Spin-echo echo-planar perfusion MR imaging in the differential diagnosis of solitary enhancing brain lesions: distinguishing solitary metastases from primary glioma

Diffusion-Weighted Imaging Combined with Perfusion-Weighted Imaging under Segmentation Algorithm in the Diagnosis of Melanoma

This study is aimed at exploring the value of magnetic resonance diffusion-weighted imaging (DWI) combined with perfusion-weighted imaging (PWI) for diagnosing melanoma under a three-dimensional (3D) hybrid segmentation algorithm. 40 patients with melanoma were collected as research objects and subjected to magnetic resonance imaging (MRI) examination. A segmentation model was constructed and the original images were input. The noise contained in the images was preprocessed and normalized, and the mixed level set segmentation was performed after linear fusion of the images. Imaging findings were analyzed to find that the combined diagnosis of DWI and PWI with a 3D hybrid segmentation algorithm had the advantage of being clear and accurate. 10 primary cases were detected, which occurred in the cerebral meninges; 30 cases of metastases occurred inside the skull, mostly adjacent to the surface of the brain. The typical T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) of melanoma showed high signal and low signal, respectively, and the enhanced scan showed obvious enhancement. Atypical melanoma was manifested variously in MRI; a few had cystic necrosis, and an enhanced scan of the solid area revealed significant enhancement. Patients with multiple metastatic melanomas mainly showed low signal on DWI, and patients with primary or single metastatic melanoma mainly showed high signal or mixed high signal. Patients with perfusion imaging showed high perfusion on PWI. The 3D hybrid segmentation algorithm helped to improve the accuracy of DWI combined with PWI in the diagnosis of melanoma. This work provided a certain reference for the clinical diagnosis of melanoma.

Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis

OBJECTIVES

Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.

METHODS

A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.

RESULTS

Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).

CONCLUSIONS

Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.

KEY POINTS

• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

The role of cerebral blood flow gradient in peritumoral edema for differentiation of glioblastomas from solitary metastatic lesions

OBJECTIVE

Differentiation of glioblastomas from solitary brain metastases using conventional MRI remains an important unsolved problem. In this study, we introduced the conception of the cerebral blood flow (CBF) gradient in peritumoral edema-the difference in CBF values from the proximity of the enhancing tumor to the normal-appearing white matter, and investigated the contribution of perfusion metrics on the discrimination of glioblastoma from a metastatic lesion.

MATERIALS AND METHODS

Fifty-two consecutive patients with glioblastoma or a solitary metastatic lesion underwent three-dimensional arterial spin labeling (3D-ASL) before surgical resection. The CBF values were measured in the peritumoral edema (near: G1; Intermediate: G2; Far: G3). The CBF gradient was calculated as the subtractions CBFG1 -CBFG3, CBFG1 - CBFG2 and CBFG2 - CBFG3. A receiver operating characteristic (ROC) curve analysis was used to seek for the best cutoff value permitting discrimination between these two tumors.

RESULTS

The absolute/related CBF values and the CBF gradient in the peritumoral regions of glioblastomas were significantly higher than those in metastases(P < 0.038). ROC curve analysis reveals, a cutoff value of 1.92 ml/100g for the CBF gradient of CBFG1 -CBFG3 generated the best combination of sensitivity (92.86%) and specificity (100.00%) for distinguishing between a glioblastoma and metastasis.

CONCLUSION

The CBF gradient in peritumoral edema appears to be a more promising ASL perfusion metrics in differentiating high grade glioma from a solitary metastasis.

Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images

Dynamic contrast-enhanced perfusion and permeability imaging, using computed tomography and magnetic resonance systems, are important techniques for assessing the vascular supply and hemodynamics of healthy brain parenchyma and tumors. These techniques can measure blood flow, blood volume, and blood-brain barrier permeability surface area product and, thus, may provide information complementary to clinical and pathological assessments. These have been used as biomarkers to enhance the treatment planning process, to optimize treatment decision-making, and to enable monitoring of the treatment noninvasively. In this review, the principles of magnetic resonance and computed tomography dynamic contrast-enhanced perfusion and permeability imaging are described (with an emphasis on their commonalities), and the potential values of these techniques for differentiating high-grade gliomas from other brain lesions, distinguishing true progression from posttreatment effects, and predicting survival after radiotherapy, chemotherapy, and antiangiogenic treatments are presented.

Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model

BACKGROUND AND PURPOSE

Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D).

MATERIALS AND METHODS

Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, β, and μ) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation.

RESULTS

Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ≤ .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ≥ .706). The receiver operating characteristic analysis showed that β (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or μ (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%.

CONCLUSIONS

The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, β exhibited a better performance than the other 2 parameters, including ADC (or D).

Brain perfusion: computed tomography and magnetic resonance techniques

Cerebral perfusion imaging provides assessment of regional microvascular hemodynamics in the living brain, enabling in vivo measurement of a variety of different hemodynamic parameters. Perfusion imaging techniques that are used in the clinical setting usually rely upon X-ray computed tomography (CT) or magnetic resonance imaging (MRI). This chapter reviews CT- and MRI-based perfusion imaging techniques, with attention to image acquisition, clinically relevant aspects of image postprocessing, and fundamental differences between CT- and MRI-based techniques. Correlations with cerebrovascular physiology and potential clinical applications of perfusion imaging are reviewed, focusing upon the two major classes of neurologic disease in which perfusion imaging is most often performed: primary perfusion disorders (including ischemic stroke, transient ischemic attack, and reperfusion syndrome), and brain tumors.

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma

BACKGROUND AND PURPOSE

Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS

This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.

RESULTS

With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS

Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model

PURPOSE

To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors.

MATERIALS AND METHODS

The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm(2)/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm(2)/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation.

CONCLUSION

The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.

Use of CT perfusion to discriminate between brain metastases from different primaries

Thirty-six metastases in 22 patients were studied prospectively using computed tomography perfusion. Regions of interests were drawn around: the enhancing part of the tumor, necrotic central part, periphery, peritumoral edema, and normal white matter. Cerebral blood volume, cerebral blood flow, and mean transit time were calculated for each zone. The enhancing part of the tumor significantly differed from the other zones in 11 of 12. Metastases of different primaries can be differentiated from one another with statistically significance (P<.05) by at least one perfusion parameter in 57% of cases.

Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions

OBJECTIVE

Glioblastoma and solitary metastatic lesions can be difficult to differentiate with conventional MRI. The use of diffusion-weighted MRI to better characterize peritumoral edema has been explored for this purpose, but the results have been conflicting. The purpose of this study was to test the hypothesis that the gradient of apparent diffusion coefficient (ADC) values in peritumoral edema--that is, the difference in ADC values from the region closest to the enhancing tumor and the one closest to the normal-appearing white matter--may be a marker for differentiating glioblastoma from a metastatic lesion.

MATERIALS AND METHODS

Forty patients, 20 with glioblastoma and 20 with a solitary metastatic lesion, underwent diffusion-weighted brain MRI before surgical resection. The ADC values were retrospectively collected in the peritumoral edema in three positions: near, an intermediate distance from, and far from the core enhancing tumor (G1, G2, and G3). The ADC gradient in the peritumoral edema was calculated as the subtractions ADCG3 - ADCG1, ADCG3 - ADCG2, and ADCG2 - ADCG1. The ADC values in the enhancing tumor, peritumoral edema, ipsilateral normal-appearing white matter, contralateral healthy white matter, and CSF were also collected.

RESULTS

A gradient of ADC values was found in the peritumoral edema of glioblastoma. The ADC values increased from the region close to the enhancing tumor (1.36 ± 0.24 × 10(-3) mm(2)/s) to the area near the normal-appearing white matter (1.57 ± 0.34 × 10(-3) mm(2)/s). In metastatic lesions, however, those values were nearly homogeneous (p = 0.04).

CONCLUSION

The ADC gradient in peritumoral edema appears to be a promising tool for differentiating glioblastoma from a metastatic lesion.

Influence of blood/tissue differences in contrast agent relaxivity on tracer-based MR perfusion measurements

PURPOSE

Perfusion assessment by monitoring the transport of a tracer bolus depends critically on conversion of signal intensity into tracer concentration. Two main assumptions are generally applied for this conversion; (1) contrast agent relaxivity is identical in blood and tissue, (2) change in signal intensity depends only on the primary relaxation effect. The purpose of the study was to assess the validity and influence of these assumptions.

MATERIALS AND METHODS

Blood and cerebral tissue relaxivities r1, r2, and r2* for gadodiamide were measured in four pigs at 1.5 T. Gadolinium concentration was determined by inductively coupled plasma atomic emission spectroscopy. Influence of the relaxivities, secondary relaxation effects and choice of singular value decomposition (SVD) regularization threshold was studied by simulations.

RESULTS

In vivo relaxivities relative to blood concentration [in s(-1) mM(-1) for blood, gray matter (GM), white matter (WM)] were for r1 (2.614 ± 1.061, 0.010 ± 0.001, 0.004 ± 0.002), r2 (5.088 ± 0.952, 0.091 ± 0.008, 0.059 ± 0.014), and r2* (13.292 ± 3.928, 1.696 ± 0.157, 0.910 ± 0.139). Although substantial, by a nonparametric test for paired samples, the differences were not statistically significant. The GM to WM blood volume ratio was estimated to 2.6 ± 0.9 by r1, 1.6 ± 0.3 by r2, and 1.9 ± 0.2 by r2*. Secondary relaxation was found to reduce the tissue blood flow, as did the SVD regularization threshold.

CONCLUSION

Contrast agent relaxivity is not identical in blood and tissue leading to substantial errors. Further errors are introduced by secondary relaxation effects and the SVD regularization.

Spin-echo echo-planar perfusion prior to chemoradiation is a strong independent predictor of progression-free and overall survival in newly diagnosed glioblastoma

Spin-echo echo planar (EP) perfusion weighted imaging (SE-PWI) has been demonstrated to be more selective than gradient-echo EP PWI for blood volume in microvessels the size of glioma neocapillaries, but it has not been comprehensively studied in human clinical use. We assessed whether SE-PWI before and after initiating chemoradiation can stratify patients with respect to progression free survival (PFS) and overall survival (OS). Sixty-eight patients with newly diagnosed glioblastoma (mean age 58.3, 36 males) were included in analysis. SE EP cerebral blood volumes (SE-CBVs) in enhancing and nonenhancing tumor, normalized to contralateral normal appearing white matter (SE-nCBV), were assessed at baseline and after initial chemoradiation. SE-nCBV parameters predictive of PFS and OS were identified in univariate and multivariate Cox proportional hazards models. Multivariate analysis demonstrated that baseline tumor mean SE-nCBV was predictive of PFS (p = 0.038) and OS (p = 0.004). Within the patient sample, baseline tumor mean SE-nCBV <2.0 predicted longer patient PFS (median 47.0 weeks, p < 0.001) and OS (median 98.6 weeks, p = 0.003) compared with baseline mean SE-nCBV >2.0 (median PFS 25.3, median OS 56.0 weeks). Exploratory multi-group stratification demonstrated that very high (>4.0) tumor SE-nCBV was associated with worse patient OS than intermediate high (>2.0, <4.0) SE-nCBV (p = 0.025). Baseline mean SE-nCBV can stratify patients for PFS and OS prior to initiation of chemoradiation, which may help select patients who require closer surveillance. Our exploratory analysis indicates a magnitude-dependent relationship between baseline SE-nCBV and OS.

[Perfusion magnetic resonance imaging for high grade astrocytomas: Can cerebral blood volume, peak height, and percentage of signal intensity recovery distinguish between progression and pseudoprogression?]

OBJECTIVES

To study the usefulness of common MRI perfusion parameters for identifying pseudoprogression in high grade astrocytomas.

MATERIAL AND METHODS

This retrospective case-control study compared the relative cerebral blood volume (rCBV), the relative percentage of signal intensity recovery (rPSR), and the relative peak height (rPH) recorded in a sample of 17 cases of anaplastic astrocytomas and gliomas considered to be undergoing pseudoprogression by biopsy or follow-up with those recorded in a sample of histologically similar tumors that were treated and considered to be undergoing progression by histologic study or follow-up. We evaluated the accuracy of these parameters and the correlations among them. Statistical significance was set at P<.05.

RESULTS

The rCBV, rPSR, and rPH were significantly different between the two groups (P=.001). The cutoff values rPH=1.37, rCBV=0.9, and rPSR=99% yielded sensitivity (S)=88% and specificity (Sp)=82.2% for rPH, S=100% and Sp=100% for rCBV, and S=100% and Sp=70.6% for rPSR, respectively. We found negative correlations between rPRS and rPH (-0.76) and between rPRS and rCBV (-0.81) and a high positive correlation between rPH and rCBV (0.87).

CONCLUSION

The variables rPH and rCBV were useful for differentiating between pseudoprogression and true progression in our sample. The variable rPRS was also very sensitive, although the overlap in the values between samples make it less useful a priori.

Intracranial metastases: spectrum of MR imaging findings

Intracranial metastatic lesions arise through a number of routes. Therefore, they can involve any part of the central nervous system and their imaging appearances vary. Magnetic resonance imaging (MRI) plays a key role in lesion detection, lesion delineation, and differentiation of metastases from other intracranial disease processes. This article is a reasoned pictorial review illustrating the many faces of intracranial metastatic lesions based on the location - intra-axial metastases, calvarial metastases, dural metastases, leptomeningeal metastases, secondary invasion of the meninges by metastatic disease involving the calvarium and skull base, direct or perineural intracranial extension of head and neck neoplasm, and other unusual manifestations of intracranial metastases. We also review the role of advanced MRI to distinguish metastases from high-grade gliomas, tumor-mimicking lesions such as brain abscesses, and delayed post-radiation changes in radiosurgically treated patients.

Advanced magnetic resonance imaging biomarkers of cerebral metastases

There are a number of magnetic resonance imaging techniques available for use in the diagnosis and management of patients with cerebral metastases. This article reviews these techniques, in particular, the advanced imaging methodologies from which quantitative parameters can be derived, the role of these imaging biomarkers have in distinguishing metastases from primary central nervous system tumours and tumour mimics, and metrics that may be of value in predicting the origin of the primary tumour.

Differentiation between brain glioblastoma multiforme and solitary metastasis: qualitative and quantitative analysis based on routine MR imaging

BACKGROUND AND PURPOSE

The differentiation between cerebral GBM and solitary MET is clinically important and may be radiologically challenging. Our hypothesis is that routine MR imaging with qualitative and quantitative analysis is helpful for this differentiation.

MATERIALS AND METHODS

Forty-five GBM and 21 solitary metastases were retrospectively identified, with their preoperative routine MR imaging analyzed. According to the comparison of the area of peritumoral T2 prolongation with that of the lesion, the tumors were classified into grade I (prolongation area ≤ tumor area) and grade II (prolongation area > tumor area). The signal intensities of peritumoral T2 prolongation were measured on T2WI and normalized to the values of the contralateral normal regions by calculating the ratios. The ratio (nSI) of both types of tumors was compared in grade I, grade II, and in tumors without grading. The best cutoff values to optimize the sensitivity and specificity were determined for optimal differentiation.

RESULTS

The nSI of GBM was significantly higher than that of MET without T2 prolongation grading (P < .001), resulting in AUC = 0.725. The difference was significant (P = .014) in grade I tumors (GBM, 38; MET, 9), with AUC = 0.741, and in grade II tumors (GBM, 7; MET, 12), with AUC = 0.869 (P = .017). Both types of tumors showed a different propensity in T2 prolongation grading (χ(2) = 12.079, P = .001).

CONCLUSIONS

Combined with qualitative and quantitative analysis of peritumoral T2 prolongation, routine MR imaging can help in the differentiation between brain GBM and solitary MET.

Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma

BACKGROUND AND PURPOSE

Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG.

MATERIALS AND METHODS

We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression.

RESULTS

We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17).

CONCLUSIONS

rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.

[Brain metastasis of breast cancer; imaging evaluation]

CNS involvement in breast cancer modifies the prognosis and the treatment of the disease. Imaging plays a leading role for the diagnosis, the pretherapeutic assessment and the follow-up. MRI is the most sensitive modality for the detection of infraclinic lesions, reported in about 15% of metastatic breast cancers. In addition to conventional MR study, diffusion MR, perfusion MR and spectroscopy have a diagnostic value with specificity of more than 95%; 3D study is required if neurosurgical resection or stereotactic radiosurgery is contemplated. The use of new drugs in clinical trials needs a precise and accurate follow up to assess their usefulness; appreciation of the response is based on the precise measure of the number of targets and of their size; The WHO and recently the RECIST have established the guidelines for measurement of the tumoral targets and to assess the response to treatments. Brain modifications related to surgery or stereotactic radiosurgery are well studied by MRI.

Elevated peritumoural rCBV values as a mean to differentiate metastases from high-grade gliomas

PURPOSE

Increased relative cerebral blood volume (rCBV) was previously found in peritumoural oedema of glioblastomas (GBM). Supposing that peritumoural rCBV is not increased in metastases, we aimed to evaluate whether rCBV values of the whole peritumoural area are accurate to differentiate solitary metastasis from GBM irrespective of the peritumoural oedema.

METHODS

Contrast-enhanced T1-weighted (T1-w) and T2*-weighted dynamic susceptibility contrast MRI was performed in 52 patients with contrast-enhancing solitary brain tumours before surgery. In each T1-w slice depicting the contrast-enhancing tumour, a rim within approximately 15 mm was defined in the peritumoural area. The rCBV values were normalised to rCBV values of the contralateral normal white matter. Differences between metastases and GBM for normalised rCBV values for each slice were determined with the Mann-Whitney U test (p < 0.05).

RESULTS

Histopathological examination revealed 29 GBM and 23 metastases. Peritumoural rCBV was significantly lower in metastases than in GBM (p < 0.01). Using the cutoff value 1.0 for discriminating metastases from GBM yielded a sensitivity of 96%, specificity of 64%, a positive predictive value of 68% and a negative predictive value of 95%.

CONCLUSIONS

The rCBV in the peritumoural area of contrast-enhancing brain tumours has a high diagnostic accuracy to discriminate metastases from GBM irrespective of surrounding oedema and without the bias of slice selection and ROI positioning. Metastases should be excluded, if at least one tumour-depicting slice reveals an increase of peritumoural rCBV compared to the normal contralateral brain (normalised rCBV value >1). Conversely, the decrease of peritumoural rCBV may not reliably exclude GBM.

Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis

INTRODUCTION

Conventional magnetic resonance (MR) imaging has limited capacity to differentiate between glioblastoma multiforme (GBM) and metastasis. The purposes of this study were: (1) to compare microvascular leakage (MVL), cerebral blood volume (CBV), and blood flow (CBF) in the distinction of metastasis from GBM using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC-MRI), and (2) to estimate the diagnostic accuracy of perfusion and permeability MR imaging.

METHODS

A prospective study of 61 patients (40 GBMs and 21 metastases) was performed at 3 T using DSC-MRI. Normalized rCBV and rCBF from tumoral (rCBVt, rCBFt), peri-enhancing region (rCBVe, rCBFe), and by dividing the value in the tumor by the value in the peri-enhancing region (rCBVt/e, rCBFt/e), as well as MVL were calculated. Hemodynamic and histopathologic variables were analyzed statistically and Spearman/Pearson correlations. Receiver operating characteristic curve analysis was performed for each of the variables.

RESULTS

The rCBVe, rCBFe, and MVL were significantly greater in GBMs compared with those of metastases. The optimal cutoff value for differentiating GBM from metastasis was 0.80 which implies a sensitivity of 95%, a specificity of 92%, a positive predictive value of 86%, and a negative predictive value of 97% for rCBVe ratio. We found a modest correlation between rCBVt and rCBFt ratios.

CONCLUSION

MVL measurements in GBMs are significantly higher than those in metastases. Statistically, both rCBVe, rCBVt/e and rCBFe, rCBFt/e were useful in differentiating between GBMs and metastases, supporting the hypothesis that perfusion MR imaging can detect infiltration of tumor cells in the peri-enhancing region.

Differentiating radiation necrosis from tumor recurrence in high-grade gliomas: assessing the efficacy of 18F-FDG PET, 11C-methionine PET and perfusion MRI

PURPOSE

The authors analyzed the characteristics of perfusion magnetic resonance imaging (MRI), (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and (11)C-methionine (MET) PET to compare the efficacies of these modalities in making the distinction between radiation necrosis and tumor recurrence of high-grade glioma.

PATIENTS AND METHODS

Ten patients were evaluated with dynamic susceptibility contrast perfusion MRI, (11)C-MET PET and (18)F-FDG PET to visualize gadolinium-enhanced lesions during the post-radiation follow-up period. In the perfusion MRI, four regions of interest (ROIs) were identified and average values were calculated. A reference ROI of the same size was defined in the contralateral white matter to obtain the relative cerebral blood volume (rCBV). After coregistering the PET images with the MRI, we measured the maximum uptake values of the lesion and of the contralateral cerebral white matter as reference area to calculate the L(max)/R(max) ratio.

RESULTS

The rCBV was higher in the recurrence group than in the necrosis group (p=0.010). There was no difference between groups in terms of the L(max)/R(max) ratio as derived from the (18)F-FDG and (11)C-MET PET.

CONCLUSION

A quantitative rCBV as calculated from a perfusion MRI scan might be superior to the L(max)/R(max) ratio as derived from (18)F-FDG and (11)C-MET PET in order to distinguish a recurrence of high-grade glioma from radiation necrosis.