Abstract
Introduction
Gliomas remain difficult to treat, in part, due to our inability to accurately delineate the margins of the tumor. The goal of our study was to evaluate if a combination of advanced MR imaging techniques and a multimodal imaging model could be used to predict tumor infiltration in patients with diffuse gliomas.
Methods
Institutional review board approval and written consent were obtained. This prospective pilot study enrolled patients undergoing stereotactic biopsy for a suspected de novo glioma. Stereotactic biopsy coordinates were coregistered with multiple standard and advanced neuroimaging sequences in 10 patients. Objective imaging values were assigned to the biopsy sites for each of the imaging sequences. A principal component analysis was performed to reduce the dimensionality of the imaging dataset without losing important information. A univariate analysis was performed to identify the statistically relevant principal components. Finally, a multivariate analysis was used to build the final model describing nuclear density.
Results
A univariate analysis identified three principal components as being linearly associated with the observed nuclear density (p values 0.021, 0.016, and 0.046, respectively). These three principal component composite scores are predominantly comprised of DTI (mean diffusivity or average diffusion coefficient and fractional anisotropy) and PWI data (rMTT, Ktrans). The p value of the model was <0.001. The correlation between the predicted and observed nuclear density was 0.75.
Conclusion
A multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.
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References
Deorah S, Lynch CF, Sibenaller ZA, Ryken TC (2006) Trends in brain cancer incidence and survival in the United States: surveillance, epidemiology, and end results program, 1973 to 2001. Neurosurg Focus 20(4):E1. doi:10.3171/foc.2006.20.4.E1
Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95(2):190–198. doi:10.3171/jns.2001.95.2.0190
Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 62(4):753–764. doi:10.1227/01.neu.0000318159.21731.cf, discussion 264-756
Wallner KE, Galicich JH, Krol G, Arbit E, Malkin MG (1989) Patterns of failure following treatment for glioblastoma multiforme and anaplastic astrocytoma. Int J Radiat Oncol Biol Phys 16(6):1405–1409
Law M, Yang S, Babb JS, Knopp EA, Golfinos JG, Zagzag D, Johnson G (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25(5):746–755
Price SJ, Jena R, Burnet NG, Carpenter TA, Pickard JD, Gillard JH (2007) Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur Radiol 17(7):1675–1684. doi:10.1007/s00330-006-0561-2
Provenzale JM, McGraw P, Mhatre P, Guo AC, Delong D (2004) Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 232(2):451–460. doi:10.1148/radiol.2322030959
Barajas RF Jr, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, Bourne G, Parsa AT, Aghi MK, McDermott MW, Berger MS, Cha S, Chang SM, Nelson SJ (2012) Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 14(7):942–954. doi:10.1093/neuonc/nos128
Kidwell CS, Wintermark M, De Silva DA, Schaewe TJ, Jahan R, Starkman S, Jovin T, Hom J, Jumaa M, Schreier J, Gornbein J, Liebeskind DS, Alger JR, Saver JL (2013) Multiparametric MRI and CT models of infarct core and favorable penumbral imaging patterns in acute ischemic stroke. Stroke 44(1):73–79. doi:10.1161/STROKEAHA.112.670034
Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, Johnson G (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27(9):1975–1982
Cook P, Bai Y, Nedjati-Gilani S, Seunarine K, Hall M, Parker G, Alexander D Camino (2006) Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA. p 2759
Barboriak DP (2006) Dynamic susceptibility contrast MR Analysis (DSCoMAn), 1st edn. Duke University, Durham
Boxerman JL, Schmainda KM, Weisskoff RM (2006) 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
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109. doi:10.1007/s00401-007-0243-4
Boruah D, Deb P (2013) Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas. ISRN Oncol 2013:760653. doi:10.1155/2013/760653
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044. doi:10.1016/j.neuroimage.2010.09.025
Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22(1):120–128. doi:10.1109/TMI.2003.809072
Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304
Johnson RA, Wichern DW (2007) Applied Multivariate Statistical Analysis. 6th edn. Pearson
Hardin JW, Hilbe JM (2002) Generalized estimating equations, 1st edn. Chapman and Hall, Boca Raton
Huber PJ (1967) The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. pp 221–233
White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4):817–838
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128. doi:10.1016/j.neuroimage.2006.01.015
Chaskis C, Stadnik T, Michotte A, Van Rompaey K, D’Haens J (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta Neurochir (Wien) 148(3):277–285. doi:10.1007/s00701-005-0718-9, discussion 285
Stecco A, Pisani C, Quarta R, Brambilla M, Masini L, Beldi D, Zizzari S, Fossaceca R, Krengli M, Carriero A (2011) DTI and PWI analysis of peri-enhancing tumoral brain tissue in patients treated for glioblastoma. J Neurooncol 102(2):261–271. doi:10.1007/s11060-010-0310-x
Law M, Oh S, Babb JS, Wang E, Inglese M, Zagzag D, Knopp EA, Johnson G (2006) Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging–prediction of patient clinical response. Radiology 238(2):658–667. doi:10.1148/radiol.2382042180
Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60(4):493–502. doi:10.1016/j.crad.2004.09.009
Sorensen AG, Batchelor TT, Zhang WT, Chen PJ, Yeo P, Wang M, Jennings D, Wen PY, Lahdenranta J, Ancukiewicz M, di Tomaso E, Duda DG, Jain RK (2009) A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 69(13):5296–5300. doi:10.1158/0008-5472.CAN-09-0814
Mills SJ, Patankar TA, Haroon HA, Baleriaux D, Swindell R, Jackson A (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 27(4):853–858
Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Pena A, Pickard JD, Carpenter TA, Gillard JH (2006) Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 27(9):1969–1974
Stadlbauer A, Nimsky C, Buslei R, Salomonowitz E, Hammen T, Buchfelder M, Moser E, Ernst-Stecken A, Ganslandt O (2007) Diffusion tensor imaging and optimized fiber tracking in glioma patients: histopathologic evaluation of tumor-invaded white matter structures. Neuroimage 34(3):949–956. doi:10.1016/j.neuroimage.2006.08.051
Lee HY, Na DG, Song IC, Lee DH, Seo HS, Kim JH, Chang KH (2008) Diffusion-tensor imaging for glioma grading at 3-T magnetic resonance imaging: analysis of fractional anisotropy and mean diffusivity. J Comput Assist Tomogr 32(2):298–303. doi:10.1097/RCT.0b013e318076b44d
Sanai N, Alvarez-Buylla A, Berger MS (2005) Neural stem cells and the origin of gliomas. N Engl J Med 353(8):811–822. doi:10.1056/NEJMra043666
Coons SW, Johnson PC, Shapiro JR (1995) Cytogenetic and flow cytometry DNA analysis of regional heterogeneity in a low grade human glioma. Cancer Res 55(7):1569–1577
Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D (2002) Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 223(1):11–29
Lupo JM, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26(6):1446–1454
Arvinda HR, Kesavadas C, Sarma PS, Thomas B, Radhakrishnan VV, Gupta AK, Kapilamoorthy TR, Nair S (2009) Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 94(1):87–96. doi:10.1007/s11060-009-9807-6
Emblem KE, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, Scheie D, Borota OC, Cvancarova M, Bjornerud A (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247(3):808–817. doi:10.1148/radiol.2473070571
Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, Fitzek MM, Chiocca EA, Rabinov JD, Csavoy AN, Rosen BR, Hochberg FH, Schaefer PW, Gonzalez RG (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 25(2):214–221
Roberts HC, Roberts TP, Ley S, Dillon WP, Brasch RC (2002) Quantitative estimation of microvascular permeability in human brain tumors: correlation of dynamic Gd-DTPA-enhanced MR imaging with histopathologic grading. Acad Radiol 9(Suppl 1):S151–S155
Uematsu H, Maeda M, Sadato N, Matsuda T, Ishimori Y, Koshimoto Y, Yamada H, Kimura H, Kawamura Y, Hayashi N, Yonekura Y, Ishii Y (2000) Vascular permeability: quantitative measurement with double-echo dynamic MR imaging—theory and clinical application. Radiology 214(3):912–917
Patankar TF, Haroon HA, Mills SJ, Baleriaux D, Buckley DL, Parker GJ, Jackson A (2005) Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 26(10):2455–2465
Zhang N, Zhang L, Qiu B, Meng L, Wang X, Hou BL (2012) Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas. J Magn Reson Imaging 36(2):355–363. doi:10.1002/jmri.23675
Bulakbasi N, Guvenc I, Onguru O, Erdogan E, Tayfun C, Ucoz T (2004) The added value of the apparent diffusion coefficient calculation to magnetic resonance imaging in the differentiation and grading of malignant brain tumors. J Comput Assist Tomogr 28(6):735–746
Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22(6):1081–1088
Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224(1):177–183
Sadeghi N, D’Haene N, Decaestecker C, Levivier M, Metens T, Maris C, Wikler D, Baleriaux D, Salmon I, Goldman S (2008) Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. AJNR Am J Neuroradiol 29(3):476–482. doi:10.3174/ajnr.A0851
Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J (1994) MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol 162(3):671–677
Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79(944):652–658. doi:10.1259/bjr/25349497
Stadlbauer A, Ganslandt O, Buslei R, Hammen T, Gruber S, Moser E, Buchfelder M, Salomonowitz E, Nimsky C (2006) Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 240(3):803–810. doi:10.1148/radiol.2403050937
Cruz LC Jr, Sorensen AG (2006) Diffusion tensor magnetic resonance imaging of brain tumors. Magn Reson Imaging Clin N Am 14(2):183–202. doi:10.1016/j.mric.2006.06.003
Yoshikawa K, Kajiwara K, Morioka J, Fujii M, Tanaka N, Fujisawa H, Kato S, Nomura SM (2006) Improvement of functional outcome after radical surgery in glioblastoma patients: the efficacy of a navigation-guided fence-post procedure and neurophysiological monitoring. J Neurooncol 78(1):91–97. doi:10.1007/s11060-005-9064-2
Acknowledgments
We would like to acknowledge our MRI technicians for their hard work, including Jaime Weathersbee, Brian Burkholder, Dean Baugher, and Thomas Huerta. Additionally, we would like to acknowledge David Beech from the OR for providing all surgical coordinates.
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We declare that we have no conflict of interest.
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Durst, C.R., Raghavan, P., Shaffrey, M.E. et al. Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 56, 107–115 (2014). https://doi.org/10.1007/s00234-013-1308-9
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DOI: https://doi.org/10.1007/s00234-013-1308-9