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DOI: 10.1148/radiol.2473070571
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(Radiology 2008;247:808-817.)
© RSNA, 2008

Neuroradiology

Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps1

Kyrre E. Emblem, MSc, Baard Nedregaard, MD, Terje Nome, MD, Paulina Due-Tonnessen, MD, John K. Hald, MD, PhD, David Scheie, MD, Olivera Casar Borota, MD, Milada Cvancarova, MSc, and Atle Bjornerud, PhD

1 From the Departments of Medical Physics (K.E.E., A.B.), Neuroradiology (B.N., T.N., P.D., J.K.H.), Pathology (D.S., O.C.B.), and Biostatistics (M.C.), and the Intervention Center (K.E.E.), Rikshospitalet-Radiumhospitalet Medical Centre, Sognsvannsveien 20, N-0027 Oslo, Norway; and Department of Physics, University of Oslo, Oslo, Norway (A.B.). Received March 28, 2007; revision requested May 23; revision received July 2; accepted August 1; final version accepted November 13. Address correspondence to K.E.E. (e-mail: kyrre.eeg.emblem{at}rikshospitalet.no).

Purpose: To retrospectively compare the diagnostic accuracy of an alternative method used to grade gliomas that is based on histogram analysis of normalized cerebral blood volume (CBV) values from the entire tumor volume (obtained with the histogram method) with that of the hot-spot method, with histologic analysis as the reference standard.

Materials and Methods: The medical ethics committee approved this study, and all patients provided informed consent. Fifty-three patients (24 female, 29 male; mean age, 48 years; age range, 14–76 years) with histologically confirmed gliomas were examined with dynamic contrast material–enhanced 1.5-T magnetic resonance (MR) imaging. CBV maps were created and normalized to unaffected white matter (normalized CBV maps). Four neuroradiologists independently measured the distribution of whole-tumor normalized CBVs and analyzed this distribution by classifying the values into area-normalized bins. Glioma grading was performed by assessing the normalized peak height of the histogram distributions. Logistic regression analysis and interobserver agreement were used to compare the proposed method with a hot-spot method in which only the maximum normalized CBV was used.

Results: For the histogram method, diagnostic accuracy was independent of the observer. Interobserver agreement was almost perfect for the histogram method ({kappa} = 0.923) and moderate for the hot-spot method ({kappa} = 0.559). For all observers, sensitivity was higher with the histogram method (90%) than with the hot-spot method (55%–76%).

Conclusion: Glioma grading based on histogram analysis of normalized CBV heterogeneity is an alternative to the established hot-spot method, as it offers increased diagnostic accuracy and interobserver agreement.

Supplemental material: http://radiology.rsnajnls.org/cgi/content/full/247/3/808/DC1

© RSNA, 2008






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