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Differentiation of Solitary Brain Metastasis from Glioblastoma Multiforme: A Predictive Model Using Combined MR Diffusion and Perfusion
Abstract
PURPOSE: Solitary brain metastasis (MET) and glioblastoma multiforme (GBM) can appear similar on conventional MRI. The purpose of this study was to identify MR perfusion and diffusion-weighted biomarkers that can differentiate MET from GBM, using voxel-based analysis.
MATERIALS AND METHODS: In this retrospective study, patients were included if they met the following criteria: underwent resection of a solitary enhancing brain tumor and had preoperative 3.0T MRI encompassing DTI, dynamic contrast-enhanced (DCE) and DSC perfusion. Using coregistered images, voxel-based FA, MD, Ktrans and rCBV values were obtained in the enhancing tumor and non-enhancing peritumoral T2 hyperintense region (NET2). Data were analyzed by logistic regression and analysis of variance. Receiver operating characteristic (ROC) analysis was performed to determine the optimal parameter(s) and threshold(s) for predicting GBM vs. MET.
RESULTS: Twenty-three patients (14 M, age: 32-78 y/o) met inclusion criteria. Pathology revealed 13 GBM’s and 10 MET’s. In the enhancing tumor, rCBV, Ktrans, and FA were significantly higher (p<0.0001) in GBM than in MET, whereas MD was significantly lower (p<0.0001) in GBM than in MET. In the NET2, rCBV and FA were significantly higher (p<0.0001) in GBM than in MET, but MD and Ktrans were significantly lower (p<0.0001) in GBM compared to MET. The best discriminative power was obtained in NET2 (not in enhancing tumor) from a combination of rCBV > 0.78, FA > 0.12, MD < 1700 x 10−6 mm2/s, and Ktrans < 0.25 1/min, resulting in an AUC of 0.92 superior to any individual or combination of other classifiers (Figure 1).
CONCLUSIONS: Our multiparametric MRI model is able to distinguish GBM from MET by using a combination of rCBV, Ktrans, FA, and ADC in NET2 with an AUC of 0.92 superior to any individual or combination of other classifiers.
... Glioblastoma (GBM) and cerebral metastasis (cMET) are the most common brain tumors in adult patients [1]. Reliably differentiating GBM and cMET based on their conventional magnetic resonance imaging (MRI) characteristics has proven difficult [2,3], as both tumor types can show necrotic centers, contrast-enhancing peripheral areas and peritumoral edema (Figure 1) [4]. However, studies employing advanced MR-imaging techniques 2 of 13 focusing on the tumor microenvironment and hypoxia-induced changes in the microvasculature found that an elevated cerebral blood flow (CBF) and proxies for increased metabolic activity including a higher resulting cerebral metabolic rate of oxygen (CMRO 2 ) were associated with high-grade gliomas [5,6]. ...
... Over 20% of cancer patients develop disseminations to the central nervous system [15]. One study identified that 55% of cMET cases had no known primary at diagnosis [16], while between 30% to 50% of cMETs have been found to first appear as solitary lesions, further complicating their correct identification [2,17,18]. Accurately discriminating between GBM and cMET is of great clinical importance because therapy approach and surgical decisions are quite different and directly affect patient outcomes [9,17,19]. The current diagnostic standard is an GBMs constitute between 60% and 70% of all malignant gliomas [7]. ...
... Over 20% of cancer patients develop disseminations to the central nervous system [15]. One study identified that 55% of cMET cases had no known primary at diagnosis [16], while between 30% to 50% of cMETs have been found to first appear as solitary lesions, further complicating their correct identification [2,17,18]. Accurately discriminating between GBM and cMET is of great clinical importance because therapy approach and surgical decisions are quite different and directly affect patient outcomes [9,17,19]. The current diagnostic standard is an invasive tissue biopsy with subsequent histopathological examination [4], a procedure that is not without inherent risks with a complication rate of about 6% [20]. ...
Glioblastoma may appear similar to cerebral metastasis on conventional MRI in some cases, but their therapies differ significantly. This prospective feasibility study was aimed at differentiating them by applying the quantitative susceptibility mapping and quantitative blood-oxygen-level-dependent (QSM + qBOLD) model to these entities for the first time. We prospectively included 15 untreated patients with glioblastoma (n = 7, median age: 68 years, range: 54–84 years) or brain metastasis (n = 8, median age 66 years, range: 50–78 years) who underwent preoperative MRI including multi-gradient echo and arterial spin labeling sequences. Oxygen extraction fraction (OEF), cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) were calculated in the contrast-enhancing tumor (CET) and peritumoral non-enhancing T2 hyperintense region (NET2), using an artificial neural network. We demonstrated that OEF in CET was significantly lower (p = 0.03) for glioblastomas than metastases, all features were significantly higher (p = 0.01) in CET than in NET2 for metastasis patients only, and the ratios of CET/NET2 for CBF (p = 0.04) and CMRO2 (p = 0.01) were significantly higher in metastasis patients than in glioblastoma patients. Discriminative power of a support-vector machine classifier was highest with a combination of two features, yielding an area under the receiver operating characteristic curve of 0.94 with 93% diagnostic accuracy. QSM + qBOLD allows for robust differentiation of glioblastoma and cerebral metastasis while yielding insights into tumor oxygenation.
... Multiple studies report on the use of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] High-grade glioma typically shows an infiltrative growth pattern with invasion of the surrounding brain tissues, whereas brain metastasis shows an expansive growth pattern causing displacement of the surrounding brain tissue. 17,18 In addition, high-grade glioma cells tend to produce large amounts of extracellular matrix, which play an important role in tumor growth and infiltration. ...
... Full-text reviews were performed, and 30 studies were excluded because of the following: 1) twelve studies because the 2 ϫ 2 table could not be obtained 29-40 ; 2) seven studies not in the field of interest 41-47 ; 3) five studies with a partially overlapping patient cohort 48-52 ; 4) four studies with mixed brain tumors 53-56 ; 5) one study with a low-grade glioma 57 ; and 6) one case series. 58 Fourteen studies evaluating the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis, [3][4][5][6][7][8][9][10][11][12][13][14][15][16] covering 1143 patients, were included in the analyses. ...
... 14 The detailed MR imaging characteristics are described in On-line Table 2. DWI was used in 7 studies 6,8,[10][11][12][13]16 ; and DTI, in 7 studies. [3][4][5]7,9,14,15 A quantitative ADC value was used in 7 studies using DWI. 6,8,[10][11][12][13]16 Five of the 7 DTI studies used both FA and MD, 3,5,9,14,15 whereas 2 studies used FA only. ...
Background:
Accurate diagnosis of high-grade glioma and solitary brain metastasis is clinically important because it affects the patient's outcome and alters patient management.
Purpose:
To evaluate the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis.
Data sources:
A literature search of Ovid MEDLINE and EMBASE was conducted up to November 10, 2017.
Study selection:
Studies evaluating the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis were selected.
Data analysis:
Summary sensitivity and specificity were established by hierarchic logistic regression modeling. Multiple subgroup analyses were also performed.
Data synthesis:
Fourteen studies with 1143 patients were included. The individual sensitivities and specificities of the 14 included studies showed a wide variation, ranging from 46.2% to 96.0% for sensitivity and 40.0% to 100.0% for specificity. The pooled sensitivity of both DWI and DTI was 79.8% (95% CI, 70.9%-86.4%), and the pooled specificity was 80.9% (95% CI, 75.1%-85.5%). The area under the hierarchical summary receiver operating characteristic curve was 0.87 (95% CI, 0.84-0.89). The multiple subgroup analyses also demonstrated similar diagnostic performances (sensitivities of 76.8%-84.7% and specificities of 79.7%-84.0%). There was some level of heterogeneity across the included studies (I2 = 36%); however, it did not reach a level of concern.
Limitations:
The included studies used various DWI and DTI parameters.
Conclusions:
DWI and DTI demonstrated a moderate diagnostic performance for differentiation of high-grade glioma from solitary brain metastasis.
... On conventional magnetic resonance imaging (MRI), these two lesions may appear very similar, mostly characterized by central necrosis, inhomogeneous ring enhancement and surrounded by edema. Thereby, the radiological differential diagnosis may result challenging, even if it is extremely important in terms of patient management and prognosis [3]. Although conventional magnetic resonance imaging is similar, there are significant histopathological differences between GB and BM. ...
... The role of advanced MRI techniques including spectroscopy, perfusion imaging, diffusion tensor imaging and the measurement of the apparent diffusion coefficient (ADC) has been investigated in order to reach a radiological differential diagnosis between GB and BM [3,4,[7][8][9][10][11][12][13][14]. Both the enhanced area and the peritumoral area have been analyzed, the second with more consistent results [3,4,15,16]. ...
... The role of advanced MRI techniques including spectroscopy, perfusion imaging, diffusion tensor imaging and the measurement of the apparent diffusion coefficient (ADC) has been investigated in order to reach a radiological differential diagnosis between GB and BM [3,4,[7][8][9][10][11][12][13][14]. Both the enhanced area and the peritumoral area have been analyzed, the second with more consistent results [3,4,15,16]. ADC and dynamic susceptibility-weighted contrastenhanced (DSC) perfusion MRI seem to be very promising imaging tools to differentiate GB from BM [3,4,7,15,16]. ...
Purpose
The authors’ purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis.
Materials and methods
Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere ® 3.0. Lesions’ volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed.
Results
According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10 ⁻³ mm ² s ⁻¹ GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%).
Conclusion
Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.
... This renders the hemodynamic study of peritumoral tissue particularly suited for a possible differentiation of the two entities (vasogenic versus infiltrated edema) [113,114]. DSC-MRI studies have provided the most consistent results on discriminating between high-grade gliomas and metastases [77,83,[95][96][97][98][99]115,116]. In fact, a higher DSC-derived CBV can be found in high-grade glioma peritumoral edema [83,95,99]. ...
... DSC-MRI studies have provided the most consistent results on discriminating between high-grade gliomas and metastases [77,83,[95][96][97][98][99]115,116]. In fact, a higher DSC-derived CBV can be found in high-grade glioma peritumoral edema [83,95,99]. Measuring the peritumoral CBV possesses the additional advantage of avoiding possible confounding due to hypervascular metastasis, e.g., melanoma, a diagnostic issue also reported in DCE studies [17,80,115]. ...
... Other studies have found there to be no difference between the two entities [96,116]. Even if less investigated, DCE-MRI also showed potential for high-grade glioma versus metastasis differentiation [80,82,94,95], despite some reports failing to find relevant significant differences [95,118]. Although the same meta-analysis by Suh et al. included only two studies using DCE, the authors still highlight how this technique can constitute a better alternative given the drawback of DSC susceptibility to surgery-dependent artifacts. ...
Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment—glioblastomas, in particular, have a dismal prognosis and are currently incurable—their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.
... On conventional Magnetic Resonance Imaging (MRI) these two lesions may appear very similar, mostly characterized by central necrosis, inhomogeneous ring enhancement and surrounded by oedema. Thereby, the radiological differential diagnosis may result challenging, even if it is extremely important in terms of patient management and prognosis [3]. Although conventional magnetic resonance imaging is similar, there are signi cant histopathological differences between GB and BM. ...
... The role of advanced MRI techniques including spectroscopy, perfusion imaging, diffusion tensor imaging and the measurement of the apparent diffusion coe cient (ADC) has been investigated in order to reach a radiological differential diagnosis between GB and BM [3,4,[6][7][8][9][10]. Both, the enhanced area and the peritumoral area have been analysed, the second with more consistent results [3,4,11,12]. ...
... The role of advanced MRI techniques including spectroscopy, perfusion imaging, diffusion tensor imaging and the measurement of the apparent diffusion coe cient (ADC) has been investigated in order to reach a radiological differential diagnosis between GB and BM [3,4,[6][7][8][9][10]. Both, the enhanced area and the peritumoral area have been analysed, the second with more consistent results [3,4,11,12]. ADC and dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI seem to be very promising imaging tools to differentiate GB from BM [3,4,7,11,12]. ...
PURPOSEThe authors purpose was to evaluate ADC and rCBV values in the enhanced lesion, in the peritumoral area and in distal oedema using a Volume of Interest (VOI) based method and to analysed hemodynamic curves obtained from DSC perfusion MRI, in order to create a valid multiparametric MRI model for the differential diagnosis between Glioblastoma and solitary Brain Metastasis.MATERIALS AND METHODS
Forty-one patients (twenty glioblastomas and twenty-one single brain metastases) were retrospectively evaluated. MRI images were acquired before surgery, radiotherapy and chemotherapy. MRIs were analysed with Olea Sphere® 3.0 (Olea Medical, La Ciotat, France), in particular with diffusion, perfusion and volume of interest segmentation plug-ins. FLAIR, 3D T1 MP-RAGE images after gadolinium, ADC and rCBV maps for each patient were co-registered by the OleaSphere software; this was followed by visual inspection to ensure adequate alignment. Volumes of interest (VOIs) of the lesions were drawn on enhanced 3D T1 MP-RAGE avoiding cyst or necrotic degeneration, and then projected on ADC and rCBV co-registered maps. Another 2 VOIs were drawn in the region of hyperintense cerebral oedema, surrounding the lesion (GB or BM) visible on FLAIR images. The first VOI was drawn into perilesional oedema within 5mm around the enhancing tumor. The second VOI was drawn into residual oedema. Both VOIs were projected on ADC and rCBV maps. Perfusion curves were obtained for each lesion and the value of signal recovery (SR) was reported. A Two sample T-Test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed to determine the optimal parameter in distinguishing GB from BM. RESULTSComparing all parameters evaluated for patients with GB and BM, the cerebral lesions were distinguishable with the mean ADC VOI- values of solid component, the PSR values and the mean and max rCBV values in the perilesional edema within 5mm around the enhancing tumor. According to ROC analysis, the area under the curve was 88%, 78% and 74% respectively for mean ADC VOI-values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95% .Using perilesional max rCBV>1,37, PSR>75% and mean lesional ADC<1x10 ⁻³ mm ² s ⁻¹ GB could be differentiated from solitary BM with sensitivity and specificity of 95% and 86%. CONCLUSION
We can conclude that lower values of ADC in the enhancing tumor volume and a higher percentage of signal recovery in perfusion curves, associated with higher values of rCBV in the peritumoral edema closed to the lesion, are strongly indicative of GB than solitary BM.
... Several previous studies reported that differentiating an SBM from HGG with MRI alone can be difficult [6][7][8][9][10][11] since the two lesions may present with similar morphological characteristics. Several algorithms have been proposed for differentiating between the two lesions with conventional and advanced MRI techniques, including automated/ semi-automated methods protocols [6][7][8][9][10][11]. ...
... Several previous studies reported that differentiating an SBM from HGG with MRI alone can be difficult [6][7][8][9][10][11] since the two lesions may present with similar morphological characteristics. Several algorithms have been proposed for differentiating between the two lesions with conventional and advanced MRI techniques, including automated/ semi-automated methods protocols [6][7][8][9][10][11]. These studies highlighted the importance of the peritumoral region; i.e., the high signal in T2-sequences surrounding the enhancing lesion. ...
Purpose:
To investigate the diagnostic efficacy of MRI diagnostic algorithms with an ascending automatization, in distinguishing between high-grade glioma (HGG) and solitary brain metastases (SBM).
Methods:
36 patients with histologically proven HGG (n = 18) or SBM (n = 18), matched by size and location were enrolled from a database containing 655 patients. Four different diagnostic algorithms were performed serially to mimic the clinical setting where a radiologist would typically seek out further findings to reach a decision: pure qualitative, analytic qualitative (based on standardized evaluation of tumor features), semi-quantitative (based on perfusion and diffusion cutoffs included in the literature) and a quantitative data-driven algorithm of the perfusion and diffusion parameters. The diagnostic yields of the four algorithms were tested with ROC analysis and Kendall coefficient of concordance.
Results:
Qualitative algorithm yielded sensitivity of 72.2%, specificity of 78.8%, and AUC of 0.75. Analytic qualitative algorithm distinguished HGG from SBM with a sensitivity of 100%, specificity of 77.7%, and an AUC of 0.889. The semi-quantitative algorithm yielded sensitivity of 94.4%, specificity of 83.3%, and AUC = 0.889. The data-driven algorithm yielded sensitivity = 94.4%, specificity = 100%, and AUC = 0.948. The concordance analysis between the four algorithms and the histologic findings showed moderate concordance for the first algorithm, (k = 0.501, P < 0.01), good concordance for the second (k = 0.798, P < 0.01), and third (k = 0.783, P < 0.01), and excellent concordance for fourth (k = 0.901, p < 0.0001).
Conclusion:
When differentiating HGG from SBM, an analytical qualitative algorithm outperformed qualitative algorithm, and obtained similar results compared to the semi-quantitative approach. However, the use of data-driven quantitative algorithm yielded an excellent differentiation.
... 8,9 In particular, DSC MR imaging is the most robust perfusion technique to perform such a task. 10,11 However, most studies found that DSC-derived relative CBV (rCBV) in intratumoral regions does not permit reliable differentiation between high-grade gliomas and metastases, 1,6,[12][13][14][15] which was thought to be related to contrast leakage from tumor vessels and, consequently, unreliable estimation of CBV. 10,16,17 rCBV measured in peritumoral regions may be effective in this regard, but this method inherently has some major disadvantages due to indefinite tumoral boundary and various definitions of the peritumoral area. ...
... Researchers have defined the tumoral and peritumoral areas in various ways. 1,13,15,41 For gliomas, the so-called peritumoral regions pathologically consist of benign changes, such as vasogenic edema and inflammatory reaction, as well as infiltration by tumor cells. Besides, the peritumoral edema areas of GBM and metastasis are usually extensive and may include different lobes and even extend to the whole cerebral hemisphere and the contralateral hemisphere. ...
Background and purpose:
Accurate differentiation between glioblastoma and solitary brain metastasis is of vital importance clinically. This study aimed to investigate the potential value of the inflow-based vascular-space-occupancy MR imaging technique, which has no need for an exogenous contrast agent, in differentiating glioblastoma and solitary brain metastasis and to compare it with DSC MR imaging.
Materials and methods:
Twenty patients with glioblastoma and 22 patients with solitary brain metastasis underwent inflow-based vascular-space-occupancy and DSC MR imaging with a 3T clinical scanner. Two neuroradiologists independently measured the maximum inflow-based vascular-space-occupancy-derived arteriolar CBV and DSC-derived CBV values in intratumoral regions and peritumoral T2-hyperintense regions, which were normalized to the contralateral white matter (relative arteriolar CBV and relative CBV, inflow-based vascular-space-occupancy relative arteriolar CBV, and DSC-relative CBV). The intraclass correlation coefficient, Student t test, or Mann-Whitney U test and receiver operating characteristic analysis were performed.
Results:
All parameters of both regions had good or excellent interobserver reliability (0.74∼0.89). In peritumoral T2-hyperintese regions, DSC-relative CBV (P < .001), inflow-based vascular-space-occupancy arteriolar CBV (P = .001), and relative arteriolar CBV (P = .005) were significantly higher in glioblastoma than in solitary brain metastasis, with areas under the curve of 0.94, 0.83, and 0.72 for discrimination, respectively. In the intratumoral region, both inflow-based vascular-space-occupancy arteriolar CBV and relative arteriolar CBV were significantly higher in glioblastoma than in solitary brain metastasis (both P < .001), with areas under the curve of 0.91 and 0.90, respectively. Intratumoral DSC-relative CBV showed no significant difference (P = .616) between the 2 groups.
Conclusions:
Inflow-based vascular-space-occupancy has the potential to discriminate glioblastoma from solitary brain metastasis, especially in the intratumoral region.
... Previous studies have demonstrated that decreased MD values and increased FA values show a positive correlation with the tumor cellularity (40,41). Based on these findings, multiple studies have used DTI metrics in along-tract and perilesional regions to define brain tumor grade (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53). Holly et al. (20,21) described that FA and MD values were, respectively, higher and lower in perilesional regions of gliomas than in metastases. ...
... Holly et al. (20,21) described that FA and MD values were, respectively, higher and lower in perilesional regions of gliomas than in metastases. On the other hand, other studies suggest that FA increases intratumorally in gliomas (20,21,47,(49)(50)(51). ...
Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology.
Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG).
Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate < 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p < 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI < 0) correlated with a higher number of abnormal MD segments ( R = −0.732) and RD segments ( R = −0.724).
Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.
... Despite this pathological difference, there have been mixed results using DTI metrics in the peritumoral region to differentiate between these two distinct tumor types (19). Although the majority of previous studies demonstrated no difference in the peritumoral FA across gliomas and metastases (6,10,11,14,(20)(21)(22)(23), few depicted higher FA in gliomas (11,19,24), while others found metastases to have higher FA (17,25). Most could not find a significant difference in MD or ADC between gliomas and metastatic lesions within the peritumoral region (4,11,14,15,17,19,21,24,25). ...
... Most could not find a significant difference in MD or ADC between gliomas and metastatic lesions within the peritumoral region (4,11,14,15,17,19,21,24,25). While others found that high-grade gliomas had significantly lower peritumoral MD values in comparison to the metastases (3,10,11,16,22), one study revealed higher MD in high-grade gliomas compared to metastatic tumors (26). ...
Differentiating high-grade gliomas and intracranial metastases through non-invasive imaging has been challenging. Here, we retrospectively compared both intratumoral and peritumoral fractional anisotropy (FA), mean diffusivity (MD), and fluid-attenuated inversion recovery (FLAIR) measurements between high-grade gliomas and metastases. Two methods were utilized to select peritumoral region of interest (ROI). The first method utilized the manual placement of four ROIs adjacent to the lesion. The second method utilized a semiautomated and proprietary MATLAB script to generate an ROI encompassing the entire tumor. The average peritumoral FA, MD, and FLAIR values were determined within the ROIs for both methods. Forty patients with high-grade gliomas and 44 with metastases were enrolled in this study. Thirty-five patients with high-grade glioma and 30 patients with metastases had FLAIR images. There was no significant difference in age, gender, or race between the two patient groups. The high-grade gliomas had a significantly higher tumor-to-brain area ratio compared to the metastases. There were no differences in average intratumoral FA, MD, and FLAIR values between the two groups. Both the manual sample method and the semiautomated peritumoral ring method resulted in significantly higher peritumoral FA and significantly lower peritumoral MD in high-grade gliomas compared to metastases (p < 0.05). No significant difference was found in FLAIR values between the two groups peritumorally. Receiver operating curve analysis revealed FA to be a more sensitive and specific metric to differentiate high-grade gliomas and metastases than MD. The differences in the peritumoral FA and MD values between high-grade gliomas and metastases seemed due to the infiltration of glioma to the surrounding brain parenchyma.
... Several imaging techniques have been used to distinguish brain metastases from GBMs, including magnetic resonance spectroscopy (Ishimaru et al., 2001;Opstad et al., 2004;Tsougos et al., 2012), dynamic susceptibility contrast-enhanced (CE) scanning (Cha et al., 2007;Blasel et al., 2010;Ma et al., 2010;Server et al., 2011;Lehmann et al., 2012;Tsougos et al., 2012;Bauer et al., 2015;Askaner et al., 2019;She et al., 2019), diffusion tensor imaging (Byrnes et al., 2011), diffusion-weighted imaging (Byrnes et al., 2011), and three-dimensional-arterial spin labeling (Lin et al., 2016). With the development of radiomics and extraction technology, texture features are increasingly used to distinguish between GBM and metastases. ...
... Machine learning can use high-throughput radiomics information to identify different neoplastic diseases (Petrujkić et al., 2019). In one study, information extracted from dynamic magnetic sensitivity CE scanning was able to differentiate GBMs from metastases with 98% accuracy (Bauer et al., 2015). In another study (Chen et al., 2013), Bayesian networkbased decision support systems were used to differentiate GBMs from solitary metastases with 94% accuracy and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.90. ...
Purpose
The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning.
Materials and Methods
Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas.
Results
Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases.
Conclusion
The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.
... Numerous studies have reported distinguishing HGGs with solitary brain metastasis using DWI and diffusion tensor imaging (DTI) (22,23). The focus of this differentiation is to distinguish infiltrative edema caused by the glioma from metastatic vasogenic edema. ...
Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.
... Of note, description of the physics behind functional MRI has previously been described in numerous peer-reviewed articles [20] and as such we will not discuss them at this time. Transcranial magnetic stimulation and diffusion and perfusion MRIs are additional methods currently used to map tumor location [21][22][23]. The latter two diagnostic procedures are useful for evaluating tumor phenotype(s) as heterogeneity among tumors always exist and can impact the response to therapeutics. ...
Brain tumors often contain many subsets of cells that are related but genetically distinct. The extent of this intra-tumor heterogeneity is daunting as inter-patient diversity has always been one of the major obstacles in designing effective treatments. The ailing brain is also exposed to toxic molecules that further facilitate the development, maintenance and propagation of metastatic lesions. One of these toxic molecules is the ubiquitous neurotransmitter, glutamate. Although glutamate signaling pathways shape brain networks during development, some glioma cells acquire the ability to produce and release glutamate into reverberating synapses which allows for selective growth advantage. In this brief review, we discuss the interplay between glutamate transmission and glioma biology and describe current therapeutic strategies used to limit metastatic lesions in the mature brain.
... To define a better diagnostic tool, previous studies have explored various advanced quantitative MR techniques, such as diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), dynamic susceptibility contrast (DSC) perfusion, arterial spin labeling (ASL) perfusion, and spectroscopy, aimed towards excavating more hidden imaging information that would help characterize the physiological and metabolic differences between GBM and SBM [6][7][8][9][10][11][12]. Whereas the data were inspiring, their attempts failed to provide adequate diagnostic confidence due to the inconsistencies in the MR-derived parameters. ...
This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).
... Once again, interrogation of the peritumoral region was most helpful in discriminating metastases and glioblastoma, using a combination of rCBV, FA, and MD. This led to an AUC of 0.98 (Bauer et al., 2015). Others have used a multiparametric approach based on both perfusion and MRS. ...
Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.
... To date, several studies have focused on peritumoral areas to differentiate cerebral metastases from malignant gliomas. The usefulness of advanced MRI techniques such as DTI (6,(27)(28)(29)(30)(31)(32)(33), perfusionweighted imaging (5,28,32,(34)(35)(36)(37)(38)(39)(40)(41)(42)(43), magnetic resonance spectroscopy (5,7,28,36,(44)(45)(46), amide proton transfer imaging (8) and combinations thereof (47,48) have been reported. ...
Background and objective: Phase difference enhanced imaging (PADRE), a new phase-related MRI technique, can enhance both paramagnetic and diamagnetic substances, and select which phases to be enhanced. Utilizing these characteristics, we developed color map of PADRE (Color PADRE), which enables simultaneous visualization of myelin-rich structures and veins. Our aim was to determine whether Color PADRE is sufficient to delineate the characteristics of non-gadolinium-enhancing T2-hyperintense regions related with metastatic tumors (MTs), diffuse astrocytomas (DAs) and glioblastomas (GBs), and whether it can contribute to the differentiation of MTs from GBs.
Methods: Color PADRE images of 11 patients with MTs, nine with DAs and 17 with GBs were created by combining tissue-enhanced, vessel-enhanced and magnitude images of PADRE, and then retrospectively reviewed. First, predominant visibility of superficial white matter and deep medullary veins within non-gadolinium-enhancing T2-hyperintense regions were compared among the three groups. Then, the discriminatory power to differentiate MTs from GBs was assessed using receiver operating characteristic analysis.
Results: The degree of visibility of superficial white matter was significantly better in MTs than in GBs (p = 0.017), better in GBs than in DAs (p = 0.014), and better in MTs than in DAs (p = 0.0021). On the contrary, the difference in the visibility of deep medullary veins was not significant (p = 0.065). The area under the receiver operating characteristic curve to discriminate MTs from GBs was 0.76 with a sensitivity of 80% and specificity of 64%.
Conclusion: Visibility of superficial white matter on Color PADRE reflects inferred differences in the proportion of vasogenic edema and tumoral infiltration within non-gadolinium-enhancing T2-hyperintense regions of MTs, DAs and GBs. Evaluation of peritumoral areas on Color PADRE can help to distinguish MTs from GBs.
... Distinguishing them is particularly complicated when there is no evidence of a previous neoplasm. In these cases, more specific techniques such as PET (Positron Emission Tomography), specialized magnetic resonance imaging (MRI) sequences such as spectroscopy, diffusion/perfusion, and other forms of quantitative analysis can be used to clarify the origin of these lesions (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18); however, in many centers, these techniques are not available, their acquisition and interpretation can sometimes be challenging and have a non-negligible margin of error. ...
Background
The differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM.
Methods
We performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM via the transfer learning method using Inception V3. Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN) and k-nearest neighbor (kNN) were used as classification algorithms. After the models' training, ten-fold stratified cross-validation was performed. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision.
Results
A total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89 % respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79 to 95 % respectively.
Conclusion
Automated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.
... molecular diffusion coefficient) will be of interest. 4,6,9,11,19,[24][25][26][27][28]30,35,42,43,49,85 The association of VEGF and inflammatory marker, interleukin-6 (IL-6), is another potential research interest as angiogenesis is also highly related to inflammation. 75 Future works in the area of radiogenomics should explore molecular imaging, nanoparticle imaging, computer-aided detection, and targeted therapies. ...
Objective:
The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer underlying biology of gliomas and correlation with imaging features. Methods: Literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles were relevant to molecular biomarkers linked to imaging biomarkers. Results: Radiogenomic markers found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). Summaries of the relationship of morphology with selected gene expressions and imaging characteristic, prognosis and therapeutic response were presented. Conclusion: Advances in knowledge: Radiogenomics offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
... To conclude, making a differential diagnosis between glioblastoma and brain metastases using only standard MRI sequences alone could be a challenging task (5). ...
Purpose:
The first aim of this study was to compare the intratumoral and peritumoral blood flow parameters in glioblastomas and brain metastases measured by pseudocontinuous arterial spin labeling MRI (3D pCASL). The second aim of this study was to determine whether pCASL could aid in identifying the source of brain metastases.
Materials and methods:
This study included 173 patients aged 12 to 83 years (median age-61 years), who were observed at the National Medical Research Center for Neurosurgery. All patients underwent preoperative MRI with pCASL perfusion. Thereafter patients were operated on and received histological diagnosis. No patients received preoperative chemo or radiotherapy.
Results:
The values of maximum and normalized intratumoral blood flow were significantly higher in the group with gliblastoma than in the group with brain metastases: 168.98 + -91.96 versus 152.1 + -173.32 and 7.6 + -8.4 versus 9.3 + -5.33 respectively (p <0.01). However, ROC analysis showed low AUC specificity and sensitivity (0.64, 70%, 60% for mTBF and 0.66, 77%, 62% for nTBF). Peritumoral blood flow parameters were also higher in the glioblastoma group (29.61 + -22.89 versus 16.58 + -6.46 for mTBF and 1.63 + -1.14 versus 0.88 + -0.38 for nTBF, respectively; p <0.01). ROC analysis showed the following measurements of AUC, specificity, and sensitivity (0.75, 68%, 73% for mTBF and 0.77, 58%, 91% for nTBF). Regarding pCASL and various histological subsets of brain metastases, the study found statistically significant differences between the lung and melanoma metastases and the lung and kidney metastases. ROC analysis gave the following values for lung and melanoma metastases: AUC-0.76, specificity-75%, and sensitivity-73% for mTBF; 0.83, 67%, and 93% respectively, for nTBF. For lung and kidney metastases: AUC-0.74, specificity-70%, and sensitivity-93% for mTBF; 0.75, 70%, and 93% respectively, for nTBF.
Conclusions:
pCASL could aid in differential diagnosis between glioblastoma and brain metastases. Measurement of peritumoral blood flow demonstrates higher specificity and sensitivity than with intratumoral blood flow. Moreover, pCASL provides the ability to distinguish lung metastases from kidney and melanoma metastases.
Background and Purpose
Differentiation between glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) remains a challenge in neuroradiology with up to 40% of the cases to be incorrectly classified using only conventional MRI. The inclusion of perfusion MRI parameters provides characteristic features that could support the distinction of these pathological entities. On these grounds, we aim to use a perfusion gradient in the peritumoral edema.
Methods
Twenty-four patients with GBM or an SBM underwent conventional and perfusion MR imaging sequences before tumors’ surgical resection. After postprocessing of the images, quantification of dynamic susceptibility contrast (DSC) perfusion parameters was made. Three concentric areas around the tumor were defined in each case. The monocompartimental and pharmacokinetics parameters of perfusion MRI were analyzed in both series.
Results
DSC perfusion MRI models can provide useful information for the differentiation between GBM and SBM. It can be observed that most of the perfusion MR parameters (relative cerebral blood volume, relative cerebral blood flow, relative Ktrans, and relative volume fraction of the interstitial space) clearly show higher gradient for GBM than SBM. GBM also demonstrates higher heterogeneity in the peritumoral edema and most of the perfusion parameters demonstrate higher gradients in the area closest to the enhancing tumor.
Conclusion
Our results show that there is a difference in the perfusion parameters of the edema between GBM and SBM demonstrating a vascularization gradient. This could help not only for the diagnosis, but also for planning surgical or radiotherapy treatments delineating the real extension of the tumor.
Purpose:
The purpose of this article is to assess the diagnostic performance of arterial spin-labeling (ASL) magnetic resonance perfusion imaging to differentiate neoplastic from non-neoplastic brain lesions.
Material and methods:
This prospective study included 60 consecutive, newly diagnosed, untreated patients with intra-axial lesions with perilesional edema (PE) who underwent clinical magnetic resonance imaging including ASL sequences at 3T. Region of interest analysis was performed to obtain mean cerebral blood flow (CBF) values from lesion (L), PE and normal contralateral white matter (CWM). Normalized (n) CBF ratio was obtained by dividing the mean CBF value of L and PE by mean CBF value of CWM. Discriminant analyses were performed to determine the best cutoff value of nCBFL and nCBFPE in differentiating neoplastic from non-neoplastic lesions.
Results:
Thirty patients were in the neoplastic group (15 high-grade gliomas (HGGs), 15 metastases) and 30 in the non-neoplastic group (12 tuberculomas, 10 neurocysticercosis, four abscesses, two fungal granulomas and two tumefactive demyelination) based on final histopathology and clincoradiological diagnosis. We found higher nCBFL (6.65 ± 4.07 vs 1.68 ± 0.80, p < 0.001) and nCBFPE (1.86 ± 1.43 vs 0.74 ± 0.21, p < 0.001) values in the neoplastic group than non-neoplastic. For predicting neoplastic lesions, we found an nCBFL cutoff value of 1.89 (AUC 0.917; 95% CI 0.854 to 0.980; sensitivity 90%; specificity 73%) and nCBFPE value of 0.76 (AUC 0.783; 95% CI 0.675 to 0.891; sensitivity 80%; specificity 58%). Mean nCBFL was higher in HGGs (8.70 ± 4.16) compared to tuberculomas (1.98 ± 0.87); and nCBFPE was higher in HGGs (3.06 ± 1.53) compared to metastases (0.86 ± 0.34) and tuberculomas (0.73 ± 0.22) ( p < 0.001).
Conclusion:
ASL perfusion may help in distinguishing neoplastic from non-neoplastic brain lesions.
Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.
Objective:
The purpose of our study was to evaluate the efficacy of the relative cerebral blood volume (rCBV) gradient in the peritumoral brain zone (PBZ)-the difference in the rCBV values from the area closest to the enhancing lesion to the area closest to the healthy white matter-in differentiating glioblastoma (GB) from solitary brain metastasis (MET).
Methods:
A 3.0-T magnetic resonance imaging (MRI) machine was used to perform dynamic susceptibility contrast perfusion MRI (DSC-MRI) on 43 patients with a solitary brain tumor (24 GB, 19 MET). The rCBV ratios were acquired by DSC-MRI data in 3 regions of the PBZ (near the enhancing tumor, G1; intermediate distance from the enhancing tumor, G2; far from the enhancing tumor, G3). The maximum rCBV ratios in the PBZ (rCBVp) and the enhancing tumor were also calculated, respectively. The perfusion parameters were evaluated using the nonparametric Mann-Whitney test. The sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve were identified.
Results:
The rCBVp ratios and rCBV gradient in the PBZ were significantly higher in GB compared with MET (P < 0.05 for both rCBVp ratios and rCBV gradient). The threshold values of 0.50 or greater for rCBVp ratios provide sensitivity and specificity of 57.69% and 79.17%, respectively, for differentiation of GB from MET. Compared with rCBVp ratios, rCBV gradient had higher sensitivity (94.44%) and specificity (91.67%) using the threshold value of greater than 0.06.
Conclusions:
The parameter of rCBV gradient derived from DSC-MRI in the PBZ seems to be the most efficient parameter to differentiate GB from METs.
Background:
Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors.
Purpose:
To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA).
Material and methods:
Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis.
Results:
Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%.
Conclusion:
Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.
Purpose:
This study aimed to determine whether whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) and apparent diffusion coefficient (ADC) for contrast-enhancing lesions can be used to differentiate between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL).
Methods:
From 20 patients, 9 with PCNSL and 11 with GBM without any hemorrhagic lesions, underwent MRI, including diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging before surgery. Histogram analysis of nCBV and ADC from whole-tumor voxels in contrast-enhancing lesions was performed. An unpaired t-test was used to compare the mean values for each type of tumor. A multivariate logistic regression model (LRM) was performed to classify GBM and PCNSL using the best parameters of ADC and nCBV.
Results:
All nCBV histogram parameters of GBMs were larger than those of PCNSLs, but only average nCBV was statistically significant after Bonferroni correction. Meanwhile, ADC histogram parameters were also larger in GBM compared to those in PCNSL, but these differences were not statistically significant. According to receiver operating characteristic curve analysis, the nCBV average and ADC 25th percentile demonstrated the largest area under the curve with values of 0.869 and 0.838, respectively. The LRM combining these two parameters differentiated between GBM and PCNSL with a higher area under the curve value (Logit (P) = -21.12 + 10.00 × ADC 25th percentile (10-3 mm2/s) + 5.420 × nCBV mean, P < 0.001).
Conclusion:
Our results suggest that whole-tumor histogram analysis of nCBV and ADC combined can be a valuable objective diagnostic method for differentiating between GBM and PCNSL.
Background and purpose
Conventional magnetic resonance imaging (MRI) is sometimes difficult to distinguish primary central nervous system lymphoma (PCNSL) from other malignant brain tumors effectively. The study aimed to evaluate the diagnostic performance of arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE)-derived permeability parameters to differentiate PCNSL from high-grade glioma (HGG) and brain metastasis.
Materials and methods
Eight patients with PCNSL, twenty one patients with HGG and six brain metastasis underwent preoperative 3.0-T MR imaging including conventional, ASL and DCE. Quantitative parameters including relative cerebral blood flow (rCBF), extravascular extracellular volume fraction (Ve) and the volume transfer constant (Ktrans) among PCNSL, HGG and metastasis were compared with a one-way analysis of variance. In addition, the area under the receiver-operating characteristic (ROC) curve (AUC) was constructed to evaluate the differentiation diagnostic performance of each parameter and the combination.
Results
The PCNSL demonstrated significantly lower rCBF, higher Ktrans and Ve compared with HGG and metastasis. For the ROC analyses, both Ktrans and rCBF had good diagnostic performance for discriminating PCNSL from HGG and metastasis, with the AUC of 0.880 and 0.889. With the combination of rCBF and Ktrans, the diagnostic ability for PCNSL was improved with AUC of 0.986.
Conclusion
rCBF and Ktrans are useful parameters for differentiating PCNSL from HGG and brain metastasis. The combination of rCBF and Ktrans further helps to improve the diagnostic performance of PCNSL.
The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.
BACKGROUND
Tractography is a popular tool for visualizing the corticospinal tract (CST). However, results may be influenced by numerous variables, eg, the selection of seeding regions of interests (ROIs) or the chosen tracking algorithm.
OBJECTIVE
To compare different variable sets by correlating tractography results with intraoperative subcortical stimulation of the CST, correcting intraoperative brain shift by the use of intraoperative MRI.
METHODS
Seeding ROIs were created by means of motor cortex segmentation, functional MRI (fMRI), and navigated transcranial magnetic stimulation (nTMS). Based on these ROIs, tractography was run for each patient using a deterministic and a probabilistic algorithm. Tractographies were processed on pre- and postoperatively acquired data.
RESULTS
Using a linear mixed effects statistical model, best correlation between subcortical stimulation intensity and the distance between tractography and stimulation sites was achieved by using the segmented motor cortex as seeding ROI and applying the probabilistic algorithm on preoperatively acquired imaging sequences. Tractographies based on fMRI or nTMS results differed very little, but with enlargement of positive nTMS sites the stimulation-distance correlation of nTMS-based tractography improved.
CONCLUSION
Our results underline that the use of tractography demands for careful interpretation of its virtual results by considering all influencing variables.
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.
Objectives
Differentiation of glioblastomas (GBMs) and solitary brain metastases (SBMs) is an important clinical problem. The aim of this study was to determine whether amide proton transfer–weighted (APTW) imaging is useful for distinguishing GBMs from SBMs.Methods
We examined 31 patients with GBM and 17 with SBM. For each tumor, enhancing areas (EAs) and surrounding non-enhancing areas with T2-prolongation (peritumoral high signal intensity areas, PHAs) were manually segmented using fusion images of the post-contrast T1-weighted and T2-weighted images. The mean amide proton transfer signal intensities (APTSIs) were compared among the EAs, PHAs, and contralateral normal appearing white matter (NAWM) within each tumor type. Furthermore, we analyzed APTSI histograms to compare the EAs and PHAs of GBMs and SBMs.ResultsIn GBMs, the mean APTSI in EAs (2.92 ± 0.74%) was the highest, followed by that in PHAs (1.64 ± 0.83%, p < 0.001) and NAWM (0.43 ± 0.83%, p < 0.001). In SBMs, the mean APTSI in EAs (1.85 ± 0.99%) and PHAs (1.42 ± 0.45%) were significantly higher than that in NAWM (0.42 ± 0.30%, p < 0.001), whereas no significant difference was found between EAs and PHAs. The mean and 10th, 25th, 50th, 75th, and 90th percentiles for APT in EAs of GBMs were significantly higher than those of SBMs. However, no significant difference was found between GBMs and SBMs in any histogram parameters for PHA.ConclusionsAPTSI in EAs, but not PHAs, is useful for differentiation between GBMs and SBMs.Key Points
• Amide proton transfer–weighted imaging and histogram analysis in the enhancing tumor can provide useful information for differentiation between glioblastomas and solitary brain metastasis.
• Amide proton transfer signal intensity histogram parameters from peritumoral areas showed no significant difference between glioblastomas and solitary brain metastasis.
• Vasogenic edema alone can substantially increase amide proton transfer signal intensity which may mimic tumor invasion.
In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.
Rationale and objectives:
To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET).
Materials and methods:
Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm2, and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET.
Results:
Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05).
Conclusion:
The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.
MRI-based perfusion techniques have become an integral part of neuroimaging and several different MRI techniques are now available for analysis of tissue perfusion and related hemodynamic parameters. This chapter gives an overview of the three MRI-based perfusion imaging techniques; dynamic susceptibility contrast (DSC)-MRI, dynamic contrast-enhanced (DCE)-MRI and arterial spin labeling (ASL). An introduction to the physical principles of the three methods will be given, as well as the different kinetic models that are applied in the analysis. This is followed by an overview of current clinical applications of the three techniques in the main indication areas: CNS cancers, cerebrovascular disease, neurodegenerative disease, and demyelinating disease. Finally, some thoughts about the future direction of perfusion-based MRI techniques and their clinical impact will be given.
Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.
AIM
To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis.
Materials and methods
Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness.
RESULTS
Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET.
CONCLUSIONS
The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
Purpose:
Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM.
Procedures:
Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol.
Results:
Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040).
Conclusion:
Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.
Purpose
To assess the value of histogram analysis, using diffusion kurtosis imaging (DKI), in differentiating glioblastoma multiforme (GBM) from single brain metastasis (SBM) and to compare the diagnostic efficiency of different region of interest (ROI) placements.
Method
Sixty-seven patients with histologically confirmed GBM (n=35) and SBM (n=32) were recruited. Two ROIs—the contrast-enhanced area and whole-tumor area—were delineated across all slices. Eleven histogram parameters of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) from both ROIs were calculated. All histogram parameter values were compared between GBM and SBM, using the Mann–Whitney U test. The accuracies of different histogram parameters were compared using the McNemar test. Receiver operating characteristic (ROC) analyses were conducted to assess the diagnostic performance.
Results
In the contrast-enhanced area, FA10, FA25, FA75, FA90, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAskewness was significantly lower for GBM than for SBM. FA25 (0.815) had the highest area under the curve (AUC). In the whole-tumor area, FA10, FA25, FA75, FA90, FASD, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAmedian (0.805) had the highest AUC. The accuracy of FA25 in the contrast-enhanced area was significantly higher than that of the FAmedian in the whole-tumor area.
Conclusions
GBM and SBM can be differentiated using the DKI-based histogram analysis. Placing the ROI on the contrast-enhanced area results in better discrimination.
Perfusion refers to the biological process of blood flow through vascularized tissue and allows for sufficient delivery of vital nutrients to most organs in the body as well as removal of metabolic waste and heat. Thus, perfusion plays a critical role in determining physiological levels of oxygenation, bioenergetics status, and pH distributions. Neoplasms in the brain, including brain tumors, are typically characterized by irregular and insufficient perfusion from abnormal neo-vasculature. Consequently, brain tumors create a hostile microenvironment that promotes tumor aggressiveness and treatment resistance. Moreover, treatment-induced changes in physiological functions, such as perfusion, may occur rapidly and before any measurable reduction in tumor volume. Hence, spatial and temporal assessment of quantitative perfusion metrics is an ideal target for diagnosis and treatment response monitoring of brain tumors. While conventional magnetic resonance imaging (MRI) remains the gold standard for non-invasive characterization of tumors of the central nervous system (CNS), quantitative measures of perfusion by dynamic susceptibility contrast (DSC)-MRI and arterial spin labeling (ASL) have helped advance cancer imaging as a non-invasive diagnostic force in the fight against cancer. Here, we review DSC-MRI and ASL, and their current and potential use in the clinical management of brain tumors.
Imaging plays a major role in the management of brain metastases, both to diagnose metastases in the pretreatment period and to differentiate recurrent metastasis from radiation change in the posttreatment period. Since approximately half of brain metastases are solitary at initial presentation, imaging is important to differentiate metastasis from other neoplastic lesions, including primary brain tumors and nonneoplastic lesions. Magnetic resonance (MR) perfusion, particularly dynamic susceptibility contrast, MR spectroscopy, and diffusion-weighted imaging have all been extensively studied to assess the tumoral and peritumoral region to aid in this differential diagnosis. Susceptibility-weighted imaging has shown promise in diagnosing hemorrhagic metastases, including melanoma.
Objective:
We sought to differentiate glioblastomas from solitary brain metastasis using arterial spin labeling perfusion (ASL)- and diffusion tensor imaging (DTI)-derived metrics.
Methods:
A prospective study was done on 36 patients with provisional diagnosis of glioblastomas versus brain metastasis who underwent ASL and DTI of the brain. The tumor blood flow (TBF) and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) of the enhancing tumoral and peritumoral parts were measured.
Results:
There was a significant difference of TBF (P = 0.001) and MD (P = 0.001) of the tumoral and peritumoral parts of glioblastoma and metastasis (P = 0.001). There was a significant difference of FA of peritumoral part (P = 0.001) and insignificant difference of tumoral part (P = 0.06) between glioblastomas and metastasis. The cutoff of TBF of tumoral and peritumoral parts used for differentiation were 29.7 and 17.8 (mL/100 g/minute) revealed an area under the curve (AUC) of 0.943 and 0.937 with accuracy of 91.7% and 88.9%. The cutoff of MD of tumoral and peritumoral parts were 1.27 and 1.33 (10-3 mm2/second) revealed AUC of 0.840 and 0.987 and accuracy of 83.3% and 91.7%, respectively. Combined TBF, MD, and FA of the peritumoral part revealed AUC of 0.984 and accuracy of 91.7%.
Conclusions:
A combination of ASL- and DTI-derived metrics of the peritumoral part can be used for differentiation of glioblastomas from solitary brain metastasis.
Purpose: Seizures related to tumor growth are common in glioma patients, especially in low-grade glioma patients this is often the first tumor manifestation. We hypothesize that there are associations between preoperative seizures and morphologic features (e.g., tumor size, location) and histogram features in patients with glioblastoma (GB). Methods: Retrospectively, 160 consecutive patients with initial diagnosis and surgery of GB (WHO IV) and preoperative MRI were analyzed. Preoperative MRI sequences were co-registered (T2-FLAIR, T1-contrast, DTI) and tumors were segmented by a neuroradiologist using the software ITK-snap blinded to the clinical data. Tumor volume (FLAIR, T1-contrast) and histogram analyses of ADC- and FA-maps were recorded in the contrast enhancing tumor part (CET) and the non-enhancing peritumoral edema (FLAIR). Location was determined after co-registration of the data with an atlas. Permutation-based multiple-testing adjusted t statistics were calculated to compare imaging variables between patients with and without seizures. Results: Patients with seizures showed significantly smaller tumors (CET, adj. p = 0.029) than patients without preoperative seizures. Less seizures were observed in patients with tumor location in the right cingulate gyrus (adj. p = 0.048) and in the right caudate nucleus (adj. p = 0.009). Significant differences of histogram analyses of FA in the contrast enhancing tumor part were observed between patients with and without seizures considering also tumor location and size. Conclusion: Preoperative seizures in GB patients are associated with lower preoperative tumor volume. The different histogram analyses suggest that there might be microstructural differences in the contrast enhancing tumor part of patients with seizures measured by fractional anisotropy. Higher variance of GB presenting without seizures might indicate a more aggressive growth of these tumors.
Background:
Radiological and/or laboratory tests may be sometimes inadequate distinguishing glioblastoma from metastatic brain tumors. The aim of this study was to find possible predictive biomarkers produced from routine blood biochemistry analysis results evaluated preoperatively in each patient with solitary brain tumor in distinguishing glioblastoma from metastatic brain tumors as well as revealing short-term prognosis.
Methods:
Patients admitted to neurosurgery clinic between January 2015 and September 2018 were included in this study and they were divided into GLIOMA (n=12) and METASTASIS (n=17) groups. Patients' data consisted of age, gender, Glasgow Coma Scale scores, duration of stay in hospital, Glasgow Outcome Scale (GOS) scores and histopathological examination reports, hemoglobin level, leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, basophil and platelet count results, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio values, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) levels were evaluated preoperatively.
Results:
The CRP levels of METASTASIS group (143.10 mg/L) were higher than those of GLIOMA group (23.90 mg/L); and it was 82% sensitive and 75% specific in distinguishing metastatic brain tumor from glioblastoma if CRP value was >55.00 mg/L. A positive correlation was determined between GOS score and hemoglobin level and between ESR and CRP values. However, GOS scores were negatively correlated with the ESR level and duration of stay in hospital.
Conclusions:
Study results demonstrated that CRP values could be predictive biomarker in distinguishing metastatic brain tumor from glioblastoma. In addition, ESR, CRP, hemoglobin levels and duration of stay in hospital could be prognostic biomarkers in predicting short-term prognosis of patients with solitary brain tumor.
Primary and metastatic brain tumors can overlap in traditional imaging features detected on preoperative conventional magnetic resonance imaging (MRI). The research objective was to determine whether morphological vascular characteristics present in routine preoperative imaging using traditional MRI sequences are predictive of primary versus metastatic brain tumors; secondarily to determine association of conventional and vascular-related imaging parameters with intraoperative blood loss, pathological invasion, and World Health Organization (WHO) tumor grade. A retrospective review analyzed 100 consecutive intracranial tumor surgeries, 50 WHO grade II-IV gliomas and 50 intracranial metastases. Two blinded expert readers independently evaluated preoperative MRIs, obtained via standard morphological imaging sequences, for adjacent or intra-tumoral arterial aneurysm, peritumoral venous ectasia, prominence, or engorgement (“aberrant peritumoral vessels”), and prominent intra-tumoral flow voids. Multivariate analysis was performed to develop models predictive of glioma and glioblastoma (GBM). Aberrant peritumoral vessels and prominent intra-tumoral flow voids were statistically significant predictors of glioma in univariate analyses (p = 0.048, p = 0.001, respectively) and when combined in multivariate analysis (OR = 5.23, p = 0.001), particularly for GBM (OR = 9.08, p < 0.001). Multivariate modeling identified prominent intra-tumoral flow voids and FLAIR invasion as the strongest combined predictors of gliomas and GBM. Aberrant peritumoral vessels and larger tumor volume predicted higher intraoperative blood loss in all analyses. No vascular-related parameters predicted pathological invasion on multivariate analysis. Aberrant peritumoral vessels and prominent intra-tumoral flow voids were predictive of gliomas, specifically GBM. These vascular characteristics, evaluated on routine clinical preoperative MRI imaging, may aid in distinguishing glioma from brain metastases and may predict intraoperative blood loss.
On conventional magnetic resonance imaging (MRI), hemangioblastomas typically appear as mural nodules with an adjacent surrounding cyst or a solid mass in the cerebellum. However, hemangioblastomas sometimes cannot be reliably distinguished using this imaging technique from other tumors, especially pilocytic astrocytomas and metastatic tumors, because of their similar imaging findings and locations. Herein, we report three cases of cerebellar hemangioblastomas and review their findings on conventional and advanced MRI, including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced perfusion-weighted imaging (DSC-PWI), and magnetic resonance spectroscopy (MRS). Solid contrast-enhanced lesions of hemangioblastomas showed increased apparent diffusion coefficient values on DWI, increased relative cerebral blood volume ratio on DSC-PWI, and high lipid/lactate peak on MRS. Therefore, advanced MRI techniques can be helpful in understanding the pathological and metabolic changes of hemangioblastomas and may be useful for their characterization.
Previous studies showed a high diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation among glioblastomas, primary cerebral lymphomas (PCLs), and solitary brain metastases, whereas other studies reported a low or no diagnostic value of DWI and DTI in differentiation among the three types of brain malignant tumors. In order to enhance the strength of evidence, meta-analysis was conducted to summarize results of studies evaluating the diagnostic values of DWI or DTI in differentiation among the three types of brain malignant tumors. Articles evaluating the diagnostic values of DWI or DTI in differentiation among the three types of tumors and published before December 2019 were searched in databases (PubMed, Medline, Web of Science, EMBASE, and Google Scholar). A summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were calculated from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of each study using STATA 12.0 software and Meta-Disc Version 1.4. In addition, the summary receive-operating characteristic (SROC) curve was constructed. Ultimately, we included 19 diagnostic studies (including 735 glioblastomas patients, 31 PCLs patients, and 792 patients with solitary brain metastases). Regarding differentiation between glioblastomas and solitary brain metastases using DWI or DTI, the calculated pooled parameters were as follows: sensitivity, 0.84 [95% confidence interval (CI): 0.78-0.89]; specificity, 0.88 (95% CI: 0.83-0.92); PLR, 7.2 (95% CI: 4.6-11.3); NLR, 0.18 (95% CI: 0.12-0.27); and DOR, 41 (95% CI: 18-93). The analysis showed a significant heterogeneity (sensitivity, I2 = 91.31%, p < 0.01; specificity, I2 = 89.24%, p < 0.01). In conclusion, DWI and DTI showed a moderate diagnostic value for differentiating glioblastomas from solitary brain metastasis. Additionally, large-scale prospective studies are essential to explore differentiation between PCLs and solitary brain metastases using DWI or DTI.
Brain metastases are the most frequent brain tumors in adults [1] and represent about 25% of brain masses. Among patients with metastatic cancer, 40% will present with brain metastases [2]. These lesions are less frequently symptomatic than expected: only 19% of patients with newly diagnosed brain metastases have neurologic symptoms [3] whereas these lesions dramatically change patients’ prognosis. We will see in this chapter that imaging is central for patients’ care.
Perfusion CT has become widespread in clinical use of brain lesions over the last decade. This technique is an important tool both in assessing and managing acute ischemia. Perfusion MRI with different methods has been widely used in a larger area for diagnosis, characterization, treatment planning, interventional guiding, and also treatment monitoring procedures of brain lesions. In recent years, perfusion imaging techniques have come to the forefront in stroke, neurodegenerative diseases, and tumor characterization.
Background
Accurate diagnosis of brain tumor is crucial for adequate surgical strategy. Our institution follows a comprehensive preoperative evaluation based on clinical and imaging information.
Methods
To assess the precision of preoperative diagnosis, we compared the “top three list” of differential diagnosis (the first, second, and third diagnoses according to the WHO 2007 classification including grading) of 1061 brain tumors, prospectively and consecutively registered in preoperative case conferences from 2010 to the end of 2017, with postoperative pathology reports.
Results
The correct diagnosis rate (sensitivity) of the first diagnosis was 75.8% in total. The sensitivity of the first diagnosis was high (84–94%) in hypothalamic-pituitary and extra-axial tumors, 67–75% in intra-axial tumors, and relatively low (29–42%) in intraventricular and pineal region tumors. Among major three intra-axial tumors, the sensitivity was highest in brain metastasis: 83.8% followed by malignant lymphoma: 81.4% and glioblastoma multiforme: 73.1%. Sensitivity was generally low (≦60%) in other gliomas. These sensitivities generally improved when the second and third diagnoses were included; 86.3% in total. Positive predictive value (PPV) was 76.9% in total. All the three preoperative diagnoses were incorrect in 3.4% (36/1061) of cases even when broader brain tumor classification was applied.
Conclusion
Our institutional experience on precision of preoperative diagnosis appeared around 75% of sensitivity and PPV for brain tumor. Sensitivity improved by 10% when the second and third diagnoses were included. Neurosurgeons should be aware of these features of precision in preoperative differential diagnosis of a brain tumor for better surgical strategy and to adequately inform the patients.
The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier. Copyright © 2014 John Wiley & Sons, Ltd.
Differentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases.
One hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined.
The fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model.
Classification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.
Purpose: To assess the contribution of 1H-magnetic resonance spectroscopy (1H-MRS), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic susceptibility contrast-enhanced (DSCE) imaging metrics in the differentiation of glioblastomas from solitary metastasis, and particularly to clarify the controversial reports regarding the hypothesis that there should be a significant differentiation between the intratumoral and peritumoral areas. Methods: Conventional MR imaging, 1H-MRS, DWI, DTI and DSCE MRI was performed on 49 patients (35 glioblastomas multiforme, 14 metastases) using a 3.0-T MR unit. Metabolite ratios, apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV) were measured in the intratumoral and peritumoral regions of the lesions. Receiver-operating characteristic analysis was used to obtain the cut-off values for the parameters presenting a statistical difference between the two tumor groups. Furthermore, we investigated the potential effect of the region of interest (ROI) size on the quantification of diffusion properties in the intratumoral region of the lesions, by applying two different ROI methods. Results: Peritumoral N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, Cho/NAA and rCBV significantly differentiated glioblastomas from intracranial metastases. ADC and FA presented no significant difference between the two tumor groups. Conclusions:
1H-MRS and dynamic susceptibility measurements in the peritumoral regions may definitely aid in the differentiation of glioblastomas and solitary metastases. The quantification of the diffusion properties in the intratumoral region is independent of the ROI size placed.
Glioblastomas, brain metastases, and PCLs may have similar enhancement patterns on MR imaging, making the differential diagnosis difficult or even impossible. The purpose of this study was to determine whether a combination of DTI and DSC can assist in the differentiation of glioblastomas, solitary brain metastases, and PCLs.
Twenty-six glioblastomas, 25 brain metastases, and 16 PCLs were retrospectively identified. DTI metrics, including FA, ADC, CL, CP, CS, and rCBV were measured from the enhancing, immediate peritumoral and distant peritumoral regions. A 2-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.
From the enhancing region, significantly elevated FA, CL, and CP and decreased CS values were observed in glioblastomas compared with brain metastases and PCLs (P < .001), whereas ADC, rCBV, and rCBV(max) values of glioblastomas were significantly higher than those of PCLs (P < .01). The best model to distinguish glioblastomas from nonglioblastomas consisted of ADC, CS (or FA) from the enhancing region, and rCBV from the immediate peritumoral region, resulting in AUC = 0.938. The best predictor to differentiate PCLs from brain metastases comprised ADC from the enhancing region and CP from the immediate peritumoral region with AUC = 0.909.
The combination of DTI metrics and rCBV measurement can help in the differentiation of glioblastomas from brain metastases and PCLs.
To investigate whether estimates of relative cerebral blood volume (rCBV) in brain tumors, obtained by using dynamic susceptibility-weighted contrast material-enhanced magnetic resonance (MR) imaging vary with choice of data acquisition and postprocessing methods.
Four acquisition methods were used to collect data in 22 high-grade glioma patients, with informed written consent under HIPAA-compliant guidelines approved by the institutional review board. During bolus administration of a standard single dose of gadolinium-based contrast agent (0.1 mmol per kilogram of body weight), one of three acquisition methods was used: gradient-echo (GRE) echo-planar imaging (echo time [TE], 30 msec; flip angle, 90 degrees ; n = 10), small-flip-angle GRE echo-planar imaging (TE, 54 msec; flip angle, 35 degrees ; n = 7), or dual-echo GRE spiral-out imaging (TE, 3.3 and 30 msec; flip angle, 72 degrees ; n = 5). Next, GRE echo-planar imaging (TE, 30 msec; flip angle, 90 degrees ; n = 22) was used to collect data during administration of a second dose of contrast agent (0.2 mmol/kg). Subsequently, six methods of analysis were used to calculate rCBV. Mean rCBV values from whole tumor, tumor hot spots, and contralateral brain were normalized to mean rCBV in normal-appearing white matter.
Friedman two-way analysis of variance and Kruskal-Wallis one-way analysis of variance results indicated that qualitative rCBV values were dependent on acquisition and postprocessing methods for both tumor and contralateral brain. By using the nonparametric Mann-Whitney test, a consistently positive (greater than zero) tumor-contralateral brain rCBV ratio resulted when either the preload-postprocessing correction approach or dual-echo acquisition approach (P < .008 for both methods) was used.
The dependence of tumor rCBV on the choice of acquisition and postprocessing methods is caused by their varying sensitivities to T1 and T2 and/or T2* leakage effects. The preload-correction approach and dual-echo acquisition approach are the most robust choices for the evaluation of brain tumors when the possibility of contrast agent extravasation exists.
Astrocytic tumors are divided into two basic categories: circumscribed (grade I) or diffuse (grades II-IV). All diffuse astrocytomas tend to progress to grade IV astrocytoma, which is synonymous with glioblastoma multiforme (GBM). GBMs are characterized by marked neovascularity, increased mitosis, greater degree of cellularity and nuclear pleomorphism, and microscopic evidence of necrosis. Several genetic abnormalities have been associated with the development of GBM: In some cases, the abnormality is inherited (e.g., Li-Fraumeni syndrome); in others, genetic alteration appears to result from mutation into an oncogene or deterioration of the tumor-suppressor gene p53. A common, distinctive histopathologic feature of GBM is pseudopalisading. The most common imaging appearance of GBM is a large heterogeneous mass in the supratentorial white matter that exerts considerable mass effect. Less frequently, GBM can occur near the dura mater or in the corpus callosum, posterior fossa, and spinal cord. GBM typically contains central areas of necrosis, has thick irregular walls, and is surrounded by extensive, vasogenic edema, but the tumor may also have thin round walls, scant edema, or a cystic appearance with a mural nodule. GBMs most commonly metastasize from their original location by direct extension along white matter tracts; however, cerebrospinal fluid, subependymal, and hematogenous spread also can occur. Given the rapidly growing body of knowledge about GBM, the radiologist's role is more important than ever in accurate and timely diagnosis.
Despite major advances in the field of tumor angiogenesis, relatively little attention has been paid to the permeability of blood vessels in tumors. The leakiness of tumor vessels is well documented in experimental tumor models and in human cancer, but the mechanism is poorly understood, as are the implications to the rate of cancer growth, predisposition to metastasis, and delivery of macromolecular therapeutics to tumor cells. Sixteen experts in the fields of cancer biology and vascular biology gathered at the William Guy Forbeck "Focus on the Future" Conference to discuss this topic. The meeting was the first of its kind focused on the significance of blood vessel leakiness in tumors. The participants discussed the cellular basis of tumor vessel leakiness, endothelial barrier function of blood vessels, monitoring tumor vessel leakiness, mediators of endothelial leakiness, consequences of tumor vessel leakiness, genomic analysis of vascular targets, targeting drugs to tumor vessels, and therapeutic manipulation of tumor vessels. The group concluded that a more complete understanding of the basic biology of tumor vessels will be necessary to fully appreciate the consequences of vessel leakiness in cancer. New research tools such as intravital measurements of tumor blood flow and vessel leakiness, in vivo phage display, magnetic resonance imaging, and use of selective angiogenesis inhibitors will contribute to this understanding.
To determine the utility of perfusion MR imaging in the differential diagnosis of brain tumors.
Fifty-seven patients with pathologically proven brain tumors (21 high-grade gliomas, 8 low-grade gliomas, 8 lymphomas, 6 hemangioblastomas, 7 metastases, and 7 various other tumors) were included in this study. Relative cerebral blood volume (rCBV) and time-to-peak (TTP) ratios were quantitatively analyzed and the rCBV grade of each tumor was also visually assessed on an rCBV map.
The highest rCBV ratios were seen in hemangioblastomas, followed by high-grade gliomas, metastases, low-grade gliomas, and lymphomas. There was no significant difference in TTP ratios between each tumor group (p<0.05). At visual assessment, rCBV was high in 17 (81%) of 21 high-grade gliomas and in 4 (50%) of 8 low-grade gliomas. Hemangioblastomas showed the highest rCBV and lymphomas the lowest.
Perfusion MR imaging may be helpful in the differentiation of thevarious solid tumors found in the brain, and in assessing the grade of the various glial tumors occurring there.
Relative cerebral blood volume (rCBV) and vascular permeability (K(trans)) permit in vivo assessment of glioma microvasculature. We assessed the associations between rCBV and K(trans) derived from dynamic, susceptibility-weighted, contrast-enhanced (DSC) MR imaging and tumor grade and between rCBV and K(trans).
Seventy-three patients with primary gliomas underwent conventional and DSC MR imaging. rCBVs were obtained from regions of maximal abnormality for each lesion on rCBV color maps. K(trans) was derived from a pharmacokinetic modeling algorithm. Histopathologic grade was compared with rCBV and K(trans) (Tukey honestly significant difference). Spearman and Pearson correlation factors were determined between rCBV, K(trans), and tumor grade. The diagnostic utility of rCBV and K(trans) in discriminating grade II or III tumors from grade I tumors was assessed by logistic regression.
rCBV was significantly different for all three grades (P </=.0005). K(trans) was significantly different between grade I and grade II or III (P =.027) but not between other grades or combinations of grades. Spearman rank and Pearson correlations, respectively, were as follows: rCBV and grade, r = 0.817 and r = 0.771; K(trans) and grade, r = 0.234 and r = 0.277; and rCBV and K(trans), r = 0.266 and r = 0.163. Only rCBV was significantly predictive of high-grade gliomas (P <.0001).
rCBV with strongly correlated with tumor grade; the correlation between K(trans) and tumor grade was weaker. rCBV and K(trans) were positively but weakly correlated, suggesting that these parameters demonstrate different tumor characteristics. rCBV is a more significant predictor of high-grade glioma than K(trans).
The relationship between socioeconomic status and health care disparities in the incidence of brain tumors is unclear.
To identify the associations between age, sex, and Medicaid enrollment and the incidence of primary malignant brain tumors in Michigan in 1996 and 1997.
Records were obtained from the Michigan Cancer Surveillance Program on the 1,006 incident cases during this period and cross-checked with Medicaid enrollment files.
Persons enrolled in Medicaid were more likely than non-enrolled persons to develop a malignant brain tumor of any type, a glioblastoma multiforme, and an astrocytoma for certain subgroups. In addition, incidence rates for malignant brain tumors in persons enrolled in Medicaid peaked at a younger age.
Sociodemographic status may be associated with cerebral malignancy and should be considered when targeting treatment and educational interventions at persons at risk.
To determine if apparent diffusion coefficient (ADC) can be used to differentiate brain tumors at magnetic resonance (MR) imaging.
Institutional review board approval or informed patient consent was not required. MR images were reviewed retrospectively in 275 patients with brain tumors: 147 males and 128 females 1-81 years old, treated between September 1997 and July 2003. Regions of interest were placed manually in tumor regions on MR images, and ADC was calculated with a five-point regression method at b values of 0, 250, 500, 750, and 1000 sec/mm2. ADC values were average values in tumor. All brain tumor subgroups were analyzed. Logistic discriminant analysis was performed by using ADC, age, and patient sex as independent variables to discriminate among tumor groups.
A significant negative correlation existed between ADC and astrocytic tumors of World Health Organization grades 2-4 (grade 2 vs grades 3 and 4, accuracy of 91.3% [P < .01]; grade 3 vs 4, accuracy of 82.4% [P < .01]). ADC of dysembryoplastic neuroepithelial tumors (DNTs) was higher than that of astrocytic grade 2 tumors (accuracy, 100%) and other glioneuronal tumors. ADC of malignant lymphomas was lower than that of glioblastomas and metastatic tumors (accuracy, 83.6%; P < .01). ADC of primitive neuroectodermal tumors (PNETs) was lower than that of ependymomas (accuracy, 100%). ADC of meningiomas was lower than that of schwannomas (accuracy, 92.4%; P < .01). ADC of craniopharyngiomas was higher than that of pituitary adenomas (accuracy, 85.2%; P < .05). ADC of epidermoid tumors was lower than that of chordomas (accuracy, 100%). In meningiomas, ADC was not indicative of malignancy grade or histologic subtype.
ADC is useful for differentiation of some human brain tumors, particularly DNT, malignant lymphomas versus glioblastomas and metastatic tumors, and ependymomas versus PNETs.
In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.
A novel technique for manipulating contrast in projection reconstruction MRI is described. The method takes advantage of the fact that the central region of k-space is oversampled, allowing one to choose different filters to enhance or reduce the amount that each view contributes to the central region, which dominates image contrast. The technique is implemented into a fast spin-echo (FSE) sequence, and it is shown that multiple T-2-weighted images can be reconstructed from a single image data set. These images are shown to be nearly identical to those acquired with the Cartesian-sampled FSE sequence at different effective echo times. Further, it is demonstrated that T-2 maps can be generated from a single image data set. This technique also has the potential to be useful in dynamic contrast enhancement studies, capable of yielding a series of images at a significantly higher effective temporal resolution than what is currently possible with other methods, without sacrificing spatial resolution. (C) 2000 Wiley-Liss, Inc.
The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann-Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.
Analytical expressions of the diffusion tensor of water, D, and of scalar invariants derived from it, are given in terms of the intensities of seven diffusion-weighted images (DWIs). These formulas simplify the post-processing steps required in diffusion tensor imaging, including estimating D in each voxel (from the set of b-matrices and their corresponding DWIs), and then computing its eigenvalues, eigenvectors, and scalar invariants. In a study conducted using artifact-free DWIs with high diffusion weighting (bmax ˜ 900 s/mm2), maps of Trace(D) and the Relative and Lattice Anisotropy indices calculated analytically and by multivariate linear regression showed excellent agreement in brain parenchyma of a healthy living cat. However, the quality of the analytically computed maps degraded markedly as diffusion weighting was reduced. Although diffusion tensor MRI with seven DWIs may be useful for clinical applications where rapid scanning and data processing are required, it does not provide estimates of the uncertainty of the measured imaging parameters, rendering it susceptible to noise and systematic artifacts. Therefore, care should be taken when using this technique in radiological applications.
Purpose:
To assess the performance of parameters used in conventional magnetic resonance imaging (MRI), perfusion-weighted MR imaging (PWI) and visual texture analysis, alone and in combination, to differentiate a single brain metastasis (MET) from glioblastoma multiforme (GBM).
Patients and methods:
In a retrospective study of 50 patients (41 GBM and 14 MET) who underwent T2/FLAIR/T1(post-contrast) imaging and PWI, morphological (circularity, surface area), perfusion (rCBV in the ring-like tumor area, rCBV in the peritumoral area, percentage of signal intensity recovery at the end of first pass) and texture parameters in the peritumoral area were estimated. Statistical differences and performances were assessed using Wilcoxon's test and receiver operating characteristic curves, respectively. Multiparametric classification of tumors was performed using k-means clustering.
Results:
Significant statistical differences in circularity, surface area, rCBVs, percentage of signal intensity recovery and texture parameters (energy, entropy, homogeneity, correlation, inverse differential moment, sum average) were observed between MET and GBM (P<0.05). Moderate-to-good classification performances were found with these parameters. Clustering based on rCBV and texture parameters (contrast, sum average) differentiated MET from GBM with a sensitivity of 92% and a specificity of 71%.
Conclusion:
Combining perfusion and visual texture parameters within a statistical classifier significantly improved the differentiation of a single brain MET and GBM.
Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs.
The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called "clusters") represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure.
A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases.
A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.
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.
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.
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.
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.
Brain metastases are the most common intracranial tumor in adults. The incidence of metastases is thought to be rising due to better detection and treatment of systemic malignancy. More widespread use and improved quality of MRI may lead to early detection of brain metastases. Available evidence suggests that survival is longer and quality of life improved if brain metastases are treated aggressively. This article reviews current therapeutic management used for brain metastases. To select the appropriate therapy, the physician must consider the extent of the systemic disease, primary histology, and patient age and performance status, as well as the number, size, and location of the brain metastases. Available treatment options include whole-brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), surgery, and chemotherapy. Multidisciplinary approaches such as the combination of WBRT with SRS or surgery have shown superior results in terms of survival time, neurocognitive function, and quality of life. The utility and optimal use of chemotherapy and radiosensitizing agents is less clear. It is hoped that further advances and multidisciplinary approaches currently under study will result in improved patient outcomes.
The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58.4 years) and brain metastases (13 men, 12 women, mean age 56.3 years) were included in this study. Contrast-enhanced T1-weighted, fluid-attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p<0.05), and the differences from the enhancing regions were most significant (p<0.001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0.90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0.98. We conclude that DTI metrics, used individually or combined, have a potential as a non-invasive measure to differentiate glioblastomas from metastases.
A noninvasive technique for assessing tumor tissue characteristics is required to assist preoperative surgical planning for malignant brain tumors. Preoperative information on tumor cell density within a tumor would help better define the target for tumor biopsy, resulting in more accurate diagnosis and grading of malignant brain tumors. One possible source of this information is diffusion tensor imaging (DTI), although to date studies have focused on its ability to delineate white matter fiber tracks by fiber-tracking and to detect tumor infiltration around the tumor and normal white matter interface. However, the use of DTI for providing information on cell density has also been examined, although with the controversial results. In addition the exact relationships between cell density and the two key values that DTI provides, namely fractional anisotropy (FA) and mean diffusivity (MD), still need to be investigated. In the present study we performed a retrospective investigation of tumor cell density and FA and MD values in biopsy cases. We found that FA has a good positive correlation (R=0.75) and MD has a good negative correlation (R=0.70) with tumor cell density within the tumor core. Similar correlation was observed between the Ki-67 labeling index and FA (R=0.71) and MD (R=0.62). Thus, measurement of both FA and MD within the tumor core has a potential to provide detailed information on tumor cell density within the tumor. Although data obtained from DTI should be interpreted carefully and comprehensively with other imaging modalities such as positron emission tomography, DTI seems to be informative for planning the best biopsy target containing the highest cell density.
The ultrastructural characteristics of blood vessels within 18 metastatic neoplasms of the brain have been studied. The abnormal permeability to protein-bound tracers was found to be related to defective capillary endothelium. Abnormalities include gap functions, fenestrations of membranes, and open normal junctions. The abnormal capillaries had the character of capillaries from current tissue. Endothelium cell abnormalities were not found in surrounding edematous brain.
The method of graphical analysis for the evaluation of sequential data (e.g., tissue and blood concentrations over time) in which the test substance is irreversibly trapped in the system has been expanded. A simpler derivation of the original analysis is presented. General equations are derived that can be used to analyze tissue uptake data when the blood-plasma concentration of the test substance cannot be easily measured. In addition, general equations are derived for situations when trapping of the test substance is incomplete and for a combination of these two conditions. These derivations are independent of the actual configuration of the compartmental system being analyzed and show what information can be obtained for the period when the reversible compartments are in effective steady state with the blood. This approach is also shown to result in equations with at least one less nonlinear term than those derived from direct compartmental analysis. Specific applications of these equations are illustrated for a compartmental system with one reversible region (with or without reversible binding) and one irreversible region.
Analytical expressions of the diffusion tensor of water, D, and of scalar invariants derived from it, are given in terms of the intensities of seven diffusion-weighted images (DWIs). These formulas simplify the post-processing steps required in diffusion tensor imaging, including estimating D in each voxel (from the set of b-matrices and their corresponding DWIs), and then computing its eigenvalues, eigenvectors, and scalar invariants. In a study conducted using artifact-free DWIs with high diffusion weighting (bmax approximately 900 s/mm2, maps of Trace(D) and the Relative and Lattice Anisotropy indices calculated analytically and by multivariate linear regression showed excellent agreement in brain parenchyma of a healthy living cat. However, the quality of the analytically computed maps degraded markedly as diffusion weighting was reduced. Although diffusion tensor MRI with seven DWIs may be useful for clinical applications where rapid scanning and data processing are required, it does not provide estimates of the uncertainty of the measured imaging parameters, rendering it susceptible to noise and systematic artifacts. Therefore, care should be taken when using this technique in radiological applications.
We describe a standard set of quantity names and symbols related to the estimation of kinetic parameters from dynamic contrast-enhanced T(1)-weighted magnetic resonance imaging data, using diffusable agents such as gadopentetate dimeglumine (Gd-DTPA). These include a) the volume transfer constant K(trans) (min(-1)); b) the volume of extravascular extracellular space (EES) per unit volume of tissue v(e) (0 < v(e) < 1); and c) the flux rate constant between EES and plasma k(ep) (min(-1)). The rate constant is the ratio of the transfer constant to the EES (k(ep) = K(trans)/v(e)). Under flow-limited conditions K(trans) equals the blood plasma flow per unit volume of tissue; under permeability-limited conditions K(trans) equals the permeability surface area product per unit volume of tissue. We relate these quantities to previously published work from our groups; our future publications will refer to these standardized terms, and we propose that these be adopted as international standards.
A novel technique for manipulating contrast in projection reconstruction MRI is described. The method takes advantage of the fact that the central region of k-space is oversampled, allowing one to choose different filters to enhance or reduce the amount that each view contributes to the central region, which dominates image contrast. The technique is implemented into a fast spin-echo (FSE) sequence, and it is shown that multiple T(2)-weighted images can be reconstructed from a single image data set. These images are shown to be nearly identical to those acquired with the Cartesian-sampled FSE sequence at different effective echo times. Further, it is demonstrated that T(2) maps can be generated from a single image data set. This technique also has the potential to be useful in dynamic contrast enhancement studies, capable of yielding a series of images at a significantly higher effective temporal resolution than what is currently possible with other methods, without sacrificing spatial resolution.
The treatment of malignant glioma remains problematic. Surgical removal followed by external beam irradiation represents a standard treatment that has demonstrated a prolonged time to progression and survival. However, the capacity to locally invade normal brain invariably leads to formation of a recurrent tumor most often immediately adjacent to the site of resection. This clinical everyday experience prompts the hypothesis that improved local control of the tumor may translate into a delayed time to progression and possibly survival, specifically because systemic chemotherapy for most of these tumors has failed to significantly improve survival. Local treatment strategies including chemotherapy, gene therapy, and immunotherapy are rapidly developing and progressing to clinical trials. Several theoretical considerations suggest that these approaches may be promising in the treatment of brain tumors.
To determine whether perfusion-weighted and proton spectroscopic MR imaging can be used to differentiate high-grade primary gliomas and solitary metastases on the basis of differences in vascularity and metabolite levels in the peritumoral region.
Fifty-one patients with a solitary brain tumor (33 gliomas, 18 metastases) underwent conventional, contrast material--enhanced perfusion-weighted, and proton spectroscopic MR imaging before surgical resection or stereotactic biopsy. Of the 33 patients with gliomas, 22 underwent perfusion-weighted MR imaging; nine, spectroscopic MR imaging; and two underwent both. Of the 18 patients with metastases, 12 underwent perfusion-weighted MR imaging, and six, spectroscopic MR imaging. The peritumoral region was defined as the area in the white matter immediately adjacent to the enhancing (hyperintense on T2-weighted images, but not enhancing on postcontrast T1-weighted images) portion of the tumor. Relative cerebral blood volumes in these regions were calculated from perfusion-weighted MR data. Spectra from the enhancing tumor, the peritumoral region, and normal brain were obtained from the two-dimensional spectroscopic MR acquisition. The Student t test was used to determine if there was a statistically significant difference in relative cerebral blood volume and metabolic ratios between high-grade gliomas and metastases.
The measured relative cerebral blood volumes in the peritumoral region in high-grade gliomas and metastases were 1.31 +/- 0.97 (mean +/- SD) and 0.39 +/- 0.19, respectively. The difference was statistically significant (P <.001). Spectroscopic imaging demonstrated elevated choline levels (choline-to-creatine ratio was 2.28 +/- 1.24) in the peritumoral region of gliomas but not in metastases (choline-to-creatine ratio was 0.76 +/- 0.23). The difference was statistically significant (P =.001).
Although conventional MR imaging characteristics of solitary metastases and primary high-grade gliomas may sometimes be similar, perfusion-weighted and spectroscopic MR imaging enable distinction between the two.
In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.