Dynamic contrast enhanced high field magnetic resonance imaging for canine primary intracranial neoplasia

Introduction

Distinguishing meningiomas from other intracranial neoplasms is clinically relevant as the prognostic and therapeutic implications differ greatly and influence clinical decision making. Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging technique that assists with characterisation of physiologic alterations such as blood flow and tissue vascular permeability. Quantitative pharmacokinetic analysis utilising DCE-MRI has not been studied in canine neuro-oncology.

Methods

A retrospective study was performed in canine patients that underwent DCE-MRI with an imaging diagnosis of an intracranial meningioma and surgery for histopathological diagnosis. Kinetic parameters Ktrans and cerebral blood flow were measured and compared to assess whether differences could be identified between meningiomas and other intracranial neoplasms.

Results

Six dogs with meningiomas and 3 dogs with other intracranial neoplasms were included for statistical analysis. Cerebral blood flow values were found to be statistically higher within meningiomas compared to other intracranial neoplasms. Ktrans values were higher within meningiomas than in other types of intracranial tumours, however this difference did not reach statistical significance.

Discussion

Based on the results of this study cerebral blood flow measurement can be utilised to differentiate canine intracranial meningiomas from other similar appearing intracranial tumours.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

Bench-to-bedside imaging in brain metastases: a road to precision oncology

Radiology has seen tremendous evolution in the last few decades. At the same time, oncology has made great strides in diagnosing and treating cancer. Distant metastases of neoplasms are being encountered more often in light of longer patient survival due to better therapeutic strategies and diagnostic methods. Brain metastasis (BM) is a dismal manifestation of systemic cancer. In the present scenario, magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are playing a big role in providing molecular information about cancer. Lately, molecular imaging has emerged as a stirring arena of dynamic imaging techniques that have enabled clinicians and scientists to noninvasively visualize and understand biological processes at the cellular and molecular levels. This knowledge has impacted etiopathogenesis, detection, personalized treatment, drug development, and our understanding of carcinogenesis. This article offers insight into the molecular biology underlying brain metastasis, its pathogenesis, imaging protocols, and algorithms. It also discusses disease-specific molecular imaging features, focusing on common tumors that spread to the brain, such as lung, breast, colorectal cancer, melanoma, and renal cell carcinoma. Additionally, it covers various targeted treatment options, criteria for assessing treatment response, and the role of artificial intelligence in diagnosing, managing, and predicting prognosis for patients with brain metastases.

A rare case of solitary, isolated dural metastasis from hepatocellular carcinoma mimicking a meningioma

Background

Distinguishing an isolated metastatic dural tumor from a meningioma on imaging is challenging and may lead to a delay in treatment. Here, we present the first known case of isolated, solitary dural metastasis from hepatocellular carcinoma (HCC) mimicking a meningioma.

Case Description

A 64-year-old male with a history of liver cirrhosis presented with a 5.8 cm enhancing left parafalcine hemorrhagic dural-based mass extending across the midline. Cerebral angiography revealed a distal left anterior pseudoaneurysm, and tumor contrast blush with feeders from the left ophthalmic and right middle meningeal artery. The pseudoaneurysm was successfully embolized to stop the bleeding, followed by an uneventful bi-coronal frontal craniotomy for falcine tumor resection to relieve brain compression. Histopathological analysis of the dural-based tumor showed poorly differentiated carcinoma with positive albumin in situ hybridization and cytokeratin tumor markers, consistent with dural metastases from HCC.

Conclusion

When encountering a solitary, highly vascular mass bearing resemblance to a meningioma, it may be prudent to consider the possibility of a dural-based metastatic carcinoma.

Quantitative analysis of magnetic resonance images for characterization of blood-brain barrier dysfunction in dogs with brain tumors

BACKGROUND

Blood-brain barrier (BBB) permeability can be assessed quantitatively using advanced imaging analysis.

HYPOTHESIS/OBJECTIVES

Quantification and characterization of blood-brain barrier dysfunction (BBBD) patterns in dogs with brain tumors can provide useful information about tumor biology and assist in distinguishing between gliomas and meningiomas.

ANIMALS

Seventy-eight hospitalized dogs with brain tumors and 12 control dogs without brain tumors.

METHODS

In a 2-arm study, images from a prospective dynamic contrast-enhanced (DCE; n = 15) and a retrospective archived magnetic resonance imaging study (n = 63) were analyzed by DCE and subtraction enhancement analysis (SEA) to quantify BBB permeability in affected dogs relative to control dogs (n = 6 in each arm). For the SEA method, 2 ranges of postcontrast intensity differences, that is, high (HR) and low (LR), were evaluated as possible representations of 2 classes of BBB leakage. BBB score was calculated for each dog and was associated with clinical characteristics and tumor location and class. Permeability maps were generated, using the slope values (DCE) or intensity difference (SEA) of each voxel, and analyzed.

RESULTS

Distinctive patterns and distributions of BBBD were identified for intra- and extra-axial tumors. At a cutoff of 0.1, LR/HR BBB score ratio yielded a sensitivity of 80% and specificity of 100% in differentiating gliomas from meningiomas.

CONCLUSIONS AND CLINICAL IMPORTANCE

Blood-brain barrier dysfunction quantification using advanced imaging analyses has the potential to be used for assessment of brain tumor characteristics and behavior and, particularly, to help differentiating gliomas from meningiomas.

Neuroradiologic Findings and Clinical Features of Meningiomas With Spontaneous Hemorrhagic Onset: A Single-center 10-year Experience

OBJECTIVE

This study aimed to elucidate the clinicoradiologic features of spontaneous hemorrhagic meningiomas (HMs) and examine risk factors associated with meningioma hemorrhage.

METHODS

We retrospectively reviewed 651 consecutive meningioma patients who underwent surgical resection in our hospital between January 2011 and January 2021. After exclusions, 169 patients were included for analysis. Patients were grouped according to presence of hemorrhage in the meningioma: the HM group (n = 19) and non-HM group (n = 150). Clinicoradiologic patient data were examined and compared using univariate and multivariate analysis.

RESULTS

HMs accounted for 2.9% of the entire series of meningiomas. HMs were mainly located at the convexity (63.2%). Mean diameter of HMs was 4.8 cm. On computed tomography, most HMs appeared as mixed isodensity and hyperdensity (84.2%). On magnetic resonance imaging, most appeared as mixed isointensity and hyperintensity on T1-weighted imaging and mixed hypointesity and hyperintensity on T2-weighted imaging (52.6%). Seventeen tumors exhibited heterogeneous enhancement, a dural tail, and peritumoral brain edema. Thirteen showed intratumoral cystic change. The misdiagnosis rate was significantly higher in HMs than non-HMs (31.6% vs. 7.3%; P = 0.005). Intratumoral cystic change was the only independent predictor of meningioma hemorrhage in multivariate analysis (odds ratio 4.116; 95% confidence interval 1.138-14.894; P = 0.031).

CONCLUSIONS

Mixed isodensity/intensity and hyperdensity/intensity on computed tomography/magnetic resonance imaging in conjunction with heterogenous enhancement, a dural tail, and varying degrees of peritumoral brain edema suggest a high possibility of HM. Presence of intratumoral cystic change was an independent risk factor associated with meningioma hemorrhage.

PET/MR Imaging of Somatostatin Receptor Expression and Tumor Vascularity in Meningioma: Implications for Pathophysiology and Tumor Outcomes

Background and Purpose

Meningiomas, the most common primary intracranial tumor, are vascular neoplasms that express somatostatin receptor-2 (SSTR2). The purpose of this investigation was to evaluate if a relationship exists between tumor vascularity and SSTR2 expression, which may play a role in meningioma prognostication and clinical management.

Materials and Methods

Gallium-68-DOTATATE PET/MRI with dynamic contrast-enhanced (DCE) perfusion was prospectively performed. Clinical and demographic patient characteristics were recorded. Tumor volumes were segmented and superimposed onto parametric DCE maps including flux rate constant (Kep), transfer constant (Ktrans), extravascular volume fraction (Ve), and plasma volume fraction (Vp). Meningioma PET standardized uptake value (SUV) and SUV ratio to superior sagittal sinus (SUVR_SSS) were recorded. Pearson correlation analyses were performed. In a random subset, analysis was repeated by a second investigator, and intraclass correlation coefficients (ICCs) were determined.

Results

Thirty-six patients with 60 meningiomas (20 WHO-1, 27 WHO-2, and 13 WHO-3) were included. Mean Kep demonstrated a strong significant positive correlation with SUV (r = 0.84, p < 0.0001) and SUVR_SSS (r = 0.81, p < 0.0001). When stratifying by WHO grade, this correlation persisted in WHO-2 (r = 0.91, p < 0.0001) and WHO-3 (r = 0.92, p = 0.0029) but not WHO-1 (r = 0.26, p = 0.4, SUVR_SSS). ICC was excellent (0.97–0.99).

Conclusion

DOTATATE PET/MRI demonstrated a strong significant correlation between tumor vascularity and SSTR2 expression in WHO-2 and WHO-3, but not WHO-1 meningiomas, suggesting biological differences in the relationship between tumor vascularity and SSTR2 expression in higher-grade meningiomas, the predictive value of which will be tested in future work.

Misdiagnosis and Delay of Diagnosis in Hemorrhagic Meningioma: A Case Series and Review of the Literature

OBJECTIVE

To evaluate the clinicoradiologic characteristics of hemorrhagic meningiomas (HMs) that are missed or misdiagnosed on radiologic imaging studies.

METHODS

Clinical and radiologic data from 6 patients with HM who were initially misdiagnosed were collected and recorded respectively. In addition, we performed a literature review for misdiagnosed HM and summarized the results.

RESULTS

Five of the 6 patients with misdiagnosed HM were female, and 1 was male. Both computed tomography (CT) and magnetic resonance imaging were performed in 4 patients, and CT alone was performed in 2. On CT, the HM was heterogeneously hyperdense in 5 patients and isodense in 1 patient. In all 4 patients who underwent magnetic resonance imaging, the HM was mixed iso- and hypointense on T1-weighted imaging and heterogeneously hyperintense on T2-weighted imaging. Marked heterogeneous contrast enhancement was observed in 2 patients, strong rim enhancement in 1, and peripheral enhancement in 1. The dural tail sign was seen in only 1 patient. The initial radiologic misdiagnoses were subdural hematoma (n = 1), malignant glioma (n = 1), ruptured arterial aneurysm (n = 1), metastasis (n = 2), and uncertain (n = 1). In the literature review, 22 cases of HM diagnostic error were collected. The main misdiagnoses were subdural hematoma (27.3%), traumatic hematoma (13.6%), vascular anomaly (13.6%), malignant glioma (4.5%), and metastasis (4.5%).

CONCLUSIONS

Our study showed that in patients with HM with inadequate imaging evaluation, a small tumor associated with massive hematoma and atypical imaging features was more likely to be misdiagnosed.

Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma

BACKGROUND AND PURPOSE

The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE).

MATERIALS AND METHODS

We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis.

RESULTS

Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity.

CONCLUSIONS

Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.

Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery

OBJECTIVE

There is a need for advanced imaging biomarkers to improve radiation treatment planning and response assessment. T1-weighted dynamic contrast-enhanced perfusion MRI (DCE MRI) allows quantitative assessment of tissue perfusion and blood-brain barrier dysfunction and has entered clinical practice in the management of primary and secondary brain neoplasms. The authors sought to retrospectively investigate DCE MRI parameters in meningiomas treated with resection and adjuvant radiation therapy using volumetric segmentation.

METHODS

A retrospective review of more than 300 patients with meningiomas resected between January 2015 and December 2018 identified 14 eligible patients with 18 meningiomas who underwent resection and adjuvant radiotherapy. Patients were excluded if they did not undergo adjuvant radiation therapy or DCE MRI. Demographic and clinical characteristics were obtained and compared to DCE perfusion metrics, including mean plasma volume (vp), extracellular volume (ve), volume transfer constant (Ktrans), rate constant (kep), and wash-in rate of contrast into the tissue, which were derived from volumetric analysis of the enhancing volumes of interest.

RESULTS

The mean patient age was 64 years (range 49–86 years), and 50% of patients (7/14) were female. The average tumor volume was 8.07 cm3 (range 0.21–27.89 cm3). The median Ki-67 in the cohort was 15%. When stratified by median Ki-67, patients with Ki-67 greater than 15% had lower median vp (0.02 vs 0.10, p = 0.002), and lower median wash-in rate (1.27 vs 4.08 sec−1, p = 0.04) than patients with Ki-67 of 15% or below. Logistic regression analysis demonstrated a statistically significant, moderate positive correlation between ve and time to progression (r = 0.49, p < 0.05). Furthermore, there was a moderate positive correlation between Ktrans and time to progression, which approached, but did not reach, statistical significance (r = 0.48, p = 0.05).

CONCLUSIONS

This study demonstrates a potential role for DCE MRI in the preoperative characterization and stratification of meningiomas, laying the foundation for future prospective studies incorporating DCE as a biomarker in meningioma diagnosis and treatment planning.

Dural masses: meningiomas and their mimics

Meningiomas are the most common dural tumour. They are regularly being seen as an incidental finding on brain imaging and treated conservatively. However, there are many other dural masses which mimic their appearances, including primary neoplastic processes, metastases, granulomatous diseases and infection. While some of these are rare, others such as metastases and tuberculosis arise relatively frequently in practice. Although not pathognomonic, key features which increase the probability of a lesion being a meningioma include intralesional calcifications, skull hyperostosis, local dural enhancement and increased perfusion. It is important to have an awareness of these entities as well as their main imaging findings, as they have a wide range of prognoses and differing management strategies. This review outlines several of the most important mimics along with their imaging findings on both standard and advanced techniques with key features which may be used to help differentiate them from meningiomas.

Intracranial dural, calvarial, and skull base metastases

Metastatic disease to the intracranial dura, the calvarium, and the skull base is relatively uncommon but presents unique diagnostic and management challenges in the patient with cancer. Modern imaging techniques have facilitated the detection of intracranial tumor deposits, leading to increased incidence. While dural and calvarial metastases often present with nonspecific symptoms, skull base metastases present with distinct clinical syndromes dependent on the local neurovascular structures affected. Intracranial dural metastases can often be confused with meningioma and pose a diagnostic challenge, as well as significant neurologic morbidity, especially in the setting of hemorrhage. Surgical intervention may be helpful in selected patients for symptomatic relief as well as survival benefit. Management paradigms need to take into account the relative risks, benefits, and likely outcomes for each possible modality of treatment. Surgical excision is useful in many patients and in combination with radiation therapy can provide significant palliation. While medical therapy is rarely an initial therapy in these entities, it may be of added benefit dependent on the underlying tumor histology and prior treatment history. Occasionally treatment with curative intent is justified.

Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas

OBJECTIVE

Microcystic meningioma (MM) is a meningioma variant with a multicystic appearance that may mimic intrinsic primary brain tumors and other nonmeningiomatous tumor types. Dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI techniques provide imaging parameters that can differentiate these tumors according to hemodynamic and permeability characteristics with the potential to aid in preoperative identification of tumor type.

METHODS

The medical data of 18 patients with a histopathological diagnosis of MM were identified through a retrospective review of procedures performed between 2008 and 2012; DSC imaging data were available for 12 patients and DCE imaging data for 6. A subcohort of 12 patients with Grade I meningiomas (i.e., of meningoepithelial subtype) and 54 patients with Grade IV primary gliomas (i.e., astrocytomas) was also included, and all preoperative imaging sequences were analyzed. Clinical variables including patient sex, age, and surgical blood loss were also included in the analysis. Images were acquired at both 1.5 and 3.0 T. The DSC images were acquired at a temporal resolution of either 1500 msec (3.0 T) or 2000 msec (1.5 T). In all cases, parameters including normalized cerebral blood volume (CBV) and transfer coefficient (kTrans) were calculated with region-of-interest analysis of enhancing tumor volume. The normalized CBV and kTrans data from the patient groups were analyzed with 1-way ANOVA, and post hoc statistical comparisons among groups were conducted with the Bonferroni adjustment.

RESULTS

Preoperative DSC imaging indicated mean (± SD) normalized CBVs of 5.7 ± 2.2 ml for WHO Grade I meningiomas of the meningoepithelial subtype (n = 12), 4.8 ± 1.8 ml for Grade IV astrocytomas (n = 54), and 12.3 ± 3.8 ml for Grade I meningiomas of the MM subtype (n = 12). The normalized CBV measured within the enhancing portion of the tumor was significantly higher in the MM subtype than in typical meningiomas and Grade IV astrocytomas (p < 0.001 for both). Preoperative DCE imaging indicated mean kTrans values of 0.49 ± 0.20 min−1 in Grade I meningiomas of the meningoepithelial subtype (n = 12), 0.27 ± 0.12 min−1 for Grade IV astrocytomas (n = 54), and 1.35 ± 0.74 min−1 for Grade I meningiomas of the MM subtype (n = 6). The kTrans was significantly higher in the MM variants than in the corresponding nonmicrocystic Grade 1 meningiomas and Grade IV astrocytomas (p < 0.001 for both). Intraoperative blood loss tended to increase with increased normalized CBV (R = 0.45, p = 0.085).

CONCLUSIONS

An enhancing cystic lesion with a normalized CBV greater than 10.3 ml or a kTrans greater than 0.88 min−1 should prompt radiologists and surgeons to consider the diagnosis of MM rather than traditional Grade I meningioma or high-grade glioma in planning surgical care. Higher normalized CBVs tend to be associated with increased intraoperative blood loss.

From the radiologic pathology archives: mass lesions of the dura: beyond meningioma-radiologic-pathologic correlation

Meningioma is the most common mass involving the dura, making it number one in the differential diagnosis for any dural-based mass; however, a variety of other neoplastic and nonneoplastic lesions also involve the dura. Knowledge of the dural anatomy can provide clues to the various processes that may involve this location. The neoplastic processes include both benign and malignant lesions such as hemangiopericytoma, lymphoma, solitary fibrous tumor, melanocytic lesions, Epstein-Barr virus-associated smooth muscle tumors, Rosai-Dorfman disease, and metastatic lesions. The nonneoplastic processes include infectious and inflammatory entities such as tuberculosis and sarcoid, which may mimic mass lesions. In some cases, neoplasms such as gliosarcoma may arise peripherally from the brain parenchyma, appearing dural-based and even inciting a dural tail. Many of these share similar computed tomographic, magnetic resonance imaging, and angiographic characteristics with meningiomas, such as a dural tail, increased vascularity, avid enhancement, and similar signal characteristics; however, knowledge of the patient's age, gender, and underlying conditions and certain imaging characteristics may provide valuable clues to recognizing these lesions. For example, in the population with human immunodeficiency virus infection, Epstein-Barr virus-associated smooth muscle tumors should be included in the differential diagnosis for dural-based lesions. The surgical course and prognosis for these lesions vary, and knowledge of the variety of lesions that involve the dura, their imaging appearances, and their clinical features assists in narrowing the radiologic differential diagnosis and optimizing patient treatment.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

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.