Heterogeneity in malignant gliomas: a magnetic resonance analysis of spatial distribution of metabolite changes and regional blood volume

Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study

OBJECTIVES

To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms.

METHODS

3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region.

RESULTS

Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05).

CONCLUSION

Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival.

KEY POINTS

• 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: correlation with spatially co-registered PET

BACKGROUND

Heterogeneity of gliomas challenges the neuronavigated biopsy and oncological therapy. Diffusion and perfusion magnetic resonance imaging (MRI) can reveal the cellular and hemodynamic heterogeneity of tumors. Integrated positron emission tomography (PET)/MRI is expected to be a non-invasive imaging approach to characterizing glioma.

PURPOSE

To evaluate the value of apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and spatially co-registered maximal standard uptake value (SUV_max) for tissue characterization and glioma grading.

MATERIAL AND METHODS

Thirty-seven consecutive patients with pathologically confirmed gliomas were retrospectively investigated. The relative minimum ADC (rADC_min), relative maximal ADC (rADC_max), relative maximal rCBV (rCBV_max), the relative minimum rCBV (rCBV_min), and the corresponding relative SUVmax (rSUV_max) were measured. The paired t-test was used to compare the quantitative parameters between different regions to clarify tumor heterogeneity. Imaging parameters between WHO grade IV and grade II/III gliomas were compared by t-test. The diagnostic efficiency of multiparametric PET/MRI was analyzed by receiver operating characteristic (ROC) curve.

RESULTS

The values of rSUV_max were significantly different between maximal diffusion/perfusion area and minimum diffusion/perfusion area (P < 0.001/P < 0.001) within tumor. The values of rADC_min (P < 0.001), rCBV_max (P = 0.002), and corresponding rSUV_max (P = 0.001/P < 0.001) could be used for grading gliomas. The areas under the ROC curves of rSUV_max defined by rADC_min and rCBV_max were 0.89 and 0.91, respectively.

CONCLUSION

Diffusion and perfusion MRI can detect glioma heterogeneity with excellent molecular imaging correlations. Regions with rCBV_max suggest tissues with the highest metabolism and malignancy for guiding glioma grading and tissue sampling.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors

PURPOSE

This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.

METHODS

Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests.

RESULTS

Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05).

CONCLUSION

Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.

Assessment of molecular markers demonstrates concordance between samples acquired via stereotactic biopsy and open craniotomy in both anaplastic astrocytomas and glioblastomas

The classification, treatment and prognosis of high-grade gliomas has been shown to correlate with the expression of molecular markers (e.g. MGMT promotor methylation and IDH1 mutations). Acquisition of tumor samples may be obtained via stereotactic biopsy or open craniotomy. Between the years 2009 and 2013, 22 patients initially diagnosed with HGGs via stereotactic biopsy, that ultimately underwent open craniotomy for resection of their tumor were prospectively included in an institutional glioma database. MGMT promotor analysis was performed using methylation-specific (MS)-PCR and IDH1R132H mutation analysis was performed using immunohistochemistry. Three patients (13.7%) exhibited IDH1R132H mutations in samples obtained via stereotactic biopsy. Tissue derived from stereotaxic biopsy was demonstrated to have MGMT promotor methylation in ten patients (45.5%), while a non-methylated MGMT promotor was demonstrated in ten patients (45.5%); inconclusive results were obtained for the remaining two patients (9%) within our cohort. The initial histologic grading, IDH1R132H mutation and MGMT promotor methylation results were confirmed using samples obtained during open craniotomy in all but one patient; here inconclusive MGMT promotor analysis was obtained in contrast to that which was obtained via stereotactic biopsy. Tumor samples acquired via stereotactic biopsy provide accurate information with regard to clinically relevant molecular markers that have been shown to impact patient care decisions. The profile of markers analyzed in our cohort was nearly concordant between those samples obtained via stereotactic biopsy or open craniotomy thereby suggesting that clinical decisions may be based on the molecular profile of the tumor samples obtained via stereotactic biopsy.

Advances in neuro-oncology imaging

Despite the fact that MRI has evolved to become the standard method for diagnosis and monitoring of patients with brain tumours, conventional MRI sequences have two key limitations: the inability to show the full extent of the tumour and the inability to differentiate neoplastic tissue from nonspecific, treatment-related changes after surgery, radiotherapy, chemotherapy or immunotherapy. In the past decade, PET involving the use of radiolabelled amino acids has developed into an important diagnostic tool to overcome some of the shortcomings of conventional MRI. The Response Assessment in Neuro-Oncology working group - an international effort to develop new standardized response criteria for clinical trials in brain tumours - has recommended the additional use of amino acid PET imaging for brain tumour management. Concurrently, a number of advanced MRI techniques such as magnetic resonance spectroscopic imaging and perfusion weighted imaging are under clinical evaluation to target the same diagnostic problems. This Review summarizes the clinical role of amino acid PET in relation to advanced MRI techniques for differential diagnosis of brain tumours; delineation of tumour extent for treatment planning and biopsy guidance; post-treatment differentiation between tumour progression or recurrence versus treatment-related changes; and monitoring response to therapy. An outlook for future developments in PET and MRI techniques is also presented.

Stereotactic biopsy of brainstem lesions: 21 years experiences of a single center

Stereotactic biopsies are procedures performed to obtain tumor tissue for diagnostic examinations. Cerebral lesions of unknown entities can safely be accessed and tissue can be examined, resulting in correct diagnosis and according treatment. Stereotactic procedures of lesions in highly eloquent regions such as the brainstem have been performed for more than two decades in our department. In this retrospective study we focus on results, approaches, modalities of anesthesia, and complications. We performed a retrospective analysis of our prospective database, including 26 patients who underwent stereotactic biopsy of the brainstem between April 1994 and June 2015. All of the patients underwent preoperative MRI. Riechert-Mundinger-frame was used before 2000, thereafter the Leksell stereotactic frame was used. After 2000 entry and target points were calculated by using BrainLab stereotactic system. We evaluated histopathological results as well as further treatment; additionally we compared complications of local versus general anesthesia and complications of a frontal versus a trans-cerebellar approach. Median age of all patients was 33 years, and median number of tissue samples taken was 12. In all patients a final histopathological diagnosis could be established. 5 patients underwent the procedure under local anesthesia, 21 patients in general anesthesia. In 19 patients a frontal approach was performed, while in 7 patients a trans-cerebellar approach was used. Complications occurred in five patients. Thereby no significant difference was found with regard to approach (frontal versus trans-cerebellar) or anesthesia (local versus general). Stereotactic biopsies even of lesions in the brainstem are a save way to obtain tumor tissue for final diagnosis, resulting in adequate treatment. Approach can be trans-cerebellar or frontal and procedure can be performed either under local or general anesthesia without significant differences concerning complication rate.

Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI

Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

BACKGROUND

We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas.

METHODS

In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients.

RESULTS

Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%.

CONCLUSIONS

Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

Reaction-diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction-diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor 'grown' for ten days as dictated by the reaction-diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model's accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error <8.8%, Dice >0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction-diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.

A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can quantitatively and qualitatively assess physiological characteristics of tissue. Quantitative DCE-MRI requires an estimate of the time rate of change of the concentration of the contrast agent in the blood plasma, the vascular input function (VIF). Measuring the VIF in small animals is notoriously difficult as it requires high temporal resolution images limiting the achievable number of slices, field-of-view, spatial resolution, and signal-to-noise. Alternatively, a population-averaged VIF could be used to mitigate the acquisition demands in studies aimed to investigate, for example, tumor vascular characteristics. Thus, the overall goal of this manuscript is to determine how the kinetic parameters estimated by a population based VIF differ from those estimated by an individual VIF. Eight rats bearing gliomas were imaged before, during, and after an injection of Gd-DTPA. K(trans), ve, and vp were extracted from signal-time curves of tumor tissue using both individual and population-averaged VIFs. Extended model voxel estimates of K(trans) and ve in all animals had concordance correlation coefficients (CCC) ranging from 0.69 to 0.98 and Pearson correlation coefficients (PCC) ranging from 0.70 to 0.99. Additionally, standard model estimates resulted in CCCs ranging from 0.81 to 0.99 and PCCs ranging from 0.98 to 1.00, supporting the use of a population based VIF if an individual VIF is not available.

Intratumoral modeling of gefitinib pharmacokinetics and pharmacodynamics in an orthotopic mouse model of glioblastoma

Like many solid tumors, glioblastomas are characterized by intratumoral biologic heterogeneity that may contribute to a variable distribution of drugs and their associated pharmacodynamic responses, such that the standard pharmacokinetic approaches based on analysis of whole-tumor homogenates may be inaccurate. To address this aspect of tumor pharmacology, we analyzed intratumoral pharmacokinetic/pharmacodynamic characteristics of the EGFR inhibitor gefitinib in mice with intracerebral tumors and developed corresponding mathematical models. Following a single oral dose of gefitinib (50 or 150 mg/kg), tumors were processed at selected times according to a novel brain tumor sectioning protocol that generated serial samples to measure gefitinib concentrations, phosphorylated extracellular signal-regulated kinase (pERK), and immunohistochemistry in 4 different regions of tumors. Notably, we observed up to 3-fold variations in intratumoral concentrations of gefitinib, but only up to half this variability in pERK levels. As we observed a similar degree of variation in the immunohistochemical index termed the microvessel pericyte index (MPI), a measure of permeability in the blood-brain barrier, we used MPI in a hybrid physiologically-based pharmacokinetic (PBPK) model to account for regional changes in drug distribution that were observed. Subsequently, the PBPK models were linked to a pharmacodynamic model that could account for the variability observed in pERK levels. Together, our tumor sectioning protocol enabled integration of the intratumoral pharmacokinetic/pharmacodynamic variability of gefitinib and immunohistochemical indices followed by the construction of a predictive PBPK/pharmacodynamic model. These types of models offer a mechanistic basis to understand tumor heterogeneity as it impacts the activity of anticancer drugs.

Analytical approach to characterize the intratumoral pharmacokinetics and pharmacodynamics of gefitinib in a glioblastoma model

Heterogeneity in brain tumors can result in variable drug distribution and possibly drug response; however, there are no readily accessible means to obtain regional pharmacokinetic (PK)/pharmacodynamic (PD) information in preclinical tumor models that typically rely on average drug concentration measurements. On the basis of a novel serial brain tumor sectioning protocol, sensitive and robust methods were developed to characterize the intratumoral PK [liquid chromatography with tandem mass spectrometry detection (LC/MS/MS)] and PD (phosphorylated extracellular-signal-regulated kinase, antibody-based detection) of gefitinib in small amounts of glioblastoma tumor samples obtained from mice bearing intracerebral tumors administered 150 mg/kg of gefitinib. LC/MS/MS method was accurate (±15%) and precise (coefficient of variation ≤15%). For PD analysis, two antibody-based assay systems [enzyme-linked immunosorbent assay and meso scale discovery (MSD)] were compared and the more sensitive method (MSD) was selected. Gefitinib concentrations showed up to 2.4 ± 0.7-fold intratumoral variability in PK and 1.5 ± 0.20-fold variability in PD. The methods are sufficiently accessible and could be applied to other anticancer drugs and tumor models to obtain greater resolution of intratumoral PKs and PDs.

Proton magnetic resonance spectroscopy predicts survival in children with diffuse intrinsic pontine glioma

Patients with diffuse intrinsic pontine glioma (DIPG) face a grim prognosis with limited treatment options. Many patients will enroll on investigational trials though the role of chemotherapy or immunotherapy is unclear. Radiographic changes on conventional MRI are used to evaluate tumor response and progression, but are not predictive of outcome in these patients. More sensitive measures of tumor biology are needed to improve patient management. We evaluated changes in magnetic resonance spectroscopy (MRS) biomarkers in patients with DIPG. Thirty-eight patients were enrolled prospectively on an IRB-approved protocol, which included standard MRI, single voxel spectroscopy (SVS) and multi-slice multi-voxel spectroscopy (MRSI). Scans were performed at multiple time points during each patient's clinical course, with a total of 142 scans. The prognostic values of Choline:N-acetylaspartate (Cho:NAA), Cho:Creatine (Cho:Cr) and the presence of lactate and lipids (+Lac/Lip) were evaluated. Cho:NAA and variance in Cho:NAA values among different voxels within a tumor were each predictive of shorter survival. This prospective study shows that MRS can be used to identify high-risk patients and monitor changes in tumor metabolism, which may reflect changes in tumor behavior.