Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression

Journal of Medical Imaging

Volumn 2, Issue 2, 2015, Pages

  Kelm, Zachary S ^a   Korfiatis, Panagiotis D ^a   Lingineni, Ravi K ^a   Daniels, John R ^b   Buckner, Jan C ^a   Lachance, Daniel H ^a   Parney, Ian F ^a   Carter, Rickey E ^a   Erickson, Bradley J ^a  

^a MAYO CLINIC   (United States)

^b MAYO CLINIC   (United States)

Author keywords

Cerebral blood volume; dynamic susceptibility contrast; glioblastoma; magnetic resonance imaging; perfusion

Indexed keywords

GADOLINIUM; TEMOZOLOMIDE;

ALGORITHM; ARTICLE; BRAIN BLOOD VOLUME; CANCER CHEMOTHERAPY; CANCER RADIOTHERAPY; COHORT ANALYSIS; COMPUTER PROGRAM; CONTRAST ENHANCEMENT; CORRELATION COEFFICIENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DYNAMIC SUSCEPTIBILITY CONTRAST MAGNETIC RESONANCE IMAGING; FOLLOW UP; GLIOBLASTOMA; HUMAN; IMAGE PROCESSING; MAJOR CLINICAL STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; OUTCOME ASSESSMENT; PREDICTION; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; TUMOR GROWTH;

EID: 85016245097     PISSN: 23294302     EISSN: 23294310     Source Type: Journal    
DOI: 10.1117/1.JMI.2.2.026001     Document Type: Article

References (30)

1. S. A. Grossman et al., "Survival of patients with newly diagnosed glioblastoma treated with radiation and temozolomide in research studies in the United States," Clin. Cancer Res. 16(8), 2443-2449 (2010).
2. R. Stupp et al., "Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial," Lancet Oncol. 10(5), 459-466 (2009).
3. M. C. Y. de Wit et al., "Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression," Neurology 63(3), 535-537 (2004).
4. J. L. Clarke and S. Chang, "Pseudoprogression and pseudoresponse: challenges in brain tumor imaging," Curr. Neurol. Neurosci. Rep. 9(3), 241-246 (2009).
5. E. Topkan et al., "Pseudoprogression in patients with glioblastoma multiforme after concurrent radiotherapy and temozolomide," Am. J. Clin. Oncol. 35(3), 284-289 (2012).
6. R. J. Young et al., "Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma," Neurology 76(22), 1918-1924 (2011).
7. J. M. Provenzale, S. Mukundan, and D. P. Barboriak, "Diffusionweighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response," Radiology 239(3), 632-649 (2006).
8. T. Sugahara et al., "Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue," Am. J. Neuroradiol. 21(5), 901-909 (2000).
9. L. S. Hu et al., "Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements," Am. J. Neuroradiol. 30(3), 552-558 (2009).
10. E. L. Gasparetto et al., "Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction," Radiology 250(3), 887-896 (2009).
11. D. S. Kong et al., "Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status," Am. J. Neuroradiol. 32(2), 382-387 (2011).
12. E. S. Paulson and K. M. Schmainda, "Comparison of dynamic susceptibility- weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors," Radiology 249(2), 601-613 (2008).
13. J. L. Boxerman et al., "The role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard," Am. J. Neuroradiol. 33(6), 1081-1087 (2012).
14. M. Law et al., "Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade," Am. J. Neuroradiol. 25(5), 746-755 (2004).
15. J. L. Boxerman, K. M. Schmainda, and R. M. Weisskoff, "Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not," AJNR Am. J. Neuroradiol. 27(4), 859-867 (2006).
16. K. Kudo et al., "Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom," Radiology 267(1), 201-211 (2013).
17. L. Orsingher, S. Piccinini, and G. Crisi, "Differences in dynamic susceptibility contrast MR perfusion maps generated by different methods implemented in commercial software," J. Comput. Assist. Tomogr. (2014).
18. M. V. Milchenko et al., "Comparison of perfusion- and diffusionweighted imaging parameters in brain tumor studies processed using different software platforms," Acad. Radiol. (2014).
19. P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability," NeuroImage 31(3), 1116-1128 (2006).
20. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. Man Cybern. 9(1), 62-66 (1979).
21. M. Jenkinson et al., "FSL," NeuroImage 62(2), 782-790 (2012).
22. M. Jenkinson et al., "Improved optimization for the robust and accurate linear registration and motion correction of brain images," NeuroImage 17 (2), 825-841 (2002).
23. S. M. Smith, "Fast robust automated brain extraction," Hum. Brain Mapp. 17(3), 143-155 (2002).
24. L. S. Hu et al., "Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival," Neuro-Oncology 14(7), 919-930 (2012).
25. H. Kim et al., "Gliomas: application of cumulative histogram analysis of normalized cerebral blood volume on 3 T MRI to tumor grading," PLoS ONE 8(5), e63462 (2013).
26. M. Gamer et al., "IRR: various coefficients of interrater reliability and agreement," 2012, http://cran.r-project.org/web/packages/irr/ (20 April 2015).
27. R Core Team, "R: a language and environment for statistical computing," 2013, http://web.mit.edu/r-v3.0.1/fullrefman.pdf (20 April 2015).
28. K. O. McGraw and S. P. Wong, "Forming inferences about some intraclass correlation coefficients," Psychol. Methods 1(1), 30 (1996).
29. E. Yaman et al., "Radiation induced early necrosis in patients with malignant gliomas receiving temozolomide," Clin. Neurol. Neurosurg. 112(8), 662-667 (2010).
30. N. Mauz et al., "Perfusion magnetic resonance imaging: comparison of semiologic characteristics in first-pass perfusion of brain tumors at 1.5 and 3 Tesla," J. Neuroradiol. 39(5), 308-316 (2012).