Synopsis

To determine whether combined use of diffusion and perfusion MRI can help in differentiating brain infections from glioblastomas (GBMs), 13 patients with infections and 20 patients with GBMs underwent DTI and DSC-PWI. Significantly lower median values of mean diffusivity (MD) and higher fractional anisotropy (FA) were observed in central core of infective lesions than in GBMs. Additionally, significantly decreased median and maximum values of relative cerebral blood volume (rCBV) from enhancing region were observed in patients with infections compared with those of GBMs. The best classification model consisted of MD from central core and rCBV_max from enhancing region.

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Purpose: Differentiation of brain infections from glioblastomas (GBMs) is crucial as prognosis and clinical management of these patients is quite different.¹ Using diffusion tensor imaging (DTI), few studies^2-4 have reported the differentiation of these two entities. However, high diffusivity similar to that found in necrotic high-grade gliomas has been reported in 5-21% of untreated abscesses.⁵ Moreover, infective lesions may also demonstrate high anisotropy mimicking GBMs⁶ suggesting that differential diagnosis may not always be possible on the basis of DTI alone. Dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) derived cerebral blood volume (CBV) has been used to differentiate infective from neoplastic lesions but with conflicting results.^7,8 Some have reported similar patterns of CBV in these two lesions, while others have found a significant decrease in CBV in infective lesions compared to neoplastic lesions. Given that DTI and DSC-PWI provide different physiological information, we hypothesize that synergistic interactions between DTI parameters and CBV may improve distinction of these two conditions. This study aims to investigate the potential of combined use of DTI and DSC-PWI to distinguish brain infections from GBMs.

Methods: A cohort of 13 patients (8M/5F, mean age=45.7±12.2years) with brain infections and 20 patients (10M/10F, mean age 60±12.2 years) with GBMs underwent anatomical imaging, DTI and PWI on a 3T MR system. DTI data were acquired using 30 directions with a single-shot spin-echo EPI with parallel imaging (acceleration factor=2), TR/TE=5000/86ms; NEX=3; in-plane resolution=1.72 × 1.72mm², slice thickness=3mm, b= 0, 1000s/mm². For DSC-PWI, T2* weighted gradient-echo EPI sequence was acquired using the following parameters: TR/TE = 2000/45ms, in-plane resolution=1.72 × 1.72mm², 20 slices with thickness= 3 mm. Forty-five sequential measurements were acquired for each section, with a temporal resolution of 2.1s. After motion and eddy current correction of raw DTI data, mean diffusivity (MD) and fractional anisotropy (FA) were computed using in-house developed algorithm.^9,10 Leakage corrected CBV maps were constructed using NordicICE program. Contrast-enhanced T1 weighted images, FLAIR, CBV and DTI maps were co-registered and a semi-automated segmentation routine was used to segment the lesion into enhancing (ER) and central regions (CR). The median MD, FA, and relative CBV (rCBV) were computed from these regions. The 90^th percentile rCBV values were also measured from enhancing region to compute maximum rCBV (rCBV_max). These parameters were compared between brain infections and GBMs using Mann-Whitney-U tests. Receiver operating characteristic (ROC) curve and multivariate logistic regression analyses were performed to determine the best model for classification.

Results: Representative anatomical images, MD, FA and rCBV maps from a patient each from GBM and brain infection is shown in Figure 1. Significantly lower MD was observed from the central core in patients with infections compared to those of GBMs. Also, significantly decreased median and maximum values of relative cerebral blood volume (rCBV) from enhancing region were observed in patients with infections compared with those of GBMs. A pair-wise comparison of DTI parameters and rCBV_max is shown in Box-Whisker plots (Fig. 2). Using ROC and multivariate logistic regression analyses, the best model to distinguish brain infections from GBM consisted of MD from central core, rCBVmax from enhancing region resulting in area under the ROC curve (AUC) of 0.88 (Fig. 3). This classification model provided a sensitivity of 95% and specificity of 77% in distinguishing brain infections from GBMs.

Discussion: Our results indicate that different pattern water diffusion and hemodynamics may allow discrimination of brain abscess from GBMs with high sensitivity and specificity. In consistent with previous report,6 significantly lower median values of MD and higher FA were observed in central core of infective lesions than in GBMs. These findings suggest that central core of infections and GBMs may harbor differential degrees of cellularity, viscosity and cyto-architectural composition (number of inflammatory cells, protein molecules, and arrangement of collagen fibers).¹¹ We also observed significantly higher median rCBV and rCBV_max in GBMs than in infective lesions from enhancing regions of lesions secondary to angiogenesis of viable neoplastic cells and increased expression of vascular endothelial growth factors and increased microvascular density with the tumor bed.¹²

Conclusions: We believe that DTI and DSC-PWI provide complementary physiological information that may improve characterization and differentiation of brain infections from GBMs with high sensitivity and specificity.

Acknowledgements

References

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