Skip to main content

Computer Aided Detection of Bone Metastases in the Thoracolumbar Spine

  • Chapter
  • First Online:
Spinal Imaging and Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 18))

Abstract

Computer-aided detection (CAD) techniques and algorithms for radiologic applications are rapidly growing in scope and sophistication. One important application of CAD techniques in medicine is in the detection and assessment of metastatic disease to the bone. Bone metastases affect approximately 400,000 patients per year in the United States. Early detection of bone metastases is important clinically, as the prognosis can change and the treatment regimen can at that point be altered from one of curative therapy to one of palliative treatment. Both lytic and sclerotic metastatic disease can act to biomechanically weaken the bone, and potentially lead to pathologic fractures. This chapter presents a framework for computer-aided detection of lytic and sclerotic metastatic lesions in the thoracolumbar spine using computed tomography (CT). State-of-art techniques are described in detail in each module of the framework. Thorough validation experiments are designed and results are presented. We also discuss the clinical significance and limitation of the CAD system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hitron A, Adams V (2009) The pharmacological management of skeletal-related events from metastatic tumors. Orthopedics 32:188

    Article  Google Scholar 

  2. Roodman GD (2004) Mechanisms of bone metastasis. N Engl J Med 350:1655–1664

    Article  Google Scholar 

  3. Guillevin R, Vallee JN, Lafitte F, Menuel C, Duverneuil NM, Chiras J (2007) Spine metastasis imaging: review of the literature. J Neuroradiol 34:311–321

    Article  Google Scholar 

  4. Lee RJ, Saylor PJ, Smith MR (2011) Treatment and prevention of bone complications from prostate cancer. Bone 48:88–95

    Article  Google Scholar 

  5. Chirgwin JM, Guise TA (2007) Skeletal metastases: decreasing tumor burden by targeting the bone microenvironment. J Cell Biochem 102:1333–1342

    Article  Google Scholar 

  6. Kinnane N (2007) Burden of bone disease. Eur J Oncol Nurs 11(Suppl 2):S28–S31

    Article  Google Scholar 

  7. Weinfurt KP, Li Y, Castel LD, Saad F, Timbie JW, Glendenning GA, Schulman KA (2005) The significance of skeletal-related events for the health-related quality of life of patients with metastatic prostate cancer. Ann Oncol 16:579–584

    Article  Google Scholar 

  8. Saad F, Lipton A, Cook R, Chen YM, Smith M, Coleman R (2007) Pathologic fractures correlate with reduced survival in patients with malignant bone disease. Cancer 110:1860–1867

    Article  Google Scholar 

  9. Bilsky MH, Lis E, Raizer J, Lee H, Boland P (1999) The diagnosis and treatment of metastatic spinal tumor. Oncologist 4:459–469

    Google Scholar 

  10. Colman LK, Porter BA, Redmond J, Olson DO, Stimac GK, Dunning DM, Friedl KE (1988) Early diagnosis of spinal metastases by CT and MR studies. J Comput Assist Tomogr 12:423–426

    Google Scholar 

  11. Coleman RE (1998) Monitoring of bone metastases. Eur J Cancer 34:252–259

    Article  Google Scholar 

  12. Beheshti M, Vali R, Waldenberger P, Fitz F, Nader M, Hammer J, Loidl W, Pirich C, Fogelman I, Langsteger W (2009) The use of F-18 choline PET in the assessment of bone metastases in prostate cancer: correlation with morphological changes on CT. Mol Imaging Biol 12:98–107

    Google Scholar 

  13. Mundy GR (2002) Metastasis to bone: causes, consequences and therapeutic opportunities. Nat Rev Cancer 2:584–593

    Article  Google Scholar 

  14. Guise TA, Mundy GR (1998) Cancer and bone. Endocr Rev 19:18–54

    Google Scholar 

  15. Keller ET, Brown J (2004) Prostate cancer bone metastases promote both osteolytic and osteoblastic activity. J Cell Biochem 91:718–729

    Article  Google Scholar 

  16. Saylor PJ, Smith MR (2009) Bone health and prostate cancer. Prostate Cancer Prostatic Dis 13:20–27

    Article  Google Scholar 

  17. Muindi J, Coombes RC, Golding S, Powles TJ, Khan O, Husband J (1983) The role of computed tomography in the detection of bone metastases in breast cancer patients. Br J Radiol 56:233–236

    Article  Google Scholar 

  18. Sundaram M, McGuire MH (1988) Computed tomography or magnetic resonance for evaluating the solitary tumor or tumor-like lesion of bone? Skeletal Radiol 17:393–401

    Google Scholar 

  19. Jeschke S, Schweigreiter E, Janetschek G (2009) Role of imaging in prostate cancer. Imaging Decisions MRI 13:68–87 (Fall/Winter 2009)

    Google Scholar 

  20. Li J, Yao J, Summers RM, Petrick N, Manry MT, Hara AK (2006) An efficient feature selection algorithm for computer-aided polyp detection. Int J Artif Intell Tools (IJAIT) 15:893–915

    Article  Google Scholar 

  21. Yao J, Dwyer A, Summers R, Mollura D (2011) Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification. Acad Radiol 18:306–314

    Article  Google Scholar 

  22. Chakraborty D (2000) The FROC, AFROC and DROC variants of the ROC analysis. In: Beutel J, Kundel H, Van Metter R (eds) Handbook of medical imaging. SPIE Press, Bellingham, pp 771–796

    Google Scholar 

  23. Irwig L, Houssami N, van Vliet C (2004) New technologies in screening for breast cancer: a systematic review of their accuracy. Br J Cancer 90:2118–2122

    Google Scholar 

  24. Hadjiiski L, Chan HP, Sahiner B, Helvie MA, Roubidoux MA, Blane C, Paramagul C, Petrick N, Bailey J, Klein K, Foster M, Patterson S, Adler D, Nees A, Shen J (2004) Improvement in radiologists’ characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study. Radiology 233:255–265

    Article  Google Scholar 

  25. Awai K, Murao K, Ozawa A, Komi M, Hayakawa H, Hori S, Nishimura Y (2004) Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’ detection performance. Radiology 230:347–352

    Article  Google Scholar 

  26. Kakeda S, Moriya J, Sato H, Aoki T, Watanabe H, Nakata H, Oda N, Katsuragawa S, Yamamoto K, Doi K (2004) Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol 182:505–510

    Article  Google Scholar 

  27. Summers RM, Jerebko AK, Franaszek M, Malley JD, Johnson CD (2002) Colonic polyps: complementary role of computer-aided detection in CT colonography. Radiology 225:391–399

    Article  Google Scholar 

  28. O’Connor SD, Yao J, Summers RM (2007) Lytic metastases in thoracolumbar spine: computer aided detection at CT—a preliminary study. Radiology 242:811–816

    Article  Google Scholar 

  29. Burns J, Yao J, Wiese T, Munoz H, Jones E, Summers R (2013) Detection of sclerotic metastases in the thoracolumbar spine on computed tomography. Radiology 268:69–78

    Article  Google Scholar 

  30. Yao J, Burns JE, Muñoz H, Summers RM (2012) Detection of vertebral body fractures based on cortical shell unwrapping. In: Proceedings of the 15th international conference on medical image computing and computer assisted intervention, Nice, France, pp 509–516

    Google Scholar 

  31. Yao J, Muñoz HE, Burns JE, Lu L, Kurdziel K, Choyke P, Summers RM (2013) Computer aided detection of spinal degenerative osteophytes on sodium fluoride PET/CT. In: MICCAI workshop, computational methods and clinical applications for spine imaging, Nagoya, Japan

    Google Scholar 

  32. Tan S, Yao J, Ward M (2008) Computer aided evaluation of ankylosing spondylitis using high-resolution CT. IEEE Trans Med Imaging 27:1252–1267

    Article  Google Scholar 

  33. Pattanaik S, Liu J, Yao J, Zhang W, Turkbey E, Zhang X, Summers R (2013) Epidural masses detection on computed tomography using spatially-constrained gaussian mixture models. In: MICCAI workshop, computational methods and clinical applications for spine imaging, Nagoya, Japan

    Google Scholar 

  34. Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Machine Intell 13:583–598

    Article  Google Scholar 

  35. Ibanez L, Schroeder W (2003) ITK software guide. Kitware Inc., New York

    Google Scholar 

  36. Cormen TH, Leiserson CE, Rivest RL (1989) Introduction to algorithms. Mac Graw Hill, New York

    Google Scholar 

  37. Wolberg G (1990) Digital image warping. IEEE Computer Society Press Monograph

    Google Scholar 

  38. Ghebreab S, Smeulders AWM (2004) Combining strings and necklaces for interactive three-dimensional segmentation of spinal images using an integral deformable spine model. IEEE Trans Biomed Eng 51:1821–1829

    Article  Google Scholar 

  39. Heitz G, Rohlfing T, Maurer CRJ (2005) Statistical shape model generation using nonrigid deformation of a template mesh. In: SPIE, San Diego, CA

    Google Scholar 

  40. Verdonck B, Nijlunsing R, Gerritsen FA, Cheung J, Wever DJ, Veldhuizen A, Devillers S, Makeram-Ebeid S (1998) Computer assisted quantitative analysis of deformities of the human spine. In: MICCAI, Cambridge, MA, pp 822–831

    Google Scholar 

  41. Pizer SM, Thall AL, Chen DT (1999) M-Reps: a new object representation for graphics. University of North Carolina, Chapel Hill TR99-030

    Google Scholar 

  42. Vrtovec T, Likar B, Pernus F (2005) Automated curved planar reformation of 3D spine images. Phys Med Biol 50:4527–4540

    Article  Google Scholar 

  43. Stawiaski J, Decencière E (2008) Region merging via graph-cuts. Image Anal Stereol 27:39–45

    Article  MATH  Google Scholar 

  44. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Pattern Anal Mach Intell 26:1124–1137

    Article  Google Scholar 

  45. Sethian JA (1999) Level set methods and fast marching methods. Cambridge University Press, Cambridge

    Google Scholar 

  46. Bankman IN, Spisz TS, Ravlopoulos S (2000) Two-dimensional shape and texture quantification. In: Bankman IN (ed) Handbook of medical imaging, processing and analysis. Academic Press, New York, pp 215–230

    Google Scholar 

  47. Baxt WG (1995) Application of artificial neural networks to clinical medicine. The Lancet 346:1135–1138

    Article  Google Scholar 

  48. Cristianini N, Taylor JS (2000) An introduction to support vector machines. Cambridge University Press, Cambridge

    Google Scholar 

  49. Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16:933–951

    Article  Google Scholar 

  50. Bauer E, Kohavi R (1998) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:1–38

    Google Scholar 

  51. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MATH  MathSciNet  Google Scholar 

  52. Yao J, Campbell S, Hara AK, Summers RM (2004) Progressive feature vector selection scheme for computer aided colonic polyp detection. In: RSNA 2004, Chicago, IL

    Google Scholar 

  53. Yao J, Summers RM, Hara AK (2005) Optimizing the support vector machines (SVM) committee configuration in colonic polyp CAD system. In: SPIE medical imaging, San Diego, CA

    Google Scholar 

  54. Metz C, Herman B, Roe C (1998) Statistical comparison of two ROC curve estimates obtained from partially-paired datasets. Med Decis Making 18:110–121

    Article  Google Scholar 

  55. Jianhua Y, O’Connor SD, Summers RM (2006) Automated spinal column extraction and partitioning. In: 3rd IEEE international symposium on biomedical imaging: nano to macro, 2006, pp 390–393

    Google Scholar 

  56. Soderlund V (1996) Radiological diagnosis of skeletal metastases. Eur Radiol 6:587–595

    Article  Google Scholar 

  57. Rybak LD, Rosenthal DI (2001) Radiological imaging for the diagnosis of bone metastases. Q J Nucl Med 45:53–64

    Google Scholar 

  58. Hamaoka T, Madewell JE, Podoloff DA, Hortobagyi GN, Ueno NT (2004) Bone imaging in metastatic breast cancer. J Clin Oncol 22:2942–2953

    Article  Google Scholar 

  59. Rosenthal DI (1997) Radiologic diagnosis of bone metastases. Cancer 80:1595–1607

    Article  Google Scholar 

  60. Kagan AR, Bassett LW, Steckel RJ, Gold RH (1986) Radiologic contributions to cancer management. Bone metastases. AJR Am J Roentgenol 147:305–312

    Article  Google Scholar 

  61. Coleman RE, Rubens RD (1985) Bone metastases and breast cancer. Cancer Treat Rev 12:251–270

    Article  Google Scholar 

  62. Tryciecky EW, Gottschalk A, Ludema K (1997) Oncologic imaging: interactions of nuclear medicine with CT and MRI using the bone scan as a model. Semin Nucl Med 27:142–151

    Article  Google Scholar 

  63. Daffner RH, Lupetin AR, Dash N, Deeb ZL, Sefczek RJ, Schapiro RL (1986) MRI in the detection of malignant infiltration of bone marrow. AJR Am J Roentgenol 146:353–358

    Article  Google Scholar 

  64. Metser U, Lerman H, Blank A, Lievshitz G, Bokstein F, Even-Sapir E (2004) Malignant involvement of the spine: assessment by 18F-FDG PET/CT. J Nucl Med 45:279–284

    Google Scholar 

  65. Algra PR, Heimans JJ, Valk J, Nauta JJ, Lachniet M, Van Kooten B (1992) Do metastases in vertebrae begin in the body or the pedicles? Imaging study in 45 patients. AJR Am J Roentgenol 158:1275–1279

    Article  Google Scholar 

  66. Even-Sapir E, Martin RH, Barnes DC, Pringle CR, Iles SE, Mitchell MJ (1993) Role of SPECT in differentiating malignant from benign lesions in the lower thoracic and lumbar vertebrae. Radiology 187:193–198

    Article  Google Scholar 

  67. Shah AN, Pietrobon R, Richardson WJ, Myers BS (2003) Patterns of tumor spread and risk of fracture and epidural impingement in metastatic vertebrae. J Spinal Disord Tech 16:83–89

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianhua Yao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yao, J., Burns, J.E., Summers, R.M. (2015). Computer Aided Detection of Bone Metastases in the Thoracolumbar Spine. In: Li, S., Yao, J. (eds) Spinal Imaging and Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-12508-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12508-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12507-7

  • Online ISBN: 978-3-319-12508-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics