Dynamic contrast-enhanced T2*-weighted MR imaging: A peritumoral brain oedema study

Alterations of cerebral microcirculation in peritumoral edema: feasibility of in vivo sidestream dark-field imaging in intracranial meningiomas

Background

Intracranial meningiomas display a variable amount of peritumoral brain edema (PTBE), which can significantly impact perioperative morbidity. The role of microcirculatory disturbances in the pathogenesis of PTBE is still debated. The aim of this study was to microscopically demonstrate and intraoperatively quantify, for the first time, the alterations to microcirculation in PTBE using sidestream dark-field (SDF) imaging.

Methods

Adult patients with WHO grade I meningiomas were recruited over a 9-month period and divided into 2 groups depending on the absence (NE group) or the presence (E group) of PTBE. In vivo intraoperative microcirculation imaging was performed in the peritumoral area before and after microsurgical resection.

Results

Six patients were included in the NE group and 6 in the E group. At the baseline in the NE group, there was a minor decrease in microcirculatory parameters compared to normal reference values, which was probably due to the mass effect. In contrast, microcirculatory parameters in the E group were significantly altered, affecting both vessel density and blood flow values, with a drop of approximately 50% of normal values. Surgical resection resulted in a quasi-normalization of microcirculation parameters in the NE group, whereas in the E group, even if all parameters statistically significantly improved, post-resection values remained considerably inferior to those of the normal reference pattern.

Conclusion

Our study confirmed significant alterations of microcirculatory parameters in PTBE in meningiomas. Further in vivo SDF imaging studies may explore the possible correlation between the severity of these microcirculatory alterations and the postoperative neurological outcome.

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.

Application of Dendrimer-based Nanoparticles in Glioma Imaging

Dendritic polymers or dendrimers present an alternate template for the development of nanoparticulate-based drug delivery and imaging systems. The smaller size (~7-12 nm) of dendrimers have the advantage over the other particles, because its smaller size can possibly improve tumor penetration and the inclusion of tumor specific drug release mechanisms. A Paramagnetic Chemical Exchange Saturation Transfer (PARACEST) MRI contrast agent, Eu-DOTA-Gly4 or a clinical relevant Gd-DOTA was conjugated on the surface of a G5 PAMAM dendrimer. To create a dual mode MRI-optical imaging nanoparticle, Dylight680 was also incorporated on the amines surface of a G5 dendrimer. The particle was detected with in vivo MRI in preclinical glioma animal model. Furthermore, noninvasive imaging results were validated with in vivo and ex-vivo optical imaging.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas

PURPOSE

To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions.

MATERIALS AND METHODS

In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI.

RESULTS

Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain.

CONCLUSION

Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions.

Assessing and monitoring intratumor heterogeneity in glioblastoma: how far has multimodal imaging come?

Glioblastoma demonstrates imaging features of intratumor heterogeneity that result from underlying heterogeneous biological properties. This stems from variations in cellular behavior that result from genetic mutations that either drive, or are driven by, heterogeneous microenvironment conditions. Among all imaging methods available, only T1-weighted contrast-enhancing and T2-weighted fluid-attenuated inversion recovery are used in standard clinical glioblastoma assessment and monitoring. Advanced imaging modalities are still considered emerging techniques as appropriate end points and robust methodologies are missing from clinical trials. Discovering how these images specifically relate to the underlying tumor biology may aid in improving quality of clinical trials and understanding the factors involved in regional responses to treatment, including variable drug uptake and effect of radiotherapy. Upon validation and standardization of emerging MR techniques, providing information based on the underlying tumor biology, these images may allow for clinical decision-making that is tailored to an individual's response to treatment.

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.

Classification methods for the differentiation of atypical meningiomas using diffusion and perfusion techniques at 3-T MRI

The purpose was to investigate the contribution of machine learning algorithms using diffusion and perfusion techniques in the differentiation of atypical meningiomas from glioblastomas and metastases. Apparent diffusion coefficient, fractional anisotropy, and relative cerebral blood volume were measured in different tumor regions. Naive Bayes, k-Nearest Neighbor, and Support Vector Machine classifiers were used in the classification procedure. The application of classification methods adds incremental differential diagnostic value. Differentiation is mainly achieved using diffusion metrics, while perfusion measurements may provide significant information for the peritumoral regions.

Imaging biomarkers of brain tumour margin and tumour invasion

Invasion of tumour cells into the normal brain is one of the major reasons of treatment failure for gliomas. Although there is a good understanding of the molecular and cellular processes that occur during this invasion, it is not possible to detect the extent of the tumour with conventional imaging. However, there is an understanding that the degree of invasion differs with individual tumours, and yet they are all treated the same. Newer imaging techniques that probe the pathological changes within tumours may be suitable biomarkers for invasion. Imaging methods are now available that can detect subtle changes in white matter organisation (diffusion tensor imaging), tumour metabolism and cellular proliferation (using MR spectroscopy and positron emission tomography) occurring in regions of tumour that cannot be detected by conventional imaging. The role of such biomarkers of invasion should allow better delineation of tumour margins, which should improve treatment planning (especially surgery and radiotherapy) and provide information on the invasiveness of an individual tumour to help select the most appropriate therapy and help stratify patients for clinical trials.

Mean intensity curve on dynamic contrast-enhanced susceptibility-weighted perfusion MR imaging--review of a new parameter to differentiate intracranial tumors

Dynamic susceptibility contrast (DSC) perfusion imaging has been in clinical use for various indications, including characterization and grading of intracranial neoplasms. However, several technical factors can lead to pitfalls in image interpretation. This review discusses the extraction of T1 and T2* information from mean curve analysis of DSC perfusion imaging of various brain tumors, which provides further insights into tumor biology and, thus, may be useful in the differential diagnosis of such tumors. Indeed, by looking at the mean time-signal intensity curve from the tumor bed in addition to the rCBV maps, it is possible to obtain further inferences of capillary density and lesion leakiness. When dynamic contrast enhanced (DCE) T1 perfusion is not available, DSC perfusion with mean curve analysis appears to be a valid alternative for characterizing various brain neoplasms in a routine clinical setting.

Intracranial Masses with Perilesional Edema: Differential Diagnosis with Perfusion CT

Perilesional edema (PE) is commonly observed in association with an intracranial mass. PE is thought to be determined by vasogenic effects in the cerebral parenchyma surrounding the mass due to the loss or absence of the blood-brain barrier (BBB) inside the lesion. Alterations in capillary permeability induce extrusion of fluids into the extravascular space around the mass. On Computed Tomography (CT) PE corresponds to an area of low density for the increased water content, outside the margins of the lesion. It is difficult to differentiate PE from areas of parenchymal compressive ischemia and sometimes the two events could be associated. A solitary mass with PE is more commonly discovered on a non-enhanced computed tomography (NECT) study performed for the onset of stable or rapidly progressive neurological symptoms. In such cases, a supplementary CT scan with contrast (CECT) is generally indicated to complete the baseline imaging before MRI. Contrast enhancement is generally present in a mass with PE and it is not specific for differential diagnosis. Perfusion computed tomography (PCT) requires a few minutes in addition to the time needed for CECT. PCT may give information on regional microvascular density, permeability and blood flow, thus it may play a role when tumoral neo-angiogenesis or non-neoplastic altered haemodynamics are suspected. We therefore investigated the utility of PCT in the differential diagnosis of the intracranial solitary masses with PE.

Correlation of MR relative cerebral blood volume measurements with cellular density and proliferation in high-grade gliomas: an image-guided biopsy study

BACKGROUND AND PURPOSE

As newer MR imaging techniques are used to assist with tumor grading, biopsy planning, and therapeutic response assessment, there is a need to relate the imaging characteristics to underlying pathologic processes. The aim of this study was to see how rCBV, a known marker of tumor vascularity, relates to cellular packing attenuation and cellular proliferation.

MATERIALS AND METHODS

Nine patients with histologically proved high-grade gliomas and 1 with a supratentorial PNET requiring an image-guided biopsy were recruited. Patients underwent a DSC study. The rCBV at the intended biopsy sites was determined by using a histogram measure to derive the mean, maximum, and 75th centile and 90th centile values. This measure was correlated with histologic markers of the MIB-1 labeling index (as a marker of glioma cell proliferation) and the total number of neoplastic cells in a high-power field (cellular packing attenuation).

RESULTS

There was a good correlation between rCBV and MIB-1 by using all the measures of rCBV. The mean rCBV provided the best results (r = 0.66, P < .001). The only correlation with cellular packing attenuation was with the 90% centile (rCBV(90%), r = 0.36, P = .03). The increase in rCBV could be seen over 1 cm from the edge of enhancement in 4/10 cases, and at 2 cm in 1/10.

CONCLUSIONS

rCBV correlated with cellular proliferation in high-grade gliomas but not with cellular packing attenuation. The increase in rCBV extended beyond the contrast-enhancing region in 50% of our patients.

Cerebral peritumoral oedema study: does a single dynamic MR sequence assessing perfusion and permeability can help to differentiate glioblastoma from metastasis?

Our purpose was to differentiate glioblastoma from metastasis using a single dynamic MR sequence to assess perfusion and permeability parameters. 24 patients with glioblastoma or cerebral metastasis with peritumoral oedema were recruited and explored with a 3T MR unit. Post processing used DPTools software. Regions of interest were drawn around contrast enhancement to assess relative cerebral blood volume and permeability parameters. Around the contrast enhancement Glioblastoma present high rCBV with modification of the permeability, metastasis present slight modified rCBV without modification of permeability. In conclusion, peritumoral T2 hypersignal exploration associating morphological MR and functional MR parameters can help to differentiate cerebral metastasis from glioblastoma.

Targeted imaging and therapy of brain cancer using theranostic nanoparticles

The past decade has seen momentous development in brain cancer research in terms of novel imaging-assisted surgeries, molecularly targeted drug-based treatment regimens or adjuvant therapies and in our understanding of molecular footprints of initiation and progression of malignancy. However, mortality due to brain cancer has essentially remained unchanged in the last three decades. Thus, paradigm-changing diagnostic and therapeutic reagents are urgently needed. Nanotheranostic platforms are powerful tools for imaging and treatment of cancer. Multifunctionality of these nanovehicles offers a number of advantages over conventional agents. These include targeting to a diseased site thereby minimizing systemic toxicity, the ability to solubilize hydrophobic or labile drugs leading to improved pharmacokinetics and their potential to image, treat and predict therapeutic response. In this article, we will discuss the application of newer theranostic nanoparticles in targeted brain cancer imaging and treatment.

Switching on the lights for real-time multimodality tumor neuroimaging: The integrated positron-emission tomography/MR imaging system

A recent report of the feasibility of simultaneous PET/MR imaging of the healthy human brain has sparked excitement in the field of neuroimaging because of its potential influence and utility in clinical neuroscience research. The aim of this communication is to discuss the benefits and current drawbacks of the hybrid imaging system and to highlight some perspectives of the new technique for brain neoplasms.