Skip to main content
Have a personal or library account? Click to login
Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach Cover

Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach

Radiology and Oncology
Volume 48 (2014): Issue 2 (June 2014)
By: ,  ,  ,   and    
Open Access
|Apr 2014
References

References

  1. 1. Zolal A, Hejcl A, Malucelli A, Novakova M, Vachata P, Bartos R, et al. Distant white-matter diffusion changes caused by tumor growth. J Neuroradiol 2013;71-80.
    Open DOISearch in Google Scholar
  2. 2. Rees JH, Smirniotopoulos JG, Jones RV, Wong K. Glioblastoma multiforme: radiologic-pathologic correlation. Radiographics 1996;1413-38.
    Open DOISearch in Google Scholar
  3. 3. Watanabe M, Tanaka R, Takeda N. Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 19 92;463-9.
    Open DOISearch in Google Scholar
  4. 4. Zimmerman RA. Imaging of adult central nervous system primary malignant gliomas. Staging and follow-up. Cancer 1991 ;1278-83.
    Open DOISearch in Google Scholar
  5. 5. Johnson PC, Hunt SJ, Drayer BP. Human cerebral gliomas: correlation of postmortem MR imaging and neuropathologic findings. Radiology 198 9;211-7.
    Open DOISearch in Google Scholar
  6. 6. DeAngelis LM. Brain tumors. N Engl J Med 2001 ;114-23.
    Open DOISearch in Google Scholar
  7. 7. Smets T, Lawson TM, Grandin C, Jankovski A, Raftopoulos C. Immediate post-operative MRI suggestive of the site and timing of glioblastoma recurrence after gross total resection: a retrospective longitudinal preliminary study. Eur Radiol 2013 ;1467-77.
    Open DOISearch in Google Scholar
  8. 8. De Bonis P, Anile C, Pompucci A, Fiorentino A, Balducci M, Chiesa S, et al.The influence of surgery on recurrence pattern of glioblastoma. Clin Neurol Neurosurg 201 3;37-43.
    Open DOISearch in Google Scholar
  9. 9. Young GS. Advanced MRI of adult brain tumors. Neurol Clin 200 7;947-73.
    Open DOISearch in Google Scholar
  10. 10. Cortez-Conradis D, Favila R, Isaac-Olive K, Martinez-Lopez M, Rios C, Roldan- Valadez E. Diagnostic performance of regional DTI-derived tensor metrics in glioblastoma multiforme: simultaneous evaluation of p, q, L, Cl, Cp, Cs, RA, RD, AD, mean diffusivity and fractional anisotropy. Eur Radiol 2013 ;1112-21.
    Open DOISearch in Google Scholar
  11. 11. Chaudhry NS, Shah AH, Ferraro N, Snelling BM, Bregy A, Madhavan K, et al. Predictors of long-term survival in patients with glioblastoma multiforme: advancements from the last quarter century. Cancer Invest 2013 ;287-308.
    Open DOISearch in Google Scholar
  12. 12. Lopez-Acevedo ML, Martinez-Lopez M, Favila R, Roldan-Valadez E. Secondary MRI-findings, volumetric and spectroscopic measurements in mesial temporal sclerosis: a multivariate discriminant analysis. Swiss Med Wkly 2012 ;w13549.
    Open DOISearch in Google Scholar
  13. 13. Toh CH, Wei KC, Ng SH, Wan YL, Lin CP, Castillo M. Differentiation of brain abscesses from necrotic glioblastomas and cystic metastatic brain tumors with diffusion tensor imaging. Am J Neuroradiol 2011 ;1646-51.
    Open DOISearch in Google Scholar
  14. 14. Wang W, Steward CE, Desmond PM. Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. Am J Neuroradiol 20 09;203-8.
    Open DOISearch in Google Scholar
  15. 15. Rorden C, Karnath HO, Bonilha L. MRIcron dicom to nifti converter. In:Accessed on June 07, 2012.
    Search in Google Scholar
  16. 16. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen- Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 200 4:208-21.
    Open DOISearch in Google Scholar
  17. 17. Smith SM. Fast robust automated brain extraction. Hum Brain Mapping 2002;143-55.
    Open DOISearch in Google Scholar
  18. 18. Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 1990 ;439-45.
    Open DOISearch in Google Scholar
  19. 19. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;534-46.
    Open DOISearch in Google Scholar
  20. 20. Brown SR, Gregory WM, Twelves CJ, Buyse M, Collinson F, Parmar M, et al. Designing phase II trials in cancer: a systematic review and guidance. Br J Cancer 201 1;194-9.
    Open DOISearch in Google Scholar
  21. 21. Obuchowski NA, McClish DK. Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices. Stat Med 1997 ;1529-42.
    Open DOISearch in Google Scholar
  22. 22. Tabachnik BG, Fidell SL. Discriminant analysis. In: Tabachnik BG, Fidell SL, editors. Using multivariate statistics. Boston: Pearson Education Inc.; 201 3. p. 377-438.
    Search in Google Scholar
  23. 23. Pallant J. Assesing normality. In: Pallant J, editor. SPSS survival manual. Crows Nest: Allen & Unwin; 2 011. p. 59-64.
    Search in Google Scholar
  24. 24. Pallant J. Multivariate analysis of variance. In: Pallant J, editor. SPSS survival manual. Crows Nest: Allen & Unwin; 20 11. p. 283-96.
    Search in Google Scholar
  25. 25. Chan YH. Biostatistics 104: correlational analysis. Singapore Med J 20 03;614-9.
    Search in Google Scholar
  26. 26. Field A. Output from the discriminant analysis. In: Field A, editor. Discovering statistics using SPSS. London: SAGE Publications Ltd; 20 09. p. 618-21.
    Search in Google Scholar
  27. 27. Cohen JW. Statistical power analysis for the behavioral sciences. 2nd edition. Hillsdale: Lawrence Erlbaum Ass ociates; 1988.
    Search in Google Scholar
  28. 28. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Ann Int Med 200 3;: W1-12.
    Open DOISearch in Google Scholar
  29. 29. Tabachnik BG, Fidell SL. Multicollinearity and singularity. In: Tabachnik BG, Fidell SL, editors. Using multivariate statistics. Boston: Pearson Education Inc.; 2 013. p. 88-91.
    Search in Google Scholar
  30. 30. Field A. Discriminant function variates. In: Field A, editor. Discovering statistics using SPSS. London: SAGE Publications Inc.; 200 9. p. 599-624.
    Search in Google Scholar
  31. 31. Pena A, Green HA, Carpenter TA, Price SJ, Pickard JD, Gillard JH. Enhanced visualization and quantification of magnetic resonance diffusion tensor imaging using the p:q tensor decomposition. Br J Radiol 20 06;101-9.
    Open DOISearch in Google Scholar
  32. 32. Budde MD, Xie M, Cross AH, Song SK. Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci 2009;2805-13.
    Open DOISearch in Google Scholar
  33. 33. Feng S, Hong Y, Zhou Z, Jinsong Z, Xiaofeng D, Zaizhong W, et al. Monitoring of acute axonal injury in the swine spinal cord with EAE by diffusion tensor imaging. J Magn Reson Imaging 200 9;277-85.
    Open DOISearch in Google Scholar
  34. 34. Schmierer K, Wheeler-Kingshott CA, Boulby PA, Scaravilli F, Altmann DR, Barker GJ, et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 200 7;467-77.
    Open DOISearch in Google Scholar
  35. 35. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005;132-40.
    Open DOISearch in Google Scholar
  36. 36. Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R. Processing and visualization for diffusion tensor MRI. Med Image Anal 20 02;93-108.
    Open DOISearch in Google Scholar
  37. 37. Woodworth DC, Pope WB, Liau LM, Kim HJ, Lai A, Nghiemphu PL, et al. Nonlinear distortion correction of diffusion MR images improves quantitative DTI measurements in glioblastoma. J Neurooncol 2013. 2014;551-8.
    Open DOISearch in Google Scholar
  38. 38. Saksena S, Jain R, Schultz L, Jiang Q, Soltanian-Zadeh H, Scarpace L, et al. The corpus callosum Wallerian degeneration in the unilateral brain tumors: evaluation with diffusion tensor imaging (DTI). J Clin Diagn Res 2 013;320-5.
    Search in Google Scholar
  39. 39. Saksena S, Jain R, Narang J, Scarpace L, Schultz LR, Lehman NL, et al. Predicting survival in glioblastomas using diffusion tensor imaging metrics. J Magn Reson Imaging 2010;788-95.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/raon-2014-0004 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Journal RSS Feed
Language: English
Page range: 127 - 136
Submitted on: Oct 15, 2013
Accepted on: Dec 21, 2013
Published on: Apr 25, 2014
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
brain neoplasms,
diffusion tensor imaging,
discriminant analysis,
magnetic resonance imaging,
predictive value of tests
Related subjects:
Medicine,
Clinical medicine,
Radiology

© 2014 Ernesto Roldan-Valadez, Camilo Rios, David Cortez-Conradis, Rafael Favila, Sergio Moreno-Jimenez, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 48 (2014): Issue 2 (June 2014)
Previous article
Next article