Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma.
Abstract
OBJECTIVEThe goal of this study was to identify parameters from routine T1- and T2-weighted MR sequences and diffusion tensor imaging (DTI) that best predict the volumetric changes in a meningioma after treatment with Gamma Knife radiosurgery (GKRS).METHODSIn 32 patients with meningioma, routine MRI and DTI data were measured before GKRS. A total of 78 parameters derived from first-level texture analysis of the pretreatment MR images, including calculation of the mean, SD, 2.5th and 97.5th percentiles, and kurtosis and skewness of data in histograms on a voxel-wise basis, were correlated with lesion volume change after a mean follow-up period of 3 years (range 19.5-63.3 months).RESULTSSeveral DTI-derived parameters correlated significantly with a meningioma volume change. The parameter that best predicted the results of GKRS was the 2.5th percentile value of the smallest eigenvalue (L3) of the diffusion tensor (correlation coefficient 0.739, p ≤ 0.001), whereas among the non-DTI parameters, only the SD of T2-weighted images correlated significantly with a tumor volume change (correlation coefficient 0.505, p ≤ 0.05, after correction for family-wise errors using false-detection-rate correction).CONCLUSIONSDTI-derived data had a higher correlation to shrinkage of meningioma volume after GKRS than data from T1- and T2-weighted image sequences. However, if only routine MR images are available, the SD of T2-weighted images can be used to predict control or possible progression of a meningioma after GKRS.
Keywords
Texture analysis
oncology
gamma knife
srs = stereotactic radiosurgery
stereotactic radiosurgery
diffusion tensor imaging
meningioma
dti = diffusion tensor imaging
fa = fractional anisotropy
cc = correlation coefficient
ce = contrast enhanced
cl = longitudinal index
cp = planar index
cs = spherical index
gkrs = gamma knife radiosurgery
sk = skewness of tensor shape
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109832
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0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
109832
Unique Identifier:
10.3171/2018.7.GKS181327
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Scimatic Chain (ID: 481)
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