Relationship of pre-surgery metabolic and physiological MR imaging parameters to survival for patients with untreated GBM

Magnetic Resonance Imaging Sequences and Technologies in Adaptive Radiation Therapy

Radiation therapy is essential in both curative and palliative treatments for most cancers. However, traditional radiation therapy workflows using computed tomography (CT) simulation-based planning and cone beam CT image guidance face several technical challenges, including limited tumor visibility and daily fluctuations in tumor size and shape. Magnetic resonance imaging (MRI) guided linear accelerators (MR-Linacs) address these issues by enabling precise visualization of changes in tumor position and morphologic changes, as well as changes in surrounding organs-at-risk. The hybrid MR-Linac systems combine MRI with linear accelerator technology, offering enhanced soft tissue visualization and the potential for adaptive radiation therapy (ART). This narrative review provides a comprehensive introduction to MR guided ART technologies, covering protocol optimization with appropriate pulse sequence selection and parameter adjustment for clinical implementations on various disease sites.

Prognostic value of preoperative diffusion restriction in glioblastoma

INTRODUCTION

Although glioblastoma (GBM) has a very poor prognosis, overall survival (OS) in treated patients shows great difference varying from few days to several months. Identifying factors explaining this difference would improve management of patient treatment.

AIM

To determine the relevance of diffusion restriction in newly diagnosed treatment-naïve GBM patients.

METHODS

Preoperative magnetic resonance scans of 33 patients with GBM were reviewed. Regions of interest including all the T2 hyperintense lesion were drawn on diffusion weighted B0 images and transferred to the apparent diffusion coefficient (ADC) map. For each patient, a histogram displaying the ADC values within in the regions of interest was generated. Volumetric parameters including tumor regions with restricted diffusion, parameters derived from histogram and mean ADC value of the tumor were calculated. Their relationship with OS was analyzed.

RESULTS

Patients with mean ADC value < 1415x10-6 mm2/s had a significantly shorter OS (p=0.021). Among volumetric parameters, the percentage of volume within T2 lesion with a normalized ADC value <1.5 times that in white matter was significantly associated with OS (p=0.0045). Patients with a percentage>23.92% had a shorter OS. Among parameters derived from histogram, the 50th percentile showed a trend towards significance for OS (p=0.055) with patients living longer when having higher values of 50th percentile. A difference in OS was observed between patients according to ADC peak of histogram but this difference did not reach statistical significance (p=0.0959).

CONCLUSION

Diffusion magnetic resonance imaging may provide useful information for predicting GBM prognosis.

Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors

This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.

Randomized phase III trial of metabolic imaging-guided dose escalation of radio-chemotherapy in patients with newly diagnosed glioblastoma (SPECTRO GLIO trial)

BACKGROUND

Glioblastoma (GBM) systematically recurs after a standard 60 Gy radio-chemotherapy regimen. Since magnetic resonance spectroscopic imaging (MRSI) has been shown to predict the site of relapse, we analyzed the effect of MRSI-guided dose escalation on overall survival (OS) of patients with newly diagnosed GBM.

METHODS

In this multicentric prospective phase III trial, patients who had undergone biopsy or surgery for a GBM were randomly assigned to a standard dose (SD) of 60 Gy or a high dose (HD) of 60 Gy with an additional simultaneous integrated boost totaling 72 Gy to MRSI metabolic abnormalities, the tumor bed and residual contrast enhancements. Temozolomide was administered concomitantly and maintained for 6 months thereafter.

RESULTS

One hundred and eighty patients were included in the study between March 2011 and March 2018. After a median follow-up of 43.9 months (95% CI [42.5; 45.5]), median OS was 22.6 months (95% CI [18.9; 25.4]) versus 22.2 months (95% CI [18.3; 27.8]) for HD, and median progression-free survival was 8.6 (95% CI [6.8; 10.8]) versus 7.8 months (95% CI [6.3; 8.6]), in SD versus HD, respectively. No increase in toxicity rate was observed in the study arm. The pseudoprogression rate was similar across the SD (14.4%) and HD (16.7%) groups. For O(6)-methylguanine-DNA methyltransferase (MGMT) methylated patients, the median OS was 38 months (95% CI [23.2; NR]) for HD patients versus 28.5 months (95% CI [21.1; 35.7]) for SD patients.

CONCLUSION

The additional MRSI-guided irradiation dose totaling 72 Gy was well tolerated but did not improve OS in newly diagnosed GBM.

TRIAL REGISTRATION

NCT01507506; registration date: December 20, 2011. https://clinicaltrials.gov/ct2/show/NCT01507506?cond=NCT01507506&rank=1.

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.

Modern preoperative imaging and functional mapping in patients with intracranial glioma

Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS::   · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

BACKGROUND AND PURPOSE

All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS).

MATERIALS AND METHODS

MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis.

RESULTS

Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome.

CONCLUSION

Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Glioblastoma Stem-like Cell Detection Using Perfusion and Diffusion MRI

Simple Summary

Glioblastoma stem-like cells (GSCs) are known to be aggressive and radio-resistant and proliferate heterogeneously in preferred environments. Additionally, quantitative diffusion and perfusion MRI biomarkers provide insight into the tissue micro-environment. This study assessed the sensitivity of these imaging biomarkers to GSCs in the hyperintensities-FLAIR region, where relapses may occur. A total of 16 patients underwent an MRI session and biopsies were extracted to study the GSCs. In vivo and in vitro biomarkers were compared and both Apparent Diffusion Coefficient (ADC) and relative Cerebral Blood Volume (rCBV) MRI metrics were found to be good predictors of GSCs presence and aggressiveness.

Abstract

Purpose: With current gold standard treatment, which associates maximum safe surgery and chemo-radiation, the large majority of glioblastoma patients relapse within a year in the peritumoral non contrast-enhanced region (NCE). A subpopulation of glioblastoma stem-like cells (GSC) are known to be particularly radio-resistant and aggressive, and are thus suspected to be the cause of these relapses. Previous studies have shown that their distribution is heterogeneous in the NCE compartment, but no study exists on the sensitivity of medical imaging for localizing these cells. In this work, we propose to study the magnetic resonance (MR) signature of these infiltrative cells. Methods: In the context of a clinical trial on 16 glioblastoma patients, relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC) were measured in a preoperative diffusion and perfusion MRI examination. During surgery, two biopsies were extracted using image-guidance in the hyperintensities-FLAIR region. GSC subpopulation was quantified within the biopsies and then cultivated in selective conditions to determine their density and aggressiveness. Results: Low ADC was found to be a good predictor of the time to GSC neurospheres formation in vitro. In addition, GSCs were found in higher concentrations in areas with high rCBV. Conclusions: This study confirms that GSCs have a critical role for glioblastoma aggressiveness and supports the idea that peritumoral sites with low ADC or high rCBV should be preferably removed when possible during surgery and targeted by radiotherapy.

Simultaneous Mapping of Vasculature, Hypoxia, and Proliferation Using Dynamic Susceptibility Contrast MRI, 18F-FMISO PET, and 18F-FLT PET in Relation to Contrast Enhancement in Newly Diagnosed Glioblastoma

Conventional MRI plays a key role in the management of patients with high-grade glioma, but multiparametric MRI and PET tracers could provide further information to better characterize tumor metabolism and heterogeneity by identifying regions having a high risk of recurrence. In this study, we focused on proliferation, hypervascularization, and hypoxia, all factors considered indicative of poor prognosis. They were assessed by measuring uptake of 18F-3'-deoxy-3'-18F-fluorothymidine (18F-FLT), relative cerebral blood volume (rCBV) maps, and uptake of 18F-fluoromisonidazole (18F-FMISO), respectively. For each modality, the volumes and high-uptake subvolumes (hot spots) were semiautomatically segmented and compared with the contrast enhancement (CE) volume on T1-weighted gadolinium-enhanced (T1w-Gd) images, commonly used in the management of patients with glioblastoma. Methods: Dynamic susceptibility contrast-enhanced MRI (31 patients), 18F-FLT PET (20 patients), or 18F-FMISO PET (20 patients), for a total of 31 patients, was performed on preoperative glioblastoma patients. Volumes and hot spots were segmented on SUV maps for 18F-FLT PET (using the fuzzy locally adaptive bayesian algorithm) and 18F-FMISO PET (using a mean contralateral image + 3.3 SDs) and on rCBV maps (using a mean contralateral image + 1.96 SDs) for dynamic susceptibility contrast-enhanced MRI and overlaid on T1w-Gd images. For each modality, the percentages of the peripheral volumes and the peripheral hot spots outside the CE volume were calculated. Results: All tumors showed highly proliferated, hypervascularized, and hypoxic regions. The images also showed pronounced heterogeneity of both tracers regarding their uptake and rCBV maps, within each individual patient. Overlaid volumes on T1w-Gd images showed that some proliferative, hypervascularized, and hypoxic regions extended beyond the CE volume but with marked differences between patients. The ranges of peripheral volume outside the CE volume were 1.6%-155.5%, 1.5%-89.5%, and 3.1%-78.0% for 18F-FLT, rCBV, and 18F-FMISO, respectively. All patients had hyperproliferative hot spots outside the CE volume, whereas hypervascularized and hypoxic hot spots were detected mainly within the enhancing region. Conclusion: Spatial analysis of multiparametric maps with segmented volumes and hot spots provides valuable information to optimize the management and treatment of patients with glioblastoma.

MR-Guided Radiotherapy for Brain and Spine Tumors

MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

Evaluating survival in subjects with astrocytic brain tumors by dynamic susceptibility-weighted perfusion MR imaging

Purpose

Studies have evaluated the application of perfusion MR for predicting survival in patients with astrocytic brain tumors, but few of them statistically adjust their results to reflect the impact of the variability of treatment administered in the patients. Our aim was to analyze the association between the perfusion values and overall survival time, with adjustment for various clinical factors, including initial treatments and follow-up treatments.

Materials and methods

This study consisted of 51 patients with astrocytic brain tumors who underwent perfusion-weighted MRI with MultiHance® at a dose of 0.1 mmol/kg prior to initial surgery. We measured the mean rCBV, the 5% & 10% maximum rCBV, and the variation of rCBV in the tumors. Comparisons were made between patients with and without 2-year survival using two-sample t-test or Wilcoxon rank-sum test for the continuous data, or chi-square and Fisher exact tests for categorical data. The multivariate cox-proportional hazard regression was fit to evaluate the association between rCBV and overall survival time, with adjustment for clinical factors.

Results

Patients who survived less than 2 years after diagnosis had a higher mean and maximum rCBV and a larger variation of rCBV. After adjusting for clinical factors including therapeutic measures, we found no significant association of overall survival time within 2 years with any of these rCBV values.

Conclusions

Although patients who survived less than 2 years had a higher mean and maximum rCBV and a larger variation of rCBV, rCBV itself may not be used independently for predicting 2-year survival of patients with astrocytic brain tumors.

The diagnostic value of MR spectroscopy versus DWI-MRI in therapeutic planning of suspicious multi-centric cerebral lesions

Background

A broad spectrum of non-neoplastic lesions can radiologically mimic cerebral neoplasms. Magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and diffusion-weighted imaging (DWI) are the most extensively used for enabling lesional characterization of different brain disorders. We aimed to assess the diagnostic value of MRS versus DWI in the diagnosis and therapeutic planning of multicentric cerebral focal lesions and in our retrospective study, we enrolled 64 patients with 100 brain lesions who underwent pre- and post-contrast MRI, MRS, and DWI. Diagnoses supplied by the histopathology and follow up clinical results as a gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated.

Results

Conventional MRI poorly differentiates multiple cerebral lesions with 89.33% sensitivity, 44.4% specificity, and 78% accuracy. MRS results revealed statistical significance for differentiating neoplastic from non-neoplastic lesions as regards Cho/Cr, Cho/NAA, and NAA/Cr ratios (M ± SD) with P < 0.001 (significant), and there is statistical significance for neoplastic lesion differentiation when Cho/NAA and Ch/Cr ratios measured in the pre-lesional areas outside the tumor margin. DWI showed mixed diffusion changes in most of the studied lesions and the measured ADC values ranges showed overlap in neoplastic and non-neoplastic lesions, P value = 0.236* (insignificant).

Conclusion

MRS was found to be a more accurate diagnostic tool than DWI with ADC measurements in the differentiation and therapeutic planning of multicentric cerebral focal lesions.

Management of Glioblastoma, Present and Future

Glioblastomas are the most common malignant brain tumor and despite extensive research have a dismal prognosis. This review focuses on the current treatment paradigms of glioblastoma and highlights current advances in surgical approaches, imaging techniques, molecular diagnostics, and translational efforts. Several promising clinical trials in immunotherapy and personalized medicine are discussed and the importance of quality of life in the patients and their caregivers both during active treatment and survivorship is also commented on.

Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma

OBJECTIVES

To determine whether diffusion- and perfusion-weighted MRI-based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) METHODS: Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning-based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).

RESULTS

The multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.

CONCLUSION

Multiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.

KEY POINTS

• The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.

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.

MR Perfusion and MR Spectroscopy of Brain Neoplasms

Advances in imaging techniques, such as MR perfusion and spectroscopy, are increasingly indispensable in the management and treatment plans of brain neoplasms: from diagnosing, molecular/genetic typing and grading neoplasms, augmenting biopsy results and improving accuracy, to ultimately directing and monitoring treatment and response. New developments in treatment methods have resulted in new diagnostic challenges for conventional MR imaging, such as pseudoprogression, where MR perfusion has the widest current application. MR spectroscopy is showing increasing promise in noninvasively determining genetic subtypes and, potentially, susceptibility to molecular targeted therapies.

Survival prediction of high-grade glioma patients with diffusion kurtosis imaging

PURPOSE

To evaluate the prognostic value of diffusion kurtosis imaging (DKI) for survival prediction of patients with high-grade glioma (HGG).

MATERIALS AND METHODS

DKI was performed for fifty-eight patients with pathologically proven HGG by using a 3-T scanner. The mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) values in the solid part of the tumor were measured and normalized. Univariate Cox regression analysis was used to evaluate the association between overall survival (OS) and sex, age, Karnofsky performance status (KPS), tumor grade, Ki-67 labeling index (LI), extent of resection, use of chemoradiotherapy, MK, MD, and FA. Multivariate Cox regression analysis including sex, age, KPS, extent of resection, use of chemoradiotherapy, MK, MD, and FA was subsequently performed. Spearman's correlation coefficient for OS and the area under receiver operating characteristic curve (AUC) for predicting 2-year survival were calculated for each DKI parameter and further compared.

RESULTS

In univariate Cox regression analyses, OS was significantly associated with the tumor grade, Ki-67 LI, extent of resection, use of chemoradiotherapy, MK, and MD (P < 0.05 for all). Multivariate Cox regression analyses indicated that MK, MD (hazard ratio = 1.582 and 0.828, respectively, for each 0.1 increase in the normalized value), extent of resection and use of chemoradiotherapy were independent predictors of OS. The absolute value of the correlation coefficient for OS and AUC for predicting 2-year survival by MK (rho = -0.565, AUC = 0.841) were higher than those by MD (rho = 0.492, AUC = 0.772), but the difference was not significant.

CONCLUSION

DKI is a promising tool to predict the survival of HGG patients. MK and MD are independent predictors. MK is potentially better associated with OS than MD, which may lead to a more accurate evaluation of HGG patient survival.

Apparent diffusion coefficient and tumor volume measurements help stratify progression-free survival of bevacizumab-treated patients with recurrent glioblastoma multiforme

BACKGROUND

The aim of this study was to determine whether apparent diffusion coefficient (ADC) bi-component curve-fitting histogram analysis and volume percentage change (VPC) prior to bevacizumab treatment can stratify progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma multiforme (GBM) on first recurrence.

METHODS

We retrospectively evaluated 17 patients with recurrent GBM who received bevacizumab and fotemustine (n = 13) or only bevacizumab (n = 4) on first recurrence at our institution between December 2009 and July 2015. Both T2/FLAIR abnormalities and enhancing tumor on T1 images were mapped to the ADC images. ADC-L and ADC-M values were obtained trough bi-Gaussian curve fitting histogram analysis. Furthermore, the study population was dichotomized into two subgroups: patients displaying a reduction in enhancing tumor volume of either >55% or <55% between the mean value calculated at baseline and first follow-up. Subsequently, a second dichotomization was performed according to a reduction in the T2 / FLAIR volume >41% or <41% at first check after treatment. OS and PFS were assessed using volume parameters in a Cox regression model adjusted for significant clinical parameters.

RESULTS

In univariate analysis, contrast-enhanced (CE)-ADC-L was significantly predictive of PFS (p = 0.01) and OS (p = 0.03). When we dichotomized our sample using the 55% cut-off for enhancing tumor volume, CE-VPC was able to predict PFS (p = 0.01) but not OS (p = 0.08). In multivariate analysis, only the CE-ADC-L was predictive of PFS (p = 0.01), albeit not predictive of OS (p = 0.14). CE-ADC-M, T2/FLAIR-ADC-L, T2/FLAIR-ADC, and T2/FLAIR VPC were not significantly predictive of PFS and OS (p > 0.05) in both univariate and multivariate analysis.

CONCLUSIONS

CE-ADC and CE-VPC can stratify PFS for patients with recurrent glioblastoma prior to bevacizumab treatment.

Dose-painting multicenter phase III trial in newly diagnosed glioblastoma: the SPECTRO-GLIO trial comparing arm A standard radiochemotherapy to arm B radiochemotherapy with simultaneous integrated boost guided by MR spectroscopic imaging

Background

Glioblastoma, a high-grade glial infiltrating tumor, is the most frequent malignant brain tumor in adults and carries a dismal prognosis. External beam radiotherapy (EBRT) increases overall survival but this is still low due to local relapses, mostly occurring in the irradiation field. As the ratio of spectra of choline/N acetyl aspartate> 2 (CNR2) on MR spectroscopic imaging has been described as predictive for the site of local relapse, we hypothesized that dose escalation on these regions would increase local control and hence global survival.

Methods/design

In this multicenter prospective phase III trial for newly diagnosed glioblastoma, 220 patients having undergone biopsy or surgery are planned for randomization to two arms. Arm A is the Stupp protocol (EBRT 60 Gy on contrast enhancement + 2 cm margin with concomitant temozolomide (TMZ) and 6 months of TMZ maintenance); Arm B is the same treatment with an additional simultaneous integrated boost of intensity-modulated radiotherapy (IMRT) of 72Gy/2.4Gy delivered on the MR spectroscopic imaging metabolic volumes of CHO/NAA > 2 and contrast-enhancing lesions or resection cavity. Stratification is performed on surgical and MGMT status.

Discussion

This is a dose-painting trial, i.e. delivery of heterogeneous dose guided by metabolic imaging. The principal endpoint is overall survival. An online prospective quality control of volumes and dose is performed in the experimental arm. The study will yield a large amount of longitudinal multimodal MR imaging data including planning CT, radiotherapy dosimetry, MR spectroscopic, diffusion and perfusion imaging.

Trial registration

NCT01507506, registration date December 20, 2011.

Molecular PET imaging in adaptive radiotherapy: brain

INTRODUCTION

Owing to their heterogeneity and radioresistance, the prognosis of primitive brain tumors, which are mainly glial tumors, remains poor. Dose escalation in radioresistant areas is a potential issue for improving local control and overall survival. This review focuses on advances in biological and metabolic imaging of brain tumors that are proving to be essential for defining tumor target volumes in radiation therapy (RT) and for increasing the use of DPRT (dose painting RT) and ART (adaptative RT), to optimize dose in radio-resistant areas.

EVIDENCE ACQUISITION

Various biological imaging modalities such as PET (hypoxia, glucidic metabolism, protidic metabolism, cellular proliferation, inflammation, cellular membrane synthesis) and MRI (spectroscopy) may be used to identify these areas of radioresistance. The integration of these biological imaging modalities improves the diagnosis, prognosis and treatment of brain tumors.

EVIDENCE SYNTHESIS

Technological improvements (PET and MRI), the development of research, and intensive cooperation between different departments are necessary before using daily metabolic imaging (PET and MRI) to treat patients with brain tumors.

CONCLUSIONS

The adaptation of treatment volumes during RT (ART) seems promising, but its development requires improvements in several areas and an interdisciplinary approach involving radiology, nuclear medicine and radiotherapy. We review the literature on biological imaging to outline the perspectives for using DPRT and ART in brain tumors.

Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery

PURPOSE

Glioblastoma (GBM) neurosurgical resection relies on contrast-enhanced MRI-based neuronavigation. However, it is well-known that infiltrating tumor extends beyond contrast enhancement. Fluorescence-guided surgery (FGS) using 5-aminolevulinic acid (5-ALA) was evaluated to improve extent of resection (EOR) of GBMs. Preoperative morphological tumor metrics were also assessed.

PROCEDURES

Thirty patients from a phase II trial evaluating 5-ALA FGS in newly diagnosed GBM were assessed. Tumors were segmented preoperatively to assess morphological features as well as postoperatively to evaluate EOR and residual tumor volume (RTV).

RESULTS

Median EOR and RTV were 94.3 % and 0.821 cm(3), respectively. Preoperative surface area to volume ratio and RTV were significantly associated with overall survival, even when controlling for the known survival confounders.

CONCLUSIONS

This study supports claims that 5-ALA FGS is helpful at decreasing tumor burden and prolonging survival in GBM. Moreover, morphological indices are shown to impact both resection and patient survival.

Association of early changes in 1H MRSI parameters with survival for patients with newly diagnosed glioblastoma receiving a multimodality treatment regimen

Background

The heterogeneous biology of glioblastoma (GBM) emphasizes the need for imaging methods to assess tumor burden and assist in evaluating individual patients. The purpose of this study was to investigate early changes in metrics from 3D 1H magnetic resonance spectroscopic imaging (MRSI) data, compare them with anatomic lesion volumes, and determine whether they were associated with survival for patients with newly diagnosed GBM receiving a multimodality treatment regimen.

Methods

Serial MRI and MRSI scans provided estimates of anatomic lesion volumes and levels of choline, creatine, N-acetylaspartate, lactate, and lipid. The association of metrics derived from these data with survival was assessed using Cox proportional hazards models with adjustments for age, Karnofsky performance score, and extent of resection. Temporal changes in parameters were evaluated using a Wilcoxon signed rank test.

Results

Anatomic lesion volumes at the post-radiotherapy (RT) scan, metabolic lesion volume at mid-RT and post-RT scans, as well as metrics describing levels of choline, lactate, and lipid were associated with overall survival. There was a significant reduction in the enhancing lesion volume, increase in T2 lesion volume from mid-RT to post-RT, and decrease in parameters describing metabolite levels during these early time points.

Conclusion

The MRSI data provided metrics that described the effects of treatment on the metabolic lesion burden and were associated with overall survival. This suggests that adding these parameters to standard assessments of changes in anatomic lesion volumes could contribute to making early decisions about the efficacy of such combination therapies.

Identifying Voxels at Risk for Progression in Glioblastoma Based on Dosimetry, Physiologic and Metabolic MRI

Despite the longstanding role of radiation in cancer treatment and the presence of advanced, high-resolution imaging techniques, delineation of voxels at-risk for progression remains purely a geometric expansion of anatomic images, missing subclinical disease at risk for recurrence while treating potentially uninvolved tissue and increasing toxicity. This remains despite the modern ability to precisely shape radiation fields. A striking example of this is the treatment of glioblastoma, a highly infiltrative tumor that may benefit from accurate identification of subclinical disease. In this study, we hypothesize that parameters from physiologic and metabolic magnetic resonance imaging (MRI) at diagnosis could predict the likelihood of voxel progression at radiographic recurrence in glioblastoma by identifying voxel characteristics that indicate subclinical disease. Integrating dosimetry can reveal its effect on voxel outcome, enabling risk-adapted voxel dosing. As a system example, 24 patients with glioblastoma treated with radiotherapy, temozolomide and an anti-angiogenic agent were analyzed. Pretreatment median apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), vessel leakage (percentage recovery), choline-to-NAA index (CNI) and dose of voxels in the T2 nonenhancing lesion (NEL), T1 post-contrast enhancing lesion (CEL) or normal-appearing volume (NAV) of brain, were calculated for voxels that progressed [NAV→NEL, CEL (N = 8,765)] and compared against those that remained stable [NAV→NAV (N = 98,665)]. Voxels that progressed (NAV→NEL) had significantly different (P < 0.01) ADC (860), FA (0.36) and CNI (0.67) versus stable voxels (804, 0.43 and 0.05, respectively), indicating increased cell turnover, edema and decreased directionality, consistent with subclinical disease. NAV→CEL voxels were more abnormal (1,014, 0.28, 2.67, respectively) and leakier (percentage recovery = 70). A predictive model identified areas of recurrence, demonstrating that elevated CNI potentiates abnormal diffusion, even far (>2 cm) from the tumor and dose escalation >45 Gy has diminishing benefits. Integrating advanced MRI with dosimetry can identify at voxels at risk for progression and may allow voxel-level risk-adapted dose escalation to subclinical disease while sparing normal tissue. When combined with modern planning software, this technique may enable risk-adapted radiotherapy in any disease site with multimodal imaging.

Adult Brain Tumors: Clinical Applications of Magnetic Resonance Spectroscopy

Proton magnetic resonance spectroscopy (H-MRS) may be helpful in suggesting tumor histology and tumor grade and may better define tumor extension and the ideal site for biopsy compared with conventional magnetic resonance (MR) imaging. A multifunctional approach with diffusion-weighted imaging, perfusion-weighted imaging, and permeability maps, along with H-MRS, may enhance the accuracy of the diagnosis and characterization of brain tumors and estimation of therapeutic response. Integration of advanced imaging techniques with conventional MR imaging and the clinical history help to improve the accuracy, sensitivity, and specificity in differentiating tumors and nonneoplastic lesions.

Intraoperative serum lactate is not a predictor of survival after glioblastoma surgery

BACKGROUND

Cancer cells can produce lactate in high concentrations. Two previous studies examined the clinical relevance of serum lactate as a biomarker in patients with brain tumors. Patients with high-grade tumors have higher serum concentrations of lactate than those with low-grade tumors. We hypothesized that serum lactic could be used of biomarker to predictor of survival in patients with glioblastoma (GB).

METHODS

This was a retrospective study. Demographic, lactate concentrations and imaging data from 275 adult patients with primary GB was included in the analysis. The progression free survival (PFS) and overall survival (OS) rates were compared in patients who had above and below the median concentrations of lactate. We also investigated the correlation between lactate concentrations and tumor volume. Multivariate analyses were conducted to test the association lactate, tumor volume and demographic variables with PFS and OS.

RESULTS

The median serum concentration of lactate was 2.3mmol/L. A weak correlation was found between lactate concentrations and tumor volume. Kaplan-Meier curves demonstrated similar survival in patients with higher or lower than 2.3mmol/L of lactate. The multivariate analysis indicated that the intraoperative levels of lactate were not independently associated with changes in survival. On another hand, a preoperative T1 volume was an independent predictor PFS (HR 95%CI: 1.41, 1.02-1.82, p=0.006) and OS (HR 95%CI: 1.47, 1.11-1.96, p=0.006).

CONCLUSION

This retrospective study suggests that the serum concentrations of lactate cannot be used as a biomarker to predict survival after GB surgery. To date, there are no clinically available serum biomarkers to determine prognosis in patients with high-grade gliomas. These tumors may produce high levels of lactic acid. We hypothesized that serum lactic could be used of biomarker to predictor of survival in patients with glioblastoma (GB). In this study, we collected perioperative and survival data from 275 adult patients with primary high-grade gliomas to determine whether intraoperative serum acid lactic concentrations can serve as a marker of prognosis. The median serum concentration of lactate was 2.3mmol/L. Our analysis indicated the intraoperative levels of lactate were not independently associated with changes in survival. This retrospective study suggests that the serum concentrations of lactate cannot be used as a biomarker to predict survival after GB surgery.

Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma

BACKGROUND AND PURPOSE

ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative to ADC.

MATERIALS AND METHODS

Pre-radiation therapy diffusion-weighted and structural imaging of 40 patients with glioblastoma were examined retrospectively. Restriction spectrum imaging and ADC-based hypercellularity volume fraction (restriction spectrum imaging-FLAIR volume fraction, restriction spectrum imaging-contrast-enhanced volume fraction, ADC-FLAIR volume fraction, ADC-contrast-enhanced volume fraction) and intensities (restriction spectrum imaging-FLAIR 90th percentile, restriction spectrum imaging-contrast-enhanced 90th percentile, ADC-FLAIR 10th percentile, ADC-contrast-enhanced 10th percentile) within the contrast-enhanced and FLAIR hyperintensity VOIs were calculated. The association of diffusion imaging metrics, contrast-enhanced volume, and FLAIR hyperintensity volume with progression-free survival and overall survival was evaluated by using Cox proportional hazards models.

RESULTS

Among the diffusion metrics, restriction spectrum imaging-FLAIR volume fraction was the strongest prognostic metric of progression-free survival (P = .036) and overall survival (P = .007) in a multivariate Cox proportional hazards analysis, with higher values indicating earlier progression and shorter survival. Restriction spectrum imaging-FLAIR 90th percentile was also associated with overall survival (P = .043), with higher intensities, indicating shorter survival. None of the ADC metrics were associated with progression-free survival/overall survival. Contrast-enhanced volume exhibited a trend toward significance for overall survival (P = .063).

CONCLUSIONS

Restriction spectrum imaging-derived cellularity in FLAIR hyperintensity regions may be a more robust prognostic marker than ADC and conventional imaging for early progression and poorer survival in patients with glioblastoma. However, future studies with larger samples are needed to explore its predictive ability.

Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer?

Abnormal metabolism is a key tumor hallmark. Proton magnetic resonance spectroscopy (1H-MRS) allows measurement of metabolite concentration that can be utilized to characterize tumor metabolic changes. 1H-MRS measurements of specific metabolites have been implemented in the clinic. This article performs a systematic review of image acquisition and interpretation of 1H-MRS for cancer evaluation, evaluates its strengths and limitations, and correlates metabolite peaks at 1H-MRS with diagnostic and prognostic parameters of cancer in different tumor types.

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

BACKGROUND

High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI.

METHODS

Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype.

RESULTS

Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features.

CONCLUSION

Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.

Serial analysis of 3D H-1 MRSI for patients with newly diagnosed GBM treated with combination therapy that includes bevacizumab

Interpretation of changes in the T1- and T2-weighted MR images from patients with newly diagnosed glioblastoma (GBM) treated with standard of care in conjunction with anti-angiogenic agents is complicated by pseudoprogression and pseudoresponse. The hypothesis being tested in this study was that 3D H-1 magnetic resonance spectroscopic imaging (MRSI) provides estimates of levels of choline, creatine, N-acetylaspartate (NAA), lactate and lipid that change in response to treatment and that metrics describing these characteristics are associated with survival. Thirty-one patients with newly diagnosed GBM and being treated with radiation therapy (RT), temozolomide, erlotinib and bevacizumab were recruited to receive serial MR scans that included 3-D lactate edited MRSI at baseline, mid-RT, post-RT and at specific follow-up time points. The data were processed to provide estimates of metrics representing changes in metabolite levels relative to normal appearing brain. Cox proportional hazards analysis was applied to examine the relationship of these parameters with progression free survival (PFS) and overall survival (OS). There were significant reductions in parameters that describe relative levels of choline to NAA and creatine, indicating that the treatment caused a decrease in tumor cellularity. Changes in the levels of lactate and lipid relative to the NAA from contralateral brain were consistent with vascular normalization. Metabolic parameters from the first serial follow-up scan were associated with PFS and OS, when accounting for age and extent of resection. Integrating metabolic parameters into the assessment of patients with newly diagnosed GBM receiving therapies that include anti-angiogenic agents may be helpful for tracking changes in tumor burden, resolving ambiguities in anatomic images caused by non-specific treatment effects and for predicting outcome.

Fractional Anisotropy Correlates with Overall Survival in Glioblastoma

OBJECTIVE

Glioblastoma (GB) is an infiltrative disease that results in microstructural damage on a cellular level. Fractional anisotropy (FA) is an important estimate of diffusion tensor imaging (DTI) that can be used to assess microstructural integrity. The aim of this study was to examine the correlation between FA values and overall survival (OS) in patients with GB.

METHODS

This retrospective single-center study included 122 consecutive patients with GB (50 women; median age, 63 years) with preoperative MRI including fluid attenuated inversion recovery (FLAIR), contrast-enhanced T1-weighted sequences, and DTI. FA and apparent diffusion coefficient (ADC) values in contrast-enhancing lesions (FA-CEL, FA-ADC), nonenhancing lesions, and central tumor regions were correlated to histopathologic and clinical parameters. Univariate and multivariate survival analyses were performed.

RESULTS

Patients with low FA-CEL (median <0.31) showed significantly improved OS in univariate analysis (P = 0.028). FA-CEL also showed a positive correlation with Ki-67 proliferation index (P = 0.003). However, in a multivariate survival model, FA values could not be identified as independent prognostic parameters beside established factors such as age and Karnofsky performance scale score. FA values in nonenhancing lesions and central tumor regions and mean ADC values had no distinct influence on OS.

CONCLUSIONS

FA values can provide prognostic information regarding OS in patients with GB. There is a correlation between FA-CEL values and Ki-67 proliferation index, a marker for malignancy. Noninvasive identification of more aggressive GB growth patterns might be beneficial for preoperative risk evaluation and estimation of prognosis.

Automatic Analysis of Cellularity in Glioblastoma and Correlation with ADC Using Trajectory Analysis and Automatic Nuclei Counting

Objective

Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting.

Materials and Methods

Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis.

Results

There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model.

Conclusions

Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.

Non-invasive metabolic imaging of brain tumours in the era of precision medicine

The revolution in cancer genomics has uncovered a variety of clinically relevant mutations in primary brain tumours, creating an urgent need to develop non-invasive imaging biomarkers to assess and integrate this genetic information into the clinical management of patients. Metabolic reprogramming is a central hallmark of cancer, including brain tumours; indeed, many of the molecular pathways implicated in the pathogenesis of brain tumours result in reprogramming of metabolism. This relationship provides the opportunity to devise in vivo metabolic imaging modalities to improve diagnosis, patient stratification, and monitoring of treatment response. Metabolic phenomena, such as the Warburg effect and altered mitochondrial metabolism, can be leveraged to image brain tumours using techniques including PET and MRI. Moreover, genetic alterations, such as mutations affecting isocitrate dehydrogenase, are associated with unique metabolic signatures that can be detected using magnetic resonance spectroscopy. The need to translate our understanding of the molecular features of brain tumours into imaging modalities with clinical utility is growing; metabolic imaging provides a unique platform to achieve this objective. In this Review, we examine the molecular basis for metabolic reprogramming in brain tumours, and examine current non-invasive metabolic imaging strategies that can be used to interrogate these molecular characteristics with the ultimate goal of guiding and improving patient care.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma

BACKGROUND

The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (k^trans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients.

METHODS

Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS.

RESULTS

Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBV_CE for PFS, and age, sex, KPS, and EOR for OS.

CONCLUSIONS

MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBV_CE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.

Proton MRS imaging in pediatric brain tumors

Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors.

Basic Principles and Clinical Applications of Magnetic Resonance Spectroscopy in Neuroradiology

Magnetic resonance spectroscopy is a powerful tool to assist daily clinical diagnostics. This review is intended to give an overview on basic principles of the technology, discuss some of its technical aspects, and present typical applications in daily clinical routine in neuroradiology.

Current and future strategies for treatment of glioma

Gliomas are one of the most common types of primary brain tumors and have remained particularly challenging to treat. This review illustrates a multidisciplinary approach to the treatment of glioma and glioblastoma. We will review current advances in surgical approaches, novel imaging techniques, advanced molecular characterization of tumors and translational efforts for treatment. We will focus on current clinical trials as well as the pursuit of personalized or precision therapy. We will also comment on the importance of both quality of life of our patients and their care givers.

Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas

Background

Glioblastomas progress rapidly making response evaluation using MRI insufficient since treatment effects are not detectable until months after initiation of treatment. Thus, there is a strong need for supplementary biomarkers that could provide reliable and early assessment of treatment efficacy. Analysis of alterations in the metabolome may be a source for identification of new biomarker patterns harboring predictive information. Ideally, the biomarkers should be found within an easily accessible compartment such as the blood.

Method

Using gas-chromatographic- time-of-flight-mass spectroscopy we have analyzed serum samples from 11 patients with glioblastoma during the initial phase of radiotherapy. Fasting serum samples were collected at admittance, on the same day as, but before first treatment and in the morning after the second and fifth dose of radiation. The acquired data was analyzed and evaluated by chemometrics based bioinformatics methods. Our findings were compared and discussed in relation to previous data from microdialysis in tumor tissue, i.e. the extracellular compartment, from the same patients.

Results

We found a significant change in metabolite pattern in serum comparing samples taken before radiotherapy to samples taken during early radiotherapy. In all, 68 metabolites were lowered in concentration following treatment while 16 metabolites were elevated in concentration. All detected and identified amino acids and fatty acids together with myo-inositol, creatinine, and urea were among the metabolites that decreased in concentration during treatment, while citric acid was among the metabolites that increased in concentration. Furthermore, when comparing results from the serum analysis with findings in tumor extracellular fluid we found a common change in metabolite patterns in both compartments on an individual patient level. On an individual metabolite level similar changes in ornithine, tyrosine and urea were detected. However, in serum, glutamine and glutamate were lowered after treatment while being elevated in the tumor extracellular fluid.

Conclusion

Cross-validated multivariate statistical models verified that the serum metabolome was significantly changed in relation to radiation in a similar pattern to earlier findings in tumor tissue. However, all individual changes in tissue did not translate into changes in serum. Our study indicates that serum metabolomics could be of value to investigate as a potential marker for assessing early response to radiotherapy in malignant glioma.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-016-0626-6) contains supplementary material, which is available to authorized users.

Effect of 10 different polymorphisms on preoperative volumetric characteristics of glioblastoma multiforme

There is a distinct diversity between the appearance of every glioblastoma multiforme (GBM) on pretreatment magnetic resonance imaging (MRI) with a potential impact on clinical outcome and survival of the patients. The object of this study was to determine the impact of 10 different single nucleotide polymorphisms (SNPs) on various volumetric parameters in patients harboring a GBM. We prospectively analyzed 20 steroid-naïve adult patients who had been treated for newly diagnosed GBM. The volumetry was performed using MRI with the help of a semiautomated quantitative software measuring contrast enhancing tumor volume including necrosis, central necrosis alone and peritumoral edema (PTE). We calculated ratios between the tumor volume and edema (ETR), respectively necrosis (NTR). SNP analysis was done using genomic DNA extracted from peripheral blood genotyped via PCR and sequencing. There was a strong correlation between tumor volume and PTE (p < 0.001), necrosis (p < 0.001) and NTR (p = 0.003). Age and sex had no influence on volumetric data. The Aquaporin 4-31G > A SNP had a significant influence on the ETR (p = 0.042) by decreasing the measured edema compared with the tumor volume. The Interleukin 8-251A > T SNP was significantly correlated with an increased tumor (p = 0.048), PTE (p = 0.033) and necrosis volume (p = 0.028). We found two SNPs with a distinct impact on pretreatment tumor characteristics, presenting a potential explanation for the individual diversity of GBM appearance on MRI and influence on survival.

Magnetic Resonance (MR) Metabolic Imaging in Glioma

This review is focused on describing the use of magnetic resonance (MR) spectroscopy for metabolic imaging of brain tumors. We will first review the MR metabolic imaging findings generated from preclinical models, focusing primarily on in vivo studies, and will then describe the use of metabolic imaging in the clinical setting. We will address relatively well-established (1) H MRS approaches, as well as (31) P MRS, (13) C MRS and emerging hyperpolarized (13) C MRS methodologies, and will describe the use of metabolic imaging for understanding the basic biology of glioma as well as for improving the characterization and monitoring of brain tumors in the clinic.

Voxel-based evidence of perfusion normalization in glioblastoma patients included in a phase I-II trial of radiotherapy/tipifarnib combination

We previously showed that the farnesyl transferase inihibitor, Tipifarnib induced vascularization normalization, oxygenation and radiosensitization in a pre-clinical glioblastoma (GBM) model. The aim of this study was to assess by dynamic-susceptibility-contrast MRI (DSC-MRI) the effect of radiotherapy (RT) and Tipifarnib combination on tumor perfusion in GBM patients. Eighteen patients with newly diagnosed GBM, enrolled in a phase I-II clinical trial associating RT with Tipifarnib, underwent anatomical MR imaging and DSC-MRI before (M0) and two months after treatment (M2). Anatomic volumes of interest (VOIs) were delineated according to contrast-enhanced and hyper-intense signal areas on T1-Gd and T2 images, respectively. Perfusion variations between M0 and M2 were assessed with median relative cerebral blood volume (rCBV) inside these VOIs. Another voxel by voxel analysis of CBV values classified 405,117 tumor voxels into High_, Normal_ and Low_CBVTUMOR according to the distribution of CBV in the contralateral normal tissue. These three categories of CBVTUMOR voxels were color-coded over anatomical MRI. Variations of median rCBV were significantly different for two groups of patients (P < 0.013): rCBV decreased when initial rCBV was ≥ 1.0 (Group_rCBV_M0 > 1) and rCBV increased when initial rCBV was < 1.0 (Group_rCBV_M0 < 1). Mapping of color-coded voxels provided additional spatial and quantitative information about tumor perfusion: Group_rCBV_M0 > 1 presented a significant decrease of High_CBVTUMOR volume (P = 0.015) simultaneously with a significant increase of Normal_CBVTUMOR volume (P = 0.009) after treatment. Group_rCBV_M0 < 1 presented a decrease of Low_CBVTUMOR volume with an increase of Normal_ and High_CBV TUMOR volume after treatment. Pre and post-treatment CBV measurements with DSC-MRI characterized tumor perfusion evolution in GBM patients treated with RT combined to Tipifarnib; showing variations in favour of tumor perfusion normalization in agreement with our pre-clinical results of vascular normalization.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

BACKGROUND

MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).

METHODS

One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients.

RESULTS

Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy.

CONCLUSIONS

By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

Modern brain tumor imaging

The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients.

Nuclear Overhauser Enhancement imaging of glioblastoma at 7 Tesla: region specific correlation with apparent diffusion coefficient and histology

OBJECTIVE

To explore the correlation between Nuclear Overhauser Enhancement (NOE)-mediated signals and tumor cellularity in glioblastoma utilizing the apparent diffusion coefficient (ADC) and cell density from histologic specimens. NOE is one type of chemical exchange saturation transfer (CEST) that originates from mobile macromolecules such as proteins and might be associated with tumor cellularity via altered protein synthesis in proliferating cells.

PATIENTS AND METHODS

For 15 patients with newly diagnosed glioblastoma, NOE-mediated CEST-contrast was acquired at 7 Tesla (asymmetric magnetization transfer ratio (MTRasym) at 3.3ppm, B1 = 0.7 μT). Contrast enhanced T1 (CE-T1), T2 and diffusion-weighted MRI (DWI) were acquired at 3 Tesla and coregistered. The T2 edema and the CE-T1 tumor were segmented. ADC and MTRasym values within both regions of interest were correlated voxelwise yielding the correlation coefficient rSpearman (rSp). In three patients who underwent stereotactic biopsy, cell density of 12 specimens per patient was correlated with corresponding MTRasym and ADC values of the biopsy site.

RESULTS

Eight of 15 patients showed a weak or moderate positive correlation of MTRasym and ADC within the T2 edema (0.16≤rSp≤0.53, p<0.05). Seven correlations were statistically insignificant (p>0.05, n = 4) or yielded rSp≈0 (p<0.05, n = 3). No trend towards a correlation between MTRasym and ADC was found in CE-T1 tumor (-0.31<rSp<0.28, p<0.05, n = 9; p>0.05, n = 6). The biopsy-analysis within CE-T1 tumor revealed a strong positive correlation between tumor cellularity and MTRasym values in two of the three patients (rSppatient3 = 0.69 and rSppatient15 = 0.87, p<0.05), while the correlation of ADC and cellularity was heterogeneous (rSppatient3 = 0.545 (p = 0.067), rSppatient4 = -0.021 (p = 0.948), rSppatient15 = -0.755 (p = 0.005)).

DISCUSSION

NOE-imaging is a new contrast promising insight into pathophysiologic processes in glioblastoma regarding cell density and protein content, setting itself apart from DWI. Future studies might be based on the assumption that NOE-mediated CEST visualizes cellularity more accurately than ADC, especially in the CE-T1 tumor region.

Evaluation of absolute and normalized apparent diffusion coefficient (ADC) values within the post-operative T2/FLAIR volume as adverse prognostic indicators in glioblastoma

To evaluate the association of normalized and absolute ADC metrics with progression free survival (PFS) and overall survival (OS) in patients treated for glioblastoma multiforme (GBM). Fifty-two patients with preradiotherapy diffusion weighted imaging treated with post-operative chemoradiation for GBM were evaluated. Region of interest analysis for ADC metrics including mean and minimum ADC value (ADCmean) and (ADCmin) was performed within the T2/FLAIR volume. Normalized (N)ADC values were generated relative to contralateral white matter. PFS and OS were analyzed relative to ADC parameters using a regression model. Kaplan-Meier and Cox proportional hazards analysis with respect to (N)ADCmean, and (N)ADCmin was performed. A (N)ADC threshold <1.3 within the T2/FLAIR volume was analyzed with respect to PFS and OS. Regression analysis indicated that normalized ADC values provide the strongest association with PFS and OS. Kaplan-Meier analysis revealed a non-significant trend toward inferior PFS and OS associated with (N)ADCmean <1.7, and a significant decrement to PFS and OS associated with (N)ADCmin <0.3. (N)ADCmin was a significant prognostic factor when taking into account age, performance status, and extent of resection. ADC thresholding analysis revealed that a retained volume of >0.45 cc per mL FLAIR volume was associated with a trend toward inferior PFS and OS. In the post-operative, pre-radiotherapy setting, the (N)ADCmin is the strongest predictor of outcomes in patients treated for GBM. ADC thresholding analysis indicates that a large volume of normalized ADC value <1.3 may be associated with adverse outcomes.

Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial

BACKGROUND

The study goal was to determine whether changes in relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) MRI are predictive of overall survival (OS) in patients with recurrent glioblastoma multiforme (GBM) when measured 2, 8, and 16 weeks after treatment initiation.

METHODS

Patients with recurrent GBM (37/123) enrolled in ACRIN 6677/RTOG 0625, a multicenter, randomized, phase II trial of bevacizumab with irinotecan or temozolomide, consented to DSC-MRI plus conventional MRI, 21 with DSC-MRI at baseline and at least 1 postbaseline scan. Contrast-enhancing regions of interest were determined semi-automatically using pre- and postcontrast T1-weighted images. Mean tumor rCBV normalized to white matter (nRCBV) and standardized rCBV (sRCBV) were determined for these regions of interest. The OS rates for patients with positive versus negative changes from baseline in nRCBV and sRCBV were compared using Wilcoxon rank-sum and Kaplan-Meier survival estimates with log-rank tests.

RESULTS

Patients surviving at least 1 year (OS-1) had significantly larger decreases in nRCBV at week 2 (P = .0451) and sRCBV at week 16 (P = .014). Receiver operating characteristic analysis found the percent changes of nRCBV and sRCBV at week 2 and sRCBV at week 16, but not rCBV data at week 8, to be good prognostic markers for OS-1. Patients with positive change from baseline rCBV had significantly shorter OS than those with negative change at both week 2 and week 16 (P = .0015 and P = .0067 for nRCBV and P = .0251 and P = .0004 for sRCBV, respectively).

CONCLUSIONS

Early decreases in rCBV are predictive of improved survival in patients with recurrent GBM treated with bevacizumab.

Association of overall survival in patients with newly diagnosed glioblastoma with contrast-enhanced perfusion MRI: Comparison of intraindividually matched T1 - and T2 (*) -based bolus techniques

BACKGROUND

To compare intraindividual dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MR perfusion parameters and determine the association of DCE parameters with overall survival (OS) with the established predictive DSC parameter cerebral blood volume (CBV) in patients with newly diagnosed glioblastoma.

METHODS

Perfusion data were analyzed retrospectively, and included scans performed preoperatively at 3.0 Tesla in 37 patients (25 males, 12 females, 39-83 years, median 65) later diagnosed with glioblastoma. All patients received standard treatment consisting of surgery and radiochemotherapy. Images were spatially coregistered and maximum region of interest-based DCE and DSC parameter measurements compared and thresholds identified using multivariate linear regression, Pearson's correlation coefficients and using receiver operating characteristic analysis. Survival analysis was performed using Kaplan-Meier curves.

RESULTS

While both, elevated volume transfer constant (K(trans) ) (>0.29 min(-1) ; P = 0.041) and CBV (>23.7 mL/100 mL; P < 0.001) were significantly associated with OS, elevated CBV was associated with worse OS compared with elevated K(trans) . K(trans) was significantly correlated with the leakage correction factor K2 but not with CBV.

CONCLUSION

The combined use of DSC and DCE MR perfusion may provide additional information of prognostic value for glioblastoma patient survival prediction. As K(trans) was not tightly coupled to CBV, both parameters may reflect different stages in the pathogenetic sequence of glioblastoma growth.