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Purpose: Radio(chemo)therapy is used as a standard treatment for glioma patients. The surrounding normal tissue is inevitably affected by the irradiation. The aim of this longitudinal study was to investigate perfusion alterations in the normal-appearing tissue after proton irradiation and assess the dose sensitivity of the normal tissue perfusion. Methods: In 14 glioma patients, a sub-cohort of a prospective clinical trial (NCT02824731), perfusion changes in normal-appearing white matter (WM), grey matter (GM) and subcortical GM structures, i.e. caudate nucleus, hippocampus, amygdala, putamen, pallidum and thalamus, were evaluated before treatment and at three-monthly intervals after proton beam irradiation. The relative cerebral blood volume (rCBV) was assessed with dynamic susceptibility contrast MRI and analysed as the percentage ratio between follow-up and baseline image (ΔrCBV). Radiation-induced alterations were evaluated using Wilcoxon signed rank test. Dose and time correlations were investigated with univariate and multivariate linear regression models. Results: No significant ΔrCBV changes were found in any normal-appearing WM and GM region after proton beam irradiation. A positive correlation with radiation dose was observed in the multivariate regression model applied to the combined ΔrCBV values of low (1–20 Gy), intermediate (21–40 Gy) and high (41–60 Gy) dose regions of GM (p < 0.001), while no time dependency was detected in any normal-appearing area. Conclusion: The perfusion in normal-appearing brain tissue remained unaltered after proton beam therapy. In further studies, a direct comparison with changes after photon therapy is recommended to confirm the different effect of proton therapy on the normal-appearing tissue.

Purpose Currently, there is an intense debate on variations in intra-cerebral radiosensitivity and relative biological effectiveness (RBE) in proton therapy of primary brain tumours. Here, both effects were retrospectively investigated using late radiation-induced brain injuries (RIBI) observed in follow-up after proton therapy of patients with diagnosed glioma. Methods In total, 42 WHO grade 2–3 glioma patients out of a consecutive patient cohort having received (adjuvant) proton radio(chemo)therapy between 2014 and 2017 were eligible for analysis. RIBI lesions (symptomatic or clinically asymptomatic) were diagnosed and delineated on contrast-enhanced T1-weighted magnetic resonance imaging scans obtained in the first two years of follow-up. Correlation of RIBI location and occurrence with dose (D), proton dose-averaged linear energy transfer (LET) and variable RBE dose parameters were tested in voxel- and in patient-wise logistic regression analyses. Additionally, anatomical and clinical parameters were considered. Model performance was estimated through cross-validated area-under-the-curve (AUC) values. Results In total, 64 RIBI lesions were diagnosed in 21 patients. The median time between start of proton radio(chemo)therapy and RIBI appearance was 10.2 months. Median distances of the RIBI volume centres to the cerebral ventricles and to the clinical target volume border were 2.1 mm and 1.3 mm, respectively. In voxel-wise regression, the multivariable model with D, D × LET and periventricular region (PVR) revealed the highest AUC of 0.90 (95 % confidence interval: 0.89–0.91) while the corresponding model without D × LET revealed a value of 0.84 (0.83–0.86). In patient-level analysis, the equivalent uniform dose (EUD11, a = 11) in the PVR using a variable RBE was the most prominent predictor for RIBI with an AUC of 0.63 (0.32–0.90). Conclusions In this glioma cohort, an increased radiosensitivity within the PVR was observed as well as a spatial correlation of RIBI with an increased RBE. Both need to be considered when delivering radio(chemo)therapy using proton beams.

Objective Brain atrophy has the potential to become a biomarker for severity of radiation-induced side-effects. Particularly brain tumour patients can show great MRI signal changes over time caused by e.g. oedema, tumour progress or necrosis. The goal of this study was to investigate if such changes affect the segmentation accuracy of normal appearing brain and thus influence longitudinal volumetric measurements. Materials and methods T1-weighted MR images of 52 glioblastoma patients with unilateral tumours acquired before and three months after the end of radio(chemo)therapy were analysed. GM and WM volumes in the contralateral hemisphere were compared between segmenting the whole brain (full) and the contralateral hemisphere only (cl) with SPM and FSL. Relative GM and WM volumes were compared using paired t tests and correlated with the corresponding mean dose in GM and WM, respectively. Results Mean GM atrophy was significantly higher for full segmentation compared to cl segmentation when using SPM (mean ± std: ΔVGM,full = − 3.1% ± 3.7%, ΔVGM,cl = − 1.6% ± 2.7%; p d = 0.62). GM atrophy was significantly correlated with the mean GM dose with the SPM cl segmentation (r = − 0.4, p = 0.004), FSL full segmentation (r = − 0.4, p = 0.004) and FSL cl segmentation (r = -0.35, p = 0.012) but not with the SPM full segmentation (r = − 0.23, p = 0.1). Conclusions For accurate normal tissue volume measurements in brain tumour patients using SPM, abnormal tissue needs to be masked prior to segmentation, however, this is not necessary when using FSL.

Background: Radiotherapy in patients with primary brain tumors may affect hippocampal structure and cause dyscognitive side-effects. Patients and methods: Using structural MRI and comprehensive neurocognitive evaluation, we investi- gated associations between hippocampal structure and memory deficits in 15 patients with WHO grade 3 and grade 4 gliomas receiving standard radio(chemo)therapy. Results: We did not find changes in hippocampal thickness or cognitive abilities three months after com- pleting radiotherapy. However, subjective memory impairment was associated with symptoms of depression, but not with objective memory performance, cortical thickness of the hippocampus or radi- ation dose. Conclusions: Irrespective of whether there is a bidirectional relationship between affective changes and subjective cognitive dysfunction in these patients, depressive symptoms remain a target for intervention to improve their quality of life. The results of our pilot study highlight that future assessment of side effects of radiotherapy concerning memory should include assessments of depressive symptoms.

Simple Summary Radiotherapy is one of the most important treatment options against cancer. Irradiation of cancerous tissue either directly destroys the cancer cells or damages them such that they cannot reproduce. One side-effect of radiotherapy is that tumor-surrounding normal tissue is inevitably also irradiated, albeit at a lower dose. The resulting long-term damage can significantly affect cognitive performance and quality of life. Many studies investigated the effect of irradiation on normal-appearing brain tissues and some of these correlated imaging findings with functional outcome. This article provides an overview of the examination of radiation-induced injuries using conventional and enhanced MRI methods and summarizes conclusions about the underlying tissue changes. Radiation-induced morphologic, microstructural, vascular, and metabolic tissue changes have been observed, in which the effect of irradiation was evident in terms of decreased perfusion and neuronal health as well as increased diffusion and atrophy. Abstract Radiotherapy is part of the standard treatment of most primary brain tumors. Large clinical target volumes and physical characteristics of photon beams inevitably lead to irradiation of surrounding normal brain tissue. This can cause radiation-induced brain injury. In particular, late brain injury, such as cognitive dysfunction, is often irreversible and progressive over time, resulting in a significant reduction in quality of life. Since 50% of patients have survival times greater than six months, radiation-induced side effects become more relevant and need to be balanced against radiation treatment given with curative intent. To develop adequate treatment and prevention strategies, the underlying cause of radiation-induced side-effects needs to be understood. This paper provides an overview of radiation-induced changes observed in normal-appearing brains measured with conventional and advanced MRI techniques and summarizes the current findings and conclusions. Brain atrophy was observed with anatomical MRI. Changes in tissue microstructure were seen on diffusion imaging. Vascular changes were examined with perfusion-weighted imaging and susceptibility-weighted imaging. MR spectroscopy revealed decreasing N-acetyl aspartate, indicating decreased neuronal health or neuronal loss. Based on these findings, multicenter prospective studies incorporating advanced MR techniques as well as neurocognitive function tests should be designed in order to gain more evidence on radiation-induced sequelae.

Background and purpose: Radiotherapy is a standard treatment option for high-grade gliomas. Cognitive impairment is a side effect associated with radiotherapy particularly in long-term survivors. Recent findings suggest involvement of the cerebellum in cognitive function. The goal of this study was therefore to investigate dose dependent cerebellar atrophy using prospective, longitudinal MR data from adult glioma patients who received radiotherapy. Materials and methods: Cerebellar volumes were measured using T1-weighted MR images from 91 glioma patients before radiotherapy and every three months thereafter. We calculated the average cerebellar volume change per year per Gy, based on the mean cerebellar dose, using linear regression analysis. Subsequently, patient age was investigated as a confounding factor using multiple linear regression analysis. The impact of chemotherapy was assessed separately in a subgroup of patients receiving a cerebellar dose ≤ 1 Gy. Cerebellar mean dose and cerebellar volume changes were compared between patients treated with proton (N = 38) and photon therapy (N = 52). Results: Cerebellar volume decreased 2.4 % per 10 Gy per year (p < 0.001). The cerebellar volume loss was progressive over time without signs of recovery within the observational period of two years. Neither patient age (p = 0.27) nor chemotherapy (p = 0.43) had a significant impact on cerebellar atrophy. Compared to patients treated with photons, the cerebellar dose was significantly lower in patients treated with proton therapy (p < 0.001, r = 0.62) which also translated to a significantly lower cerebellar volume reduction per year (p = 0.016, r = 0.25). Conclusion: Cerebellar volume decreased significantly and irreversibly after radiotherapy as function of time and dose. Further work is now needed to correlate these results with cognitive function and motor performance.

BACKGROUND AND PURPOSE: Glutathione is an important antioxidant in the human brain and therefore of interest in neurodegenerative disorders. The purpose of this study was to investigate the feasibility of measuring glutathione in healthy nonsedated children by using the 1H Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) sequence at 3T and to compare glutathione levels between the medial parietal gray matter and the cerebellum. MATERIALS AND METHODS: Glutathione was measured using MEGA-PRESS MRS (TR = 1.8 seconds, TE = 131 ms) in the parietal gray matter (35 × 25 × 20 mm3) of 6 healthy children (10.0 ± 2.4 years of age; range, 7–14 years; 3 males) and in the cerebellum of 11 healthy children (12.0 ± 2.7 years of age; range, 7–16 years; 6 males). A postprocessing pipeline was developed to account for frequency and phase variations in the edited ON and nonedited OFF spectra. Metabolites were quantified with LCModel and reported both as ratios and water-scaled values. Glutathione was quantified in the ON-OFF spectra, whereas total NAA, total Cho, total Cr, mIns, Glx, and taurine were quantified in the OFF spectra. RESULTS: We found significantly higher glutathione, total Cho, total Cr, mIns, and taurine in the cerebellum (P CONCLUSIONS: We demonstrated that glutathione measurement in nonsedated children is feasible. We found significantly higher glutathione in the cerebellum compared with the parietal gray matter. Metabolite differences between the parietal gray matter and cerebellum agree with published MRS data in adults.

Highlights • Linear decline in cerebellar volume in people with classical A-T across childhood. • Divergent volume trajectories in children with and without A-T in the first decade. • Alterations in metabolites seen in childhood A-T independent of age and volume. • Fractional fourth ventricular volume predicts neurological status in childhood A-T., Background Ataxia Telangiectasia (A-T) is an inherited multisystem disorder with cerebellar neurodegeneration. The relationships between imaging metrics of cerebellar health and neurological function across childhood in A-T are unknown, but may be important for determining timing and impact of therapeutic interventions. Purpose To test the hypothesis that abnormalities of cerebellar structure, physiology and cellular health occur in childhood A-T and correlate with neurological disability, we performed multiparametric cerebellar MRI and establish associations with disease status in childhood A-T. Methods Prospective cross-sectional observational study. 22 young people (9 females / 13 males, age 6.6–17.8 years) with A-T and 24 matched healthy controls underwent 3-Tesla MRI with volumetric, diffusion and proton spectroscopic acquisitions. Participants with A-T underwent structured neurological assessment, and expression / activity of ataxia-telangiectasia mutated (ATM) kinase were recorded. Results Ataxia-telangiectasia participants had cerebellar volume loss (fractional total cerebellar volume: 5.3% vs 8.7%, P

Background and purpose Standard treatment of high grade gliomas includes gross tumour resection followed by radio(chemo)therapy. Radiotherapy inevitably leads to irradiation of normal brain tissue. The goal of this prospective, longitudinal study was to use MRI to quantify normal appearing white and grey matter changes following radiation treatment as a function of dose and time after radiotherapy. Materials and methods Pre-radiotherapy (proton or photon therapy) MRI and follow-up MRIs collected in 3 monthly intervals thereafter were analysed for 22 glioma patients and included diffusion tensor imaging, quantitative T1, T2* and proton density mapping. Abnormal tissue was excluded from analysis. MR signal changes were quantified within different dose bin regions for grey and white matter and subsequently for whole brain white matter. Results We found significant reductions in mean diffusivity, radial diffusivity, axial diffusivity and T2* in normal appearing white matter regions receiving a radiation dose as low as 10–20 Gy within the observational period of up to 18 months. The magnitude of these changes increased with the received radiation dose and progressed with time after radiotherapy. Whole brain white matter also showed a significant reduction in radial diffusivity as a function of radiation dose and time after radiotherapy. No significant changes were observed in grey matter. Conclusion Diffusion tensor imaging and T2* imaging revealed normal appearing white matter changes following radiation treatment. The changes were dose dependant and progressed over time. Further work is needed to understand the underlying tissue changes and to correlate the observed diffusion changes with late brain malfunctions.

Introduction: Proton radiation therapy is an effective treatment for glioma patients. To exploit its full potential, a better description of regional differences in radiation response within the brain is required. In this study, we present a model for predicting magnetic resonance (MR) image changes in glioma patients that considers the variability in proton relative biological effectiveness (RBE) as well as the regional susceptibility of brain tissue to radiation damage. Material and methods: Six glioma patients treated with adjuvant proton radio(chemo)therapy showed contrast enhancement on follow-up T1-weighted MR imaging corresponding to treatment-related changes. Physical dose and linear energy transfer (LET) were obtained from high-precision Monte-Carlo simulations. The periventricular region (PVR) was contoured as a 4 mm expansion around the ventricles. Correlations between the image changes and dose, LET, and the PVR were evaluated voxel-wise in univariable and multivariable logistic regression analyses. Model performance was assessed by the area under the curve (AUC) using leave-one-out cross validation. The tolerance dose TD50, at which 50% of the tissue voxels show toxicity was interpolated from the model and used for RBE estimation. Results: The spatial distribution of MR image changes in the brain was highly non-uniform and correlated poorly with the considered predictors in univariable analysis. Multivariable modelling with either only dose and LET or PVR as third predictor revealed AUC values of 0.89 and 0.92, respectively. TD50 decreased with increasing LET and the modelled RBE was found to vary between 0.9 and 2.3. No relevant difference in model performance was found for track- or dose-averaged LET. Conclusion: The strong correlation of MR image changes with dose, LET, and the PVR demonstrates the relevance of non-uniform dose response models. A larger patient cohort including patients without image changes will be used to validate the clinically observed indication of a variable proton RBE.

In glioma patients, linac-based photon beam irradiation is a widely applied therapy, which achieves highly conformal target volume coverage, but is also known to cause side-effects to adjacent areas of healthy tissue. Apart from subjective measures, such as quality of life assessment and neurocognitive function tests, objective methods to quantify tissue damage are needed to assess this impact. Magnetic resonance imaging (MRI) is a well-established method for brain tumor diagnoses as well as assessing treatment response. In this study, we retrospectively assessed volumetric changes of gray matter (GM) and white matter (WM) in glioma patients following photon irradiation using a heterogeneous MRI-dataset obtained in routine clinical practice at different sites with imaging parameters and magnetic field strengths. We found a significant reduction in WM volume at one year [Formula: see text] and two years [Formula: see text] post radio(chemo)therapy whereas corresponding GM volumes did not change significantly ([Formula: see text] and [Formula: see text], respectively). More importantly, we also found large variations in the segmented tissue volumes caused by the heterogeneous MR data, thus potentially masking more subtle tissue changes over time. On the basis of these observations, we present suggestions regarding data acquisitions in future prospective MR studies to assess such volumetric changes.

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis. Magn Reson Med 74:868–878, 2015. © 2014 Wiley Periodicals, Inc.

(1) H MRSI has demonstrated the ability to characterise and delineate brain tumours, but robust data analysis methods are still needed. In this study, we present an objective analysis method for MRSI data to delineate tumour abnormality regions. The presented method is a development of the choline-to-N-acetylaspartate index (CNI), which uses perpendicular distances in a choline versus N-acetylaspartate plot as a measure of abnormality. We propose a radial CNI (rCNI) method that uses the choline to N-acetylaspartate ratio directly as an abnormality measure. To avoid problems with small or zero denominators, we perform an arctangent transformation. CNI abnormality contours were evaluated using a z-score threshold of 2 (CNI2) and 2.5 (CNI2.5) and compared with rCNI2. Simulations modelling low-grade (LGG) and high-grade (HGG) gliomas with different tissue compartments and partial volume effects suggest improved specificity of rCNI2 (LGG 92%/HGG 91%) over CNI2 (LGG 69%/HGG 69%) and CNI2.5 (LGG 74%/HGG 75%), whilst retaining a similar sensitivity to both CNI2 and CNI2.5. Our simulation results also confirm a previously reported increase in specificity of CNI2.5 over CNI2 with little penalty in sensitivity. The analysis of MRSI data acquired from 10 patients with low-grade glioma at 3 T suggests a more robust delineation of the lesions using rCNI with respect to conventional imaging compared with standard CNI. Further analysis of 29 glioma datasets acquired at 1.5 T, together with previously published estimated tumour proportions, suggests that rCNI has higher sensitivity and specificity for the identification of abnormal MRSI voxels.

Purpose To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation. Methods Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent. Results Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging. Conclusion LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade. Magn Reson Med 73:1381–1389, 2015. © 2014 Wiley Periodicals, Inc.

In this study, mean tumor spectra are used as the basis functions in LCModel to create a direct classification tool for short echo time (1)H magnetic resonance spectroscopy of pediatric brain tumors. LCModel is a widely used analysis tool designed to fit a linear combination of individual metabolite spectra to in vivo spectra. Here, we have used LCModel to fit mean spectra and corresponding variability components of childhood cerebellar tumors, as calculated using principal component analysis, and assessed for classification accuracy. Classification was performed according to the highest estimated tumor proportion. This method was tested in a leave-one-out analysis discriminating between pediatric brain tumor spectra of medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma. Additionally, the effect of accepting different Cramér-Rao Lower Bound cut-off criteria on classification accuracy and estimated tissue proportions was investigated. The best classification results differentiating medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma were 100 and 87.7%, respectively. These results are comparable to a specialized pattern recognition analysis of this data set and give easy to interpret results in the form of estimated tissue proportions. The method requires minimal user input and is easily transferable across sites and to other magnetic resonance spectroscopy classification problems.

This study presents a novel method for the direct classification of 1H single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS. Copyright © 2011 John Wiley & Sons, Ltd.

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method.In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data.An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC.The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis.

Background This study aims to investigate the role of the mid-anterior cingulate cortex γ-aminobutyric acid levels in chronic nociceptive pain. The molecular mechanisms of pain chronification are not well understood. In fibromyalgia, low mid-anterior cingulate cortex γ-aminobutyric acid was associated with high pain suggesting a role of prefrontal disinhibition. We hypothesize that mid-anterior cingulate cortex GABAergic disinhibition may underpin chronic pain independent of the pain etiology and comorbid negative affect. Proton magnetic resonance spectra were acquired at 3T from the mid-anterior cingulate cortex in 20 patients with chronic painful knee osteoarthritis, and 19 healthy pain-free individuals using a point resolved spectroscopy sequence optimized for detection of γ-aminobutyric acid. Participants underwent questionnaires for negative affect (depression and anxiety) and psychophysical pain phenotyping. Results No differences in mid-anterior cingulate cortex γ-aminobutyric acid or other metabolite levels were detected between groups. Ratings of perceived intensity of ongoing osteoarthritis pain were inversely correlated with γ-aminobutyric acid (r = −0.758, p