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

Synthetic MRI derived relaxometry parameters: a new insight into characterization of ring enhancing lesions of brain

BACKGROUND AND PURPOSE

Synthetic MRI utilizes the quantitative relaxometry parameters to generate multiple contrast images through a single acquisition. We tried to explore the utility of synthetic MRI derived relaxometry parameters in evaluation of ring enhancing lesions of brain.

MATERIALS AND METHODS

This was a prospective study. 40 subjects with ring enhancing lesions in brain underwent pre and post contrast synthetic MRI using MDME sequence. Pre and post contrast R1, R2 and PD values were recorded from the core, wall and perilesional edema of lesions and sub group analysis was done among infective, primary neoplastic and secondary neoplastic (metastatic) lesion groups.

RESULTS

Pre and post contrast R1, R2 values from core were higher in the infective group compared to the others. Pre and post contrast R1, R2 values were lower in the wall where as it was significantly higher in the perilesional edema of primary neoplastic group. Post-pre the values increased significantly in the perilesional edema of primary neoplasms. R1 value of ≥ 0.689 and R2 value of ≥ 7.481 in the perilesional edema predicts a primary neoplasm over infection with 70.6% sensitivity and 85.7% specificity and over secondary neoplasm with 64.7% sensitivity and 100% specificity.

CONCLUSION

Synthetic MRI derived relaxometry parameters in ring enhancing lesions were found to be significantly different across sub groups and can be used to differentiate between primary neoplastic, secondary neoplastic and infective group with parameters from perilesional edema being the most useful.

1H-MRS parameters in non-enhancing peritumoral regions can predict the recurrence of glioblastoma

This study aimed to evaluate the predictive value of metabolic parameters in preoperative non-enhancing peritumoral regions (NEPTRs) for glioblastoma recurrence, using multivoxel hydrogen proton magnetic resonance spectroscopy (1H-MRS). Clinical and imaging data from patients with recurrent glioblastoma were analyzed. Through co-registration of preoperative and post-recurrence MRI, we identified future tumor recurrence regions (FTRRs) and future non-tumor recurrence regions (FNTRRs) within the NEPTRs. Metabolic parameters were recorded separately for each region. Cox regression analysis was applied to assess the association between metabolic parameters and glioblastoma recurrence. Compared to FNTRRs, FTRRs exhibited a higher Cho/Cr ratio, higher Cho/NAA ratio, and lower NAA/Cr ratio. Both Cho/NAA and Cho/Cr ratios were recognized as risk factors in univariate and multivariate analyses (P < 0.05). The Cox regression model indicated that Cho/NAA > 1.99 and Cho/Cr > 1.73 are independent risk factors for early glioblastoma recurrence. Based on these cut-off values, patients were stratified into low-risk and high-risk groups, with a statistically significant difference in recurrence rates between the two groups (P < 0.01). The Cho/NAA and Cho/Cr ratios in NEPTRs are independent predictors of future glioblastoma recurrence. Specifically, Cho/NAA > 1.99 and/or Cho/Cr > 1.73 in NEPTRs may indicate a higher risk of early postoperative recurrence at these regions.

Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis

BACKGROUND

Differentiating high-grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context.

PURPOSE

To differentiate high-grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2-FLAIR digital subtraction images and the peritumoral edema area.

STUDY TYPE

Retrospective.

POPULATION

The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses).

FIELD STRENGTH/SEQUENCE

Axial T2-weighted fast spin-echo sequence (T2WI) and T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), using 1.5-T and 3.0-T scanners.

ASSESSMENT

Radiomic features were extracted from digitally subtracted T2-FLAIR images in the area of peritumoral edema. The maximum relevance-minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP).

STATISTICAL TESTS

Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC).

RESULTS

The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922.

DATA CONCLUSION

The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2-FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Metabolic habitat imaging with hemodynamic heterogeneity predicts individual progression-free survival in high-grade glioma

AIM

We design a feasibility study to obtain a set of metabolic-hemodynamic habitats for tackling tumor spatial metabolic patterns with hemodynamic information.

MATERIALS AND METHODS

Preoperative data from 69 high-grade gliomas (HGG) patients with subsequent histologic confirmation of HGG were prospectively collected (January 2016 to March 2020) after concurrent chemoradiotherapy (CCRT). Four vascular habitats were automatically segmented by multiparametric magnetic resonance imaging (MRI). The metabolic information, either at enhancing or edema tumor regions, was obtained by two neuroradiologists. The relative habitat volumes were used for weight estimation procedures for computing the coefficients of a linear regression model using weighted least squares (WLS) for metabolite semiquantifications (i.e. the Cho/NAA ratio and the Cho/Cr ratio) at vascular habitats. Multivariate Cox proportional hazard regression analyses are used to obtain the odds ratio (OR) and develop a nomogram using weighted estimators corresponding to each covariate derived from Cox regression coefficients.

RESULTS

There was a strongly correlation between perfusion indexes and the Cho/Cr ratio (rCBV, r=0.71) or Cho/NAA ratio (rCBV, r=0.66) at high-angiogenic enhancing tumor habitats (HAT) habitat. Compared isocitrate dehydrogenase (IDH) mutation to their wild type, the IDH wild type had significantly decreased Cho/Cr ratio (IDH mutation: Cho/Cr ratio = 2.44 ± 0.33, IDH wildtype: Cho/Cr ratio = 2.66 ± 0.36, p=0.02) and Cho/NAA ratio (IDH mutation: Cho/Cr ratio = 4.59 ± 0.61, IDH wildtype: Cho/Cr ratio = 4.99 ± 0.66, p=0.022) at the HAT. The C-index for the median progression-free survival (PFS) prediction was 0.769 for the Cho/NAA nomogram and 0.747 for the Cho/Cr nomogram through 1000 bootstrapping validation.

CONCLUSIONS

Our findings suggest that spatial metabolism combined with hemodynamic heterogeneity is associated with individual PFS to HGG patients post-CCRT.

The STEMRI trial: Magnetic resonance spectroscopy imaging can define tumor areas enriched in glioblastoma stem-like cells

Despite maximally safe resection of the magnetic resonance imaging (MRI)-defined contrast-enhanced (CE) central tumor area and chemoradiotherapy, most patients with glioblastoma (GBM) relapse within a year in peritumoral FLAIR regions. Magnetic resonance spectroscopy imaging (MRSI) can discriminate metabolic tumor areas with higher recurrence potential as CNI+ regions (choline/N-acetyl-aspartate index >2) can predict relapse sites. As relapses are mainly imputed to glioblastoma stem-like cells (GSCs), CNI+ areas might be GSC enriched. In this prospective trial, 16 patients with GBM underwent MRSI/MRI before surgery/chemoradiotherapy to investigate GSC content in CNI-/+ biopsies from CE/FLAIR. Biopsy and derived-GSC characterization revealed a FLAIR/CNI+ sample enrichment in GSC and in gene signatures related to stemness, DNA repair, adhesion/migration, and mitochondrial bioenergetics. FLAIR/CNI+ samples generate GSC-enriched neurospheres faster than FLAIR/CNI-. Parameters assessing biopsy GSC content and time-to-neurosphere formation in FLAIR/CNI+ were associated with worse patient outcome. Preoperative MRI/MRSI would certainly allow better resection and targeting of FLAIR/CNI+ areas, as their GSC enrichment can predict worse outcomes.

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO₂), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO₂ to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.

Precision Neurosurgery: A Path Forward

Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.

Radiomics and radiogenomics in gliomas: a contemporary update

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma

INTRODUCTION

IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature.

PURPOSE

To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients.

MATERIALS AND METHODS

Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS ≥ 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform).

RESULTS

For LTSs, we found a significant association between a moderate value of rCBV_mean and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBV_mean and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs.

CONCLUSION

We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBV_mean at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.

Diagnosis and Management of Glioblastoma: A Comprehensive Perspective

Glioblastoma is the most common malignant brain tumor in adults. The current management relies on surgical resection and adjuvant radiotherapy and chemotherapy. Despite advances in our understanding of glioblastoma onset, we are still faced with an increased incidence, an altered quality of life and a poor prognosis, its relapse and a median overall survival of 15 months. For the past few years, the understanding of glioblastoma physiopathology has experienced an exponential acceleration and yielded significant insights and new treatments perspectives. In this review, through an original R-based literature analysis, we summarize the clinical presentation, current standards of care and outcomes in patients diagnosed with glioblastoma. We also present the recent advances and perspectives regarding pathophysiological bases as well as new therapeutic approaches such as cancer vaccination and personalized treatments.

Conventional and advanced imaging throughout the cycle of care of gliomas

Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.

Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype

BACKGROUND AND PURPOSE

Genetic classifications are crucial for understanding the heterogeneity of glioblastoma. Recently, perfusion MRI techniques have demonstrated associations molecular alterations. In this work, we investigated whether perfusion markers within infiltrated peripheral edema were associated with proneural, mesenchymal, classical and neural subtypes.

MATERIALS AND METHODS

ONCOhabitats open web services were used to obtain the cerebral blood volume at the infiltrated peripheral edema for MRI studies of 50 glioblastoma patients from The Cancer Imaging Archive: TCGA-GBM. ANOVA and Kruskal-Wallis tests were carried out in order to assess the association between vascular features and the Verhaak subtypes. For assessing specific differences, Mann-Whitney U-test was conducted. Finally, the association of overall survival with molecular and vascular features was assessed using univariate and multivariate Cox models.

RESULTS

ANOVA and Kruskal-Wallis tests for the maximum cerebral blood volume at the infiltrated peripheral edema between the four subclasses yielded false discovery rate corrected p-values of <0.001 and 0.02, respectively. This vascular feature was significantly higher (p = 0.0043) in proneural patients compared to the rest of the subtypes while conducting Mann-Whitney U-test. The multivariate Cox model pointed to redundant information provided by vascular features at the peripheral edema and proneural subtype when analyzing overall survival.

CONCLUSIONS

Higher relative cerebral blood volume at infiltrated peripheral edema is associated with proneural glioblastoma subtype suggesting underlying vascular behavior related to molecular composition in that area.

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

PURPOSE

To systematically review evidence regarding the association of multiparametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas.

MATERIALS AND METHODS

Scopus database was searched for original journal papers from January 1st, 2007 to February 20th, 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria.

RESULTS

It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and highrisk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, α=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature.

CONCLUSION

Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.

Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

Molecular and Cellular Complexity of Glioma. Focus on Tumour Microenvironment and the Use of Molecular and Imaging Biomarkers to Overcome Treatment Resistance

This review highlights the importance and the complexity of tumour biology and microenvironment in the progression and therapy resistance of glioma. Specific gene mutations, the possible functions of several non-coding microRNAs and the intra-tumour and inter-tumour heterogeneity of cell types contribute to limit the efficacy of the actual therapeutic options. In this scenario, identification of molecular biomarkers of response and the use of multimodal in vivo imaging and in particular the Positron Emission Tomography (PET) based molecular approach, can help identifying glioma features and the modifications occurring during therapy at a regional level. Indeed, a better understanding of tumor heterogeneity and the development of diagnostic procedures can favor the identification of a cluster of patients for personalized medicine in order to improve the survival and their quality of life.

Multi-scale segmentation in GBM treatment using diffusion tensor imaging

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.

Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging

OBJECTIVE

The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma.

METHODS

Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06-1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16-2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001).

CONCLUSIONS

DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.

Improving Delineation of True Tumor Volume With Multimodal MRI in a Rat Model of Brain Metastasis

PURPOSE

Brain metastases are almost universally lethal with short median survival times. Despite this, they are often potentially curable, with therapy failing only because of local relapse. One key reason relapse occurs is because treatment planning did not delineate metastasis margins sufficiently or accurately, allowing residual tumor to regrow. The aim of this study was to determine the extent to which multimodal magnetic resonance imaging (MRI), with a simple and automated analysis pipeline, could improve upon current clinical practice of single-modality, independent-observer tumor delineation.

METHODS AND MATERIALS

We used a single rat model of brain metastasis (ENU1564 breast carcinoma cells in BD-IX rats), with and without radiation therapy. Multimodal MRI data were acquired using sequences either in current clinical use or in clinical trial and included postgadolinium T1-weighted images and maps of blood flow, blood volume, T1 and T2 relaxation times, and apparent diffusion coefficient.

RESULTS

In all cases, independent observers underestimated the true size of metastases from single-modality gadolinium-enhanced MRI (85 ± 36 μL vs 131 ± 40 μL histologic measurement), although multimodal MRI more accurately delineated tumor volume (132 ± 41 μL). Multimodal MRI offered increased sensitivity compared with independent observer for detecting metastasis (0.82 vs 0.61, respectively), with only a slight decrease in specificity (0.86 vs 0.98). Blood flow maps conferred the greatest improvements in margin detection for late-stage metastases after radiation therapy. Gadolinium-enhanced T1-weighted images conferred the greatest increase in accuracy of detection for smaller metastases.

CONCLUSIONS

These findings suggest that multimodal MRI of brain metastases could significantly improve the visualization of brain metastasis margins, beyond current clinical practice, with the potential to decrease relapse rates and increase patient survival. This finding now needs validation in additional tumor models or clinical cohorts.

Clinical Significance of Fractional Anisotropy Measured in Peritumoral Edema as a Biomarker of Overall Survival in Glioblastoma: Evidence Using Correspondence Analysis

Introduction

Fractional anisotropy (FA), a diffusion tensor image (DTI) derived biomarker is related to invasion, infiltration, and extension of glioblastoma (GB). We aimed to evaluate FA values and their association with intervals of overall survival (OS).

Materials and Methods

Retrospective study conducted in 36 patients with GB included 23 (63.9%) males, 46 ± 14 y; and 13 (36.1%) females, 53 ± 13; followed up for 36 months. We measured FA at edema, enhancing rim, and necrosis. We created two categorical variables using levels of FA and intervals of OS to evaluate their relationships. Kaplan-Meier method and correspondence analysis evaluated the association between OS (grouped in 7 six-month intervals) and FA measurements.

Results

Median FA values were higher in healthy brain regions (0.351), followed by peritumoral edema (0.190), enhancing ring (0.116), and necrosis (0.071). Pair-wise comparisons among tumor regions showed a significant difference, P < 0.001. The median OS for all patients was 19.3 months; variations in the OS curves among subgroups was significant χ² (3) = 8.48, P = 0.037. Correspondence analysis showed a significant association between FA values in the edema region and the survival intervals χ² (18) = 30.996, P = 0.029.

Conclusions

Alternative multivariate assessment using correspondence analysis might supplement the traditional survival analysis in patients with GB. A close follow-up of the variability of FA in the peritumoral edema region is predictive of the OS within specific six-month interval subgroup. Further studies should focus on predictive models combining surgical and DTI biomarkers.

Ventricle contact is associated with lower survival and increased peritumoral perfusion in glioblastoma

OBJECTIVE

The purpose of this study was to prospectively investigate outcome and differences in peritumoral MRI characteristics of glioblastomas (GBMs) that were in contact with the ventricles (ventricle-contacting tumors) and those that were not (noncontacting tumors). GBMs are heterogeneous tumors with variable survival. Lower survival is suggested for patients with ventricle-contacting tumors than for those with noncontacting tumors. This might be supported by aggressive peritumoral MRI features. However, differences in MRI characteristics of the peritumoral environment between ventricle-contacting and noncontacting GBMs have not yet been investigated.

METHODS

Patients with newly diagnosed GBM underwent preoperative MRI with contrast-enhanced T1-weighted, FLAIR, diffusion-weighted, and perfusion-weighted sequences. Tumors were categorized into ventricle-contacting or noncontacting based on contrast enhancement. Survival analysis was performed using log-rank for univariate analysis and Cox regression for multivariate analysis. Normalized perfusion (relative cerebral blood volume [rCBV]) and diffusion (apparent diffusion coefficient [ADC]) values were calculated in 2 regions: the peritumoral nonenhancing FLAIR region overlapping the subventricular zone and the remaining peritumoral nonenhancing FLAIR region.

RESULTS

Overall survival was significantly lower for patients with contacting tumors than for those with noncontacting tumors (434 vs 747 days, p < 0.001). Progression-free survival showed a comparable trend (260 vs 375 days, p = 0.094). Multivariate analysis confirmed a survival difference for both overall survival (HR 3.930, 95% CI 1.740-8.875, p = 0.001) and progression-free survival (HR 2.506, 95% CI 1.254-5.007, p = 0.009). Peritumoral perfusion was higher in contacting than in noncontacting tumors for both FLAIR regions (p = 0.04). There was no difference in peritumoral ADC values between the 2 groups.

CONCLUSIONS

Patients with ventricle-contacting tumors had poorer outcomes than patients with noncontacting tumors. This disadvantage of ventricle contact might be explained by higher peritumoral perfusion leading to more aggressive behavior.

Assessing therapeutic response non-invasively in a neonatal rat model of acute inflammatory white matter injury using high-field MRI

Perinatal infection and inflammatory episodes in preterm infants are associated with diffuse white matter injury (WMI) and adverse neurological outcomes. Inflammation-induced WMI was previously shown to be linked with later hippocampal atrophy as well as learning and memory impairments in preterm infants. Early evaluation of injury load and therapeutic response with non-invasive tools such as multimodal magnetic resonance imaging (MRI) would greatly improve the search of new therapeutic approaches in preterm infants. Our aim was to evaluate the potential of multimodal MRI to detect the response of interleukin-1 receptor antagonist (IL-1Ra) treatment, known for its neuroprotective properties, during the acute phase of injury on a model of neonatal WMI. Rat pups at postnatal day 3 (P3) received intracerebral injection of lipopolysaccharide with systemic IL-1Ra therapy. 24 h later (P4), rats were imaged with multimodal MRI to assess microstructure by diffusion tensor imaging (DTI) and neurochemical profile of the hippocampus with ¹H-magnetic resonance spectroscopy. Astrocyte and microglial activation, apoptosis and the mRNA expression of pro-inflammatory and necroptotic markers were assessed. During the acute phase of injury, neonatal LPS exposure altered the concentration of hippocampus metabolites related to neuronal integrity, neurotransmission and membrane integrity and induced diffusivity restriction. Just 24 h after initiation of therapy, early indication of IL-1Ra neuroprotective effect could be detected in vivo by non-invasive spectroscopy and DTI, and confirmed with immunohistochemical evaluation and mRNA expression of inflammatory markers and cell death. In conclusion, multimodal MRI, particularly DTI, can detect not only injury but also the acute therapeutic effect of IL-1Ra suggesting that MRI could be a useful non-invasive tool to follow, at early time points, the therapeutic response in preterm infants.

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

OBJECTIVES

Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.

METHODS

Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.

RESULTS

Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).

CONCLUSIONS

The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.

KEY POINTS

• Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.

Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells

The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.

Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Our inability to identify the invasive margin of glioblastomas hampers attempts to achieve local control. Diffusion tensor imaging (DTI) has been implemented clinically to delineate the margin of the tumor infiltration, its derived anisotropic (q) values can extend beyond the contrast-enhanced area and correlates closely with the tumor. However, its correlation with tumor infiltration shown on multivoxel proton magnetic resonance spectroscopy¹ (MRS) and perfusion magnetic resonance imaging (MRI) should be investigated. In this study, we aimed to show tissue characteristics of the q-defined peritumoral invasion on MRS and perfusion MRI. Patients with a primary glioblastoma were included (n = 51). Four regions of interest were analyzed; the contrast-enhanced lesion, peritumoral abnormal q region, peritumoral normal q region, and contralateral normal-appearing white matter. MRS, including choline (Cho)/creatinine (Cr), Cho/N-acetyl-aspartate (NAA) and NAA/Cr ratios, and the relative cerebral blood volume (rCBV) were analyzed. Our results showed an increase in the Cho/NAA (p = 0.0346) and Cho/Cr (p = 0.0219) ratios in the peritumoral abnormal q region, suggestive of tumor invasion. The rCBV was marginally elevated (p = 0.0798). Furthermore, the size of the abnormal q regions was correlated with survival; patients with larger abnormal q regions showed better progression-free survival (median 287 versus 53 days, p = 0.001) and overall survival (median 464 versus 274 days, p = 0.006) than those with smaller peritumoral abnormal q regions of interest. These results support how the DTI q abnormal area identifies tumor activity beyond the contrast-enhanced area, especially correlating with MRS.

Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals

Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI.

Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival

BACKGROUND AND PURPOSE

Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management.

MATERIALS AND METHODS

A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival.

RESULTS

Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005).

CONCLUSIONS

Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.

Is the blood-brain barrier really disrupted in all glioblastomas? A critical assessment of existing clinical data

The blood-brain barrier (BBB) excludes the vast majority of cancer therapeutics from normal brain. However, the importance of the BBB in limiting drug delivery and efficacy is controversial in high-grade brain tumors, such as glioblastoma (GBM). The accumulation of normally brain impenetrant radiographic contrast material in essentially all GBM has popularized a belief that the BBB is uniformly disrupted in all GBM patients so that consideration of drug distribution across the BBB is not relevant in designing therapies for GBM. However, contrary to this view, overwhelming clinical evidence demonstrates that there is also a clinically significant tumor burden with an intact BBB in all GBM, and there is little doubt that drugs with poor BBB permeability do not provide therapeutically effective drug exposures to this fraction of tumor cells. This review provides an overview of the clinical literature to support a central hypothesis: that all GBM patients have tumor regions with an intact BBB, and cure for GBM will only be possible if these regions of tumor are adequately treated.

Magnetic Resonance Imaging Parameters and Their Impact on Survival of Patients with Glioblastoma: Tumor Perfusion Predicts Survival

BACKGROUND

Many prognostic factors influence overall survival (OS) of patients with glioblastoma. Despite gross total resection and Stupp protocol adherence, many patients have poor survival. Perfusion magnetic resonance imaging may assist in diagnosis, treatment monitoring, and prognostication.

METHODS

This retrospective study of 36 patients with glioblastoma assessed influence of preoperative magnetic resonance imaging parameters reflecting tumor cell density and vascularity and patient age on OS.

RESULTS

The area under curve based on optimal receiver operating characteristic curves for the perfusion parameters normalized relative tumor blood volume (n_rTBV) and normalized relative tumor blood flow (n_rTBF) were 0.92 and 0.89, respectively, and the highest among all imaging parameters and age. OS showed strongly negative correlations with corrected n_rTBV (R = -0.70; P < 0.001) and n_rTBF (R = -0.67; P < 0.001). The Cox model, which included age and imaging parameters, demonstrated that n_rTBV and n_rTBF were most predictive of OS, with hazard ratios of 5.97 (P = 0.0001) and 8.76 (P = 0.0001), respectively, compared with 1.63 (P = 0.19) for age. Eighteen patients with corrected n_rTBV ≤2.5 (best cutoff value) had a median OS of 15.1 months (95% confidence interval (CI), 11.34-21.25) compared with 2.8 months (95% CI, 1.48-4.03; P < 0.001) for 18 patients with corrected n_rTBV >2.5. Twenty-four patients with n_rTBF ≤2.79 had a median OS of 12 months (95% CI, 10.46-17.9) compared with 2.8 months for 12 patients with n_rTBF >2.79 (95% CI, 1.31-4.2; P < 0.001).

CONCLUSIONS

The dominant predictors of OS are normalized perfusion parameters n_rTBV and n_rTBF. Preoperative perfusion imaging may be used as a surrogate to predict glioblastoma aggressiveness and survival independent of treatment.

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

Advanced MRI and molecular markers have been raised as crucial to improve prognostic models for patients having glioblastoma (GBM) lesions. In particular, different MR perfusion based markers describing vascular intrapatient heterogeneity have been correlated with tumor aggressiveness, and represent key information to understand tumor resistance against effective therapies of these neoplasms. Recently, hemodynamic tissue signature (HTS) markers based on MR perfusion images have been demonstrated to be useful for describing the heterogeneity of GBM at the voxel level, as well as demonstrating significant correlations with the patient's overall survival. In this work, we analyze the abilities of these markers to improve the conventional prognostic models based on clinical, morphological, and demographic features. Our results, in both the regression and classification tests, show that inclusion of the HTS markers improves the reliability of prognostic models. The HTS method is fully automatic and it is available for research use at http://www.oncohabitats.upv.es.

Findings of DTI-p maps in comparison with T2/T2-FLAIR to assess postoperative hyper-signal abnormal regions in patients with glioblastoma

PURPOSE

The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T₂/T₂-FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach.

MATERIALS AND METHODS

Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T₂, T₂-FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T₂, T₂-FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic method. Discordance Index (DI) and area difference percentage between the three above regions from the three modalities were calculated for each patient.

RESULTS

Area of abnormality in the p-map was smaller than the corresponding areas in the T₂ and T₂-FLAIR images in 17 patients; with mean difference percentage of 30 ± 0.15 and 35 ± 0.15, respectively. Abnormal region in the p-map was larger than the corresponding areas in the T₂-FLAIR and T₂ images in 4 patients; with mean difference percentage of 26 ± 0.17 and 29 ± 0.28, respectively. This region in the p-map was larger than the one in the T₂ image and smaller than the one in the T₂-FLAIR image in 3 patients; with mean difference percentage of 34 ± 0.08 and 27 ± 0.06, respectively. Lack of concordance was observed ranged from 0.214-0.772 for T₂-FLAIR/p-map (average: 0.462 ± 0.18), 0.266-0.794 for T₂ /p-map (average: 0.468 ± 0.13) and 0.123-0.776 for T₂/ T₂-FLAIR (average: 0.423 ± 0.2). These regions on three modalities were segmented using a semi-automatic segmentation method with over 86% sensitivity, 90% specificity and 89% dice score for three modalities.

CONCLUSION

It is noted that T₂, T₂-FLAIR and DTI p-maps represent different but complementary information for delineation of glioblastoma tumor margins. Therefore, this study suggests DTI p-map modality as a candidate to improve target volume delineation based on conventional modalities, which needs further investigations with follow-up data to be confirmed.

Residual enhancing disease after surgery for glioblastoma: evaluation of practice in the United Kingdom

BACKGROUND

A growing body of clinical data highlights the prognostic importance of achieving gross total resection (GTR) in patients with glioblastoma. The aim of this study was to determine nationwide practice and attitudes towards achieving GTR and dealing with residual enhancing disease.

METHODS

The study was in 2 parts: an electronic questionnaire sent to United Kingdom neuro-oncology surgeons to assess surgical practice followed by a 3-month prospective, multicenter observational study of current neurosurgical oncology practice.

RESULTS

Twenty-seven surgeons representing 22 neurosurgical units completed the questionnaire. Prospective data were collected for 113 patients from 15 neurosurgical units. GTR was deemed to be achieved at time of surgery in 82% (91/111) of cases, but in only 45% (36/80) on postoperative MRI. Residual enhancing disease was deemed operable in 16.3% (13/80) of cases, however, no patient underwent early repeat surgery for residual enhancing disease. The most commonly cited reason (38.5%, 5/13) was perceived lack of clinical benefit.

CONCLUSION

There is a subset of patients for whom GTR is thought possible, but not achieved at surgery. For these patients, early repeat resection may improve overall survival. Further prospective surgical research is required to better define the prognostic implications of GTR for residual enhancing disease and examine the potential benefit of this early re-intervention.

Less Invasive Phenotype Found in Isocitrate Dehydrogenase-mutated Glioblastomas than in Isocitrate Dehydrogenase Wild-Type Glioblastomas: A Diffusion-Tensor Imaging Study

Purpose To explore the diffusion-tensor (DT) imaging-defined invasive phenotypes of both isocitrate dehydrogenase (IDH-1)-mutated and IDH-1 wild-type glioblastomas. Materials and Methods Seventy patients with glioblastoma were prospectively recruited and imaged preoperatively. All patients provided signed consent, and the local research ethics committee approved the study. Patients underwent surgical resection, and tumor samples underwent immunohistochemistry for IDH-1 R132H mutations. DT imaging data were coregistered to the anatomic magnetic resonance study and reconstructed to provide the anisotropic and isotropic components of the DT. The invasive phenotype was determined by using previously published criteria and correlated with IDH-1 mutation status by using the Freeman-Halton extension of the Fisher exact probability test. Results Nine patients had an IDH-1 mutation and 61 had IDH-1 wild type. All of the patients with IDH-1 mutation had a minimally invasive DT imaging phenotype. Among the IDH-1 wild-type tumors, 42 of 61 (69%) were diffusively invasive glioblastomas, 14 of 61 (23%) were locally invasive, and five of 61 (8%) were minimally invasive (P < .001). Conclusion IDH-mutated glioblastomas have a less invasive phenotype compared with IDH wild type. This finding may have implications for individualizing the extent of surgical resection and radiation therapy volumes.

Combined 3 Tesla MRI Biomarkers Improve the Differentiation between Benign vs Malignant Single Ring Enhancing Brain Masses

PURPOSE

To evaluate whether the combination of imaging biomarkers obtained by means of different 3 Tesla (3T) Magnetic Resonance Imaging (MRI) advanced techniques can improve the diagnostic accuracy in the differentiation between benign and malignant single ring-enhancing brain masses.

MATERIALS AND METHODS

14 patients presenting at conventional 3T MRI single brain mass with similar appearance as regard ring enhancement, presence of peri-lesional edema and absence of hemorrhage signs were included in the study. All lesions were histologically proven: 5 pyogenic abscesses, 6 glioblastomas, and 3 metastases. MRI was performed at 3 Tesla and included Diffusion Weighted Imaging (DWI), Dynamic Susceptibility Contrast -Perfusion Weighted Imaging (DSC-PWI), Magnetic Resonance Spectroscopy (MRS), and Diffusion Tensor Imaging (DTI). Imaging biomarkers derived by those advanced techniques [Cerebral Blood Flow (CBF), relative Cerebral Blood Volume (rCBV), relative Main Transit Time (rMTT), Choline (Cho), Creatine (Cr), Succinate, N-Acetyl Aspartate (NAA), Lactate (Lac), Lipids, relative Apparent Diffusion Coefficient (rADC), and Fractional Anisotropy (FA)] were detected by two experienced neuroradiologists in joint session in 4 areas: Internal Cavity (IC), Ring Enhancement (RE), Peri-Lesional edema (PL), and Contralateral Normal Appearing White Matter (CNAWM). Significant differences between benign (n = 5) and malignant (n = 9) ring enhancing lesions were tested with Mann-Withney U test. The diagnostic accuracy of MRI biomarkers taken alone and MRI biomarkers ratios were tested with Receiver Operating Characteristic (ROC) analysis with an Area Under the Curve (AUC) ≥ 0.9 indicating a very good diagnostic accuracy of the variable.

RESULTS

Five MRI biomarker ratios achieved excellent accuracy: IC-rADC/PL-NAA (AUC = 1), IC-rADC/IC-FA (AUC = 0.978), RE-rCBV/RE-FA (AUC = 0.933), IC-rADC/RE-FA (AUC = 0.911), and IC-rADC/PL-FA (AUC = 0.911). Only IC-rADC achieved a very good diagnostic accuracy (AUC = 0.909) among MRI biomarkers taken alone.

CONCLUSION

Although the major limitation of the study was the small sample size, preliminary results seem to suggest that combination of multiple 3T MRI biomarkers is a feasible approach to MRI biomarkers in order to improve diagnostic accuracy in the differentiation between benign and malignant single ring enhancing brain masses. Further studies in larger cohorts are needed to reach definitive conclusions.

Diffusion tensor imaging with direct cytopathological validation: characterisation of decorin treatment in experimental juvenile communicating hydrocephalus

BACKGROUND

In an effort to develop novel treatments for communicating hydrocephalus, we have shown previously that the transforming growth factor-β antagonist, decorin, inhibits subarachnoid fibrosis mediated ventriculomegaly; however decorin's ability to prevent cerebral cytopathology in communicating hydrocephalus has not been fully examined. Furthermore, the capacity for diffusion tensor imaging to act as a proxy measure of cerebral pathology in multiple sclerosis and spinal cord injury has recently been demonstrated. However, the use of diffusion tensor imaging to investigate cytopathological changes in communicating hydrocephalus is yet to occur. Hence, this study aimed to determine whether decorin treatment influences alterations in diffusion tensor imaging parameters and cytopathology in experimental communicating hydrocephalus. Moreover, the study also explored whether diffusion tensor imaging parameters correlate with cellular pathology in communicating hydrocephalus.

METHODS

Accordingly, communicating hydrocephalus was induced by injecting kaolin into the basal cisterns in 3-week old rats followed immediately by 14 days of continuous intraventricular delivery of either human recombinant decorin (n = 5) or vehicle (n = 6). Four rats remained as intact controls and a further four rats served as kaolin only controls. At 14-days post-kaolin, just prior to sacrifice, routine magnetic resonance imaging and magnetic resonance diffusion tensor imaging was conducted and the mean diffusivity, fractional anisotropy, radial and axial diffusivity of seven cerebral regions were assessed by voxel-based analysis in the corpus callosum, periventricular white matter, caudal internal capsule, CA1 hippocampus, and outer and inner parietal cortex. Myelin integrity, gliosis and aquaporin-4 levels were evaluated by post-mortem immunohistochemistry in the CA3 hippocampus and in the caudal brain of the same cerebral structures analysed by diffusion tensor imaging.

RESULTS

Decorin significantly decreased myelin damage in the caudal internal capsule and prevented caudal periventricular white matter oedema and astrogliosis. Furthermore, decorin treatment prevented the increase in caudal periventricular white matter mean diffusivity (p = 0.032) as well as caudal corpus callosum axial diffusivity (p = 0.004) and radial diffusivity (p = 0.034). Furthermore, diffusion tensor imaging parameters correlated primarily with periventricular white matter astrocyte and aquaporin-4 levels.

CONCLUSIONS

Overall, these findings suggest that decorin has the therapeutic potential to reduce white matter cytopathology in hydrocephalus. Moreover, diffusion tensor imaging is a useful tool to provide surrogate measures of periventricular white matter pathology in communicating hydrocephalus.