E, Samuels A, Tiwari RC et al (2005) Cancer statistics, 2005. CA Cancer J Clin 55(1):10–30Google Scholar
  54. 54.
    Cancer Facts and Figures (2005) American Cancer Society, AtlantaGoogle Scholar
  55. 55.
    Flehinger BJ, Kimmel M, Melamed MR (1992) The effect of surgical treatment on survival from early lung cancer. Implications for screening. Chest 101(4):1013–1018CrossRefGoogle Scholar
  56. 56.
    Sobue T, Suzuki T, Matsuda M, Kuroishi T, Ikeda S et al (1992) Survival for clinical stage I lung cancer not surgically treated. Comparison between screen-detected and symptom-detected cases. The Japanese Lung Cancer Screening Research Group. Cancer 69(3):685–692CrossRefGoogle Scholar
  57. 57.
    Miettinen S (2000) Screening for lung cancer. Radiol Clin North Am 38(3):479–486MathSciNetCrossRefGoogle Scholar
  58. 58.
    Heelan RT, Flehinger BJ, Melamed MR, Zaman MB, Perchick WB et al (1984) Non-small-cell lung cancer: results of the New York screening program. Radiology 151(2):289–293Google Scholar
  59. 59.
    Frost JK, Ball WC Jr, Levin ML, Tockman MS, Baker RR et al (1984) Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Johns Hopkins study. Am Rev Respir Dis 130(4):549–554Google Scholar
  60. 60.
    Flehinger BJ, Melamed MR, Zaman MB, Heelan RT, Perchick WB et al (1984) Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Memorial Sloan-Kettering study. Am Rev Respir Dis 130(4):555–560Google Scholar
  61. 61.
    Fontana RS, Sanderson DR, Taylor WF, Woolner LB, Miller WE et al (1984) Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Mayo Clinic study. Am Rev Respir Dis 130(4):561–565Google Scholar
  62. 62.
    Kubik A, Polak J (1986) Lung cancer detection. Results of a randomized prospective study in Czechoslovakia. Cancer 57(12):2427–2437CrossRefGoogle Scholar
  63. 63.
    Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G et al (1999) Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354(9173):99–105CrossRefGoogle Scholar
  64. 64.
    Miettinen S, Henschke CI (2001) CT screening for lung cancer: coping with nihilistic recommendations. Radiology 221(3):592–596CrossRefGoogle Scholar
  65. 65.
    Henschke CI, Naidich DP, Yankelevitz DF, McGuinness G, McCauley DI et al (2001) Early lung cancer action project: initial findings on repeat screenings. Cancer 92(1):153–159CrossRefGoogle Scholar
  66. 66.
    Swensen SJ, Jett JR, Hartman TE, Midthun DE, Sloan JA et al (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226(3):756–761CrossRefGoogle Scholar
  67. 67.
    Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T et al (1996) Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201(3):798–802Google Scholar
  68. 68.
    Sone S, Takashima S, Li F, Yang Z, Honda T et al (1998) Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet 351(9111):1242–1245CrossRefGoogle Scholar
  69. 69.
    Sone S, Li F, Yang ZG, Honda T, Maruyama Y et al (2001) Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. Br J Cancer 84(1):25–32CrossRefGoogle Scholar
  70. 70.
    Nawa T, Nakagawa T, Kusano S, Kawasaki Y, Sugawara Y et al (1996) Lung cancer screening using low-dose spiral CT: results of baseline and 1-year follow-up studies. Chest 122(1):15–20CrossRefGoogle Scholar
  71. 71.
    Gurney JW (1996) Missed lung cancer at CT: imaging findings in nine patients. Radiology 199(1):117–122MathSciNetGoogle Scholar
  72. 72.
    Li F, Sone S, Abe H, MacMahon H, Armato SG 3rd et al (2002) Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology 225(3):673–683CrossRefGoogle Scholar
  73. 73.
    Kobayashi T, Xu XW, MacMahon H, Metz CE, Doi K (1996) Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs. Radiology 199(3):843–848Google Scholar
  74. 74.
    Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 9(1):62–66MathSciNetCrossRefGoogle Scholar
  75. 75.
    Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183–192CrossRefGoogle Scholar
  76. 76.
    Suzuki K, Horiba I, Sugie N (2001) A simple neural network pruning algorithm with application to filter synthesis. Neural Process Lett 13(1):43–53CrossRefzbMATHGoogle Scholar
  77. 77.
    Suzuki K (2004) Determining the receptive field of a neural filter. J Neural Eng 1(4):228–237CrossRefGoogle Scholar
  78. 78.
    Suzuki K, Doi K (2005) How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Acad Radiol 12(10):1333–1341Google Scholar
  79. 79.
    Egan JP, Greenberg GZ, Schulman AI (1961) Operating characteristics, signal detectability, and the method of free response. J Acoust Soc Am 33:993–1007CrossRefGoogle Scholar
  80. 80.
    Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30(8):2040–2051Google Scholar
  81. 81.
    Winawer SJ, Fletcher RH, Miller L, Godlee F, Stolar MH et al (1997) Colorectal cancer screening: clinical guidelines and rationale. Gastroenterology 112(2):594–642CrossRefGoogle Scholar
  82. 82.
    Dachman H (2003) Atlas of virtual colonoscopy. Springer, New YorkCrossRefGoogle Scholar
  83. 83.
    Macari M, Bini EJ (2005) CT colonography: where have we been and where are we going? Radiology 237(3):819–833CrossRefGoogle Scholar
  84. 84.
    Fletcher JG, Booya F, Johnson CD, Ahlquist D (2005) CT colonography: unraveling the twists and turns. Curr Opin Gastroenterol 21(1):90–98Google Scholar
  85. 85.
    Yoshida H, Dachman AH (2005) CAD techniques, challenges, and controversies in computed tomographic colonography. Abdom Imaging 30(1):26–41CrossRefGoogle Scholar
  86. 86.
    Johnson CD, Dachman AH (2000) CT colonography: the next colon screening examination? Radiology 216(2):331–341CrossRefGoogle Scholar
  87. 87.
    Nappi J, Yoshida H (2003) Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography. Med Phys 30(7):1592–1601CrossRefGoogle Scholar
  88. 88.
    Frimmel H, Nappi J, Yoshida H (2004) Fast and robust computation of colon centerline in CT colonography. Med Phys 31(11):3046–3056CrossRefGoogle Scholar
  89. 89.
    Yoshida H, Masutani Y, MacEneaney P, Rubin DT, Dachman AH (2002) Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. Radiology 222(2):327–336CrossRefGoogle Scholar
  90. 90.
    Kupinski MA, Edwards DC, Giger ML, Metz CE (2001) Ideal observer approximation using Bayesian classification neural networks. IEEE Trans Med Imaging 20(9):886–899CrossRefGoogle Scholar
  91. 91.
    Nappi J, Yoshida H (2002) Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Acad Radiol 9(4):386–397CrossRefGoogle Scholar
  92. 92.
    Suzuki K, Armato SG, Li F, Sone S, Doi K (2003) Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. In: Proceedings of SPIE Medical Imaging (SPIE MI), San Diego, CA, pp 1355–1366Google Scholar
  93. 93.
    Rockey DC, Paulson E, Niedzwiecki D, Davis W, Bosworth HB et al (2005) Analysis of air contrast barium enema, computed tomographic colonography, and colonoscopy: prospective comparison. Lancet 365(9456):305–311Google Scholar
  94. 94.
    Doshi T, Rusinak D, Halvorsen RA, Rockey DC, Suzuki K et al (2007) CT colonography: false-negative interpretations. Radiology 244(1):165–173CrossRefGoogle Scholar
  95. 95.
    Edwards DC, Kupinski MA, Metz CE, Nishikawa RM (2002) Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys 29(12):2861–2870CrossRefGoogle Scholar
  96. 96.
    Rembacken BJ, Fujii T, Cairns A, Dixon MF, Yoshida S et al (2000) Flat and depressed colonic neoplasms: a prospective study of 1000 colonoscopies in the UK. Lancet 355(9211):1211–1214CrossRefGoogle Scholar
  97. 97.
    Soetikno RM, Kaltenbach T, Rouse RV, Park W, Maheshwari A et al (2008) Prevalence of nonpolypoid (flat and depressed) colorectal neoplasms in asymptomatic and symptomatic adults. JAMA 299(9):1027–1035CrossRefGoogle Scholar
  98. 98.
    Soetikno R, Friedland S, Kaltenbach T, Chayama K, Tanaka S (2006) Nonpolypoid (flat and depressed) colorectal neoplasms. Gastroenterology 130(2):566–576; quiz 588–589Google Scholar
  99. 99.
    Kudo S, Kashida H, Tamura T (2000) Early colorectal cancer: flat or depressed type. J Gastroenterol Hepatol 15(Suppl):D66–70Google Scholar
  100. 100.
    Kudo S, Kashida H, Tamura T, Kogure E, Imai Y et al (2000) Colonoscopic diagnosis and management of nonpolypoid early colorectal cancer. World J Surg 24(9):1081–1090CrossRefGoogle Scholar
  101. 101.
    Ross S, Waxman I (2006) Flat and depressed neoplasms of the colon in Western populations. Am J Gastroenterol 101(1):172–180CrossRefGoogle Scholar
  102. 102.
    Fujii T, Rembacken BJ, Dixon MF, Yoshida S, Axon AT (1998) Flat adenomas in the United Kingdom: are treatable cancers being missed? Endoscopy 30(5):437–443CrossRefGoogle Scholar
  103. 103.
    Johnson CD, Chen MH, Toledano AY, Heiken JP, Dachman A et al (2008) Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med 359(12):1207–1217CrossRefGoogle Scholar
  104. 104.
    Fidler JL, Johnson CD, MacCarty RL, Welch TJ, Hara AK et al (2002) Detection of flat lesions in the colon with CT colonography. Abdom Imaging 27(3):292–300CrossRefGoogle Scholar
  105. 105.
    Fidler J, Johnson C (2008) Flat polyps of the colon: accuracy of detection by CT colonography and histologic significance. Abdom ImagingGoogle Scholar
  106. 106.
    Taylor SA, Suzuki N, Beddoe G, Halligan S (2008) Flat neoplasia of the colon: CT colonography with CAD. Abdom Imaging 34(2):173–181Google Scholar
  107. 107.
    Lostumbo A, Suzuki K, Dachman AH (2009) Flat lesions in CT colonography. Abdom Imaging 34(2):173–181Google Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Radiology, Division of Biological SciencesThe University of ChicagoChicagoUSA

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