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Patients with brain metastases, the most common intracranial tumor, have an average survival ranging from a few months to 40 months, and new treatment initiatives are needed. Cryoablation is a minimally invasive, well-tolerated, and effective procedure commonly applied for treatment of renal tumors and certain other malignancies. We aimed to examine the clinical usefulness of this procedure in a step-by-step program starting with cerebral cryoablation in healthy pigs. In four terminal and four non-terminal non-tumor bearing pigs, we studied immediate and delayed effects of cerebral cryoablation. Safety was assessed by computed tomography (CT), and clinical observation of behavior, neurological deficits, and wellbeing. Effects were assessed by histological and immuno-histochemical analyses addressing structural and metabolic changes supported by additional magnetic resonance imaging (MRI) and positron emission tomography (PET) in the non-terminal animals. Using CT-guidance, cryoablation probes were successfully inserted without complications, and ice formation could be monitored real-time with CT. No animal developed neurological deficits or signs of discomfort. Histological and immunohistochemical analyses, MRI, and PET revealed profound structural and biological damage within the lesion. MRI and PET revealed no long-term damage to healthy tissue outside the cryoablation zone. Cerebral cryoablation appears to be a feasible, safe, and controllable procedure that can be monitored successfully with CT. The net effect is a dead brain lesion without damage of either nearby or remote healthy structures. Immediate changes are local hemorrhage and edema; delayed effects are perfusion defects, immune system activation, and astrogliosis.

OBJECTIVES: Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data.MATERIALS AND METHODS: C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison.RESULTS: C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class.CONCLUSIONS: These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.

Glioblastoma (GBM) is the most aggressive brain tumor type in adults. GBM is heterogeneous, with a compact core lesion surrounded by an invasive tumor front. This front is highly relevant for tumor recurrence but is generally non-detectable using standard imaging techniques. Recent studies demonstrated distinct metabolic profiles of the invasive phenotype in GBM. Magnetic resonance (MR) of hyperpolarized 13C-labeled probes is a rapidly advancing field that provides real-time metabolic information. Here, we applied hyperpolarized 13C-glucose MR to mouse GBM models. Compared to controls, the amount of lactate produced from hyperpolarized glucose was higher in the compact GBM model, consistent with the accepted “Warburg effect”. However, the opposite response was observed in models reflecting the invasive zone, with less lactate produced than in controls, implying a reduction in aerobic glycolysis. These striking differences could be used to map the metabolic heterogeneity in GBM and to visualize the infiltrative front of GBM.

Direct electrical stimulation mapping was used to map executive functions during awake surgery of a patient with a right frontal low-grade glioma. We specifically targeted the frontal aslant tract, as this pathway had been infiltrated by the tumor. The right frontal aslant tract has been implicated in executive functions in the neuroscientific literature, but is yet of unknown relevance for clinical practice. Guided by tractography, electrical stimulation of the frontal aslant tract disrupted working memory and inhibitory functions. In this report we illustrate the dilemmas that neurosurgeons face when balancing maximal tumor resection against optimal cognitive performance. In particular, we emphasize that intraoperative tasks that target cognitive functions should be carefully introduced in clinical practice to prevent clinically irrelevant responses and too early termination of the resection. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-020-00439-8.

BACKGROUND: For decades many patients with small cell lung cancer (SCLC) have been offered prophylactic cranial irradiation (PCI) to prevent brain metastases (BM). However, the role of PCI is debated in the modern era of increased brain magnetic resonance imaging (MRI) availability. BM in SCLC patients may respond to chemotherapy, and if a negative MRI is used in the decision to use of PCI in the treatment strategy, the timing of brain MRI may be crucial when evaluating the effect of PCI. This retrospective study investigates the impact of PCI outcomes in patients with SCLC staged with brain MRI prior to chemotherapy.MATERIALS AND METHODS: This study included 245 patients diagnosed SCLC/mixed NSCLC-SCLC treated between 2012 and 2019. The population was analyzed separately for limited disease (LS-SCLC) and extensive disease (ES-SCLC). Patients were divided into groups based on baseline brain MRI prior to chemotherapy and PCI. The primary endpoint was time to symptomatic BM. Secondary endpoints were overall survival (OS), and progression-free survival (PFS).RESULTS: In patients with LS-SCLC staged with brain MRI the probability of developing symptomatic BM at one year was 4% vs. 22% (p CONCLUSION: This study found that PCI in LS-SCLC patients staged with brain MRI had lower incidence of symptomatic BM and improved survival outcomes suggesting PCI as standard of care. Similar benefit of PCI in patients with ES-SCLC was not found.

Publicly available Glioblastoma (GBM) datasets predominantly include pre-operative Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient. Access to fully longitudinal datasets is critical to advance the refinement of treatment response assessment. We release a single-center longitudinal GBM MRI dataset with expert ratings of selected follow-up studies according to the response assessment in neuro-oncology criteria (RANO). The expert rating includes details about the rationale of the ratings. For a subset of patients, we provide pathology information regarding methylation of the O 6 -methylguanine-DNA methyltransferase (MGMT) promoter status and isocitrate dehydrogenase 1 (IDH1), as well as the overall survival time. The data includes T1-weighted pre- and post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI. Segmentations from state-of-the-art automated segmentation tools, as well as radiomic features, complement the data. Possible applications of this dataset are radiomics research, the development and validation of automated segmentation methods, and studies on response assessment. This collection includes MRI data of 91 GBM patients with a total of 638 study dates and 2487 images.

The invasive behavior of glioblastoma, the most aggressive primary brain tumor, is considered highly relevant for tumor recurrence. However, the invasion zone is difficult to visualize by Magnetic Resonance Imaging (MRI) and is protected by the blood brain barrier, posing a particular challenge for treatment. We report biological features of invasive growth accompanying tumor progression and invasion based on associated metabolic and transcriptomic changes observed in patient derived orthotopic xenografts (PDOX) in the mouse and the corresponding patients’ tumors. The evolution of metabolic changes, followed in vivo longitudinally by 1H-Magnetic Resonance Spectroscopy (MRS) at ultra-high field, reflected growth and the invasive properties of the human glioblastoma transplanted into the brains of mice (PDOX). Comparison of MRS derived metabolite signatures, reflecting temporal changes of tumor development and invasion in PDOX, revealed high similarity to spatial metabolite signatures of combined multi-voxel MRS analyses sampled across different areas of the patients’ tumors. Pathway analyses of the transcriptome associated with the metabolite profiles of the PDOX, identified molecular signatures of invasion, comprising extracellular matrix degradation and reorganization, growth factor binding, and vascular remodeling. Specific analysis of expression signatures from the invaded mouse brain, revealed extent of invasion dependent induction of immune response, recapitulating respective signatures observed in glioblastoma. Integrating metabolic profiles and gene expression of highly invasive PDOX provided insights into progression and invasion associated mechanisms of extracellular matrix remodeling that is essential for cell-cell communication and regulation of cellular processes. Structural changes and biochemical properties of the extracellular matrix are of importance for the biological behavior of tumors and may be druggable. Ultra-high field MRS reveals to be suitable for in vivo monitoring of progression in the non-enhancing infiltration zone of glioblastoma.

MR-guided laser interstitial thermal therapy (LITT) is a minimally invasive neurosurgical procedure, which in the last decade has gained significant momentum of the treatment of intracranial tumours and epileptic foci. In brief, LITT utilises the heat from a stereotactically placed laser catheter to selectively ablate a lesion or a structure under real-time MRI guidance, which is summarised and discussed in this review. The first LITT system gained FDA approval in 2007 and was CE-marked in 2018. In December 2020, the first patient with recurrent glioblastoma was treated at the Department of Neurosurgery at Rigshospitalet, Copenhagen.

Purpose This pilot study aimed to evaluate the amino acid tracer 18F-FACBC with simultaneous PET/MRI in diagnostic assessment and neurosurgery of gliomas. Materials and Methods Eleven patients with suspected primary or recurrent low- or high-grade glioma received an 18F-FACBC PET/MRI examination before surgery. PET and MRI were used for diagnostic assessment, and for guiding tumor resection and histopathological tissue sampling. PET uptake, tumor-to-background ratios (TBRs), time-activity curves, as well as PET and MRI tumor volumes were evaluated. The sensitivities of lesion detection and to detect glioma tissue were calculated for PET, MRI, and combined PET/MRI with histopathology (biopsies for final diagnosis and additional image-localized biopsies) as reference. Results Overall sensitivity for lesion detection was 54.5% (95% confidence interval [CI], 23.4–83.3) for PET, 45.5% (95% CI, 16.7–76.6) for contrast-enhanced MRI (MRICE), and 100% (95% CI, 71.5–100.0) for combined PET/MRI, with a significant difference between MRICE and combined PET/MRI (P = 0.031). TBRs increased with tumor grade (P = 0.004) and were stable from 10 minutes post injection. PET tumor volumes enclosed most of the MRICE volumes (>98%) and were generally larger (1.5–2.8 times) than the MRICE volumes. Based on image-localized biopsies, combined PET/MRI demonstrated higher concurrence with malignant findings at histopathology (89.5%) than MRICE (26.3%). Conclusions Low- versus high-grade glioma differentiation may be possible with 18F-FACBC using TBR. 18F-FACBC PET/MRI outperformed MRICE in lesion detection and in detection of glioma tissue. More research is required to evaluate 18F-FACBC properties, especially in grade II and III tumors, and for different subtypes of gliomas. © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal

The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D&ndash, 2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D&ndash, 2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.

Objective Intravoxel incoherent motion (IVIM) is an MRI technique with potential applications in measuring brain tumor perfusion, but its clinical impact remains to be determined. We assessed the usefulness of IVIM-metrics in predicting survival in newly diagnosed glioblastoma. Methods Fifteen patients with glioblastoma underwent MRI including spin-echo echo-planar DWI using 13 b-values ranging from 0 to 1000 s/mm(2). Parametric maps for diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were generated for contrast-enhancing regions (CER) and non-enhancing regions (NCER). Regions of interest were manually drawn in regions of maximum f and on the corresponding dynamic susceptibility contrast images. Prognostic factors were evaluated by Kaplan-Meier survival and Cox proportional hazards analyses. Results We found that f(CER) and D*(CER) correlated with rCBF(CER). The best cutoffs for 6-month survival were f(CER)>9.86% and D*(CER)>21.712 x 10(-3)mm(2)/s (100% sensitivity, 71.4% specificity, 100% and 80% positive predictive values, and 80% and 100% negative predictive values; AUC: 0.893 and 0.857, respectively). Treatment yielded the highest hazard ratio (5.484; 95% CI: 1.162-25.88; AUC: 0.723; P = 0.031); f(CER) combined with treatment predicted survival with 100% accuracy. Conclusions The IVIM-metrics f(CER) and D*(CER) are promising biomarkers of 6-month survival in newly diagnosed glioblastoma.

Relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC)-MRI is a valuable biomarker in patients with glioblastoma for assessing treatment response and predicting overall survival. DSC-MRI based on echo planar images (EPI) may possess severe geometric distortions from magnetic field inhomogeneities up to the order of centimeters. The aim of this study is to assess how much two readily available EPI-based geometric distortion correction methods, FSL TOPUP and EPIC, affect rCBV values from DSC-MRI in patients with confirmed glioblastoma.We used a combined single-shot 2D gradient-echo (T2*), spin-echo (T2) EPI sequence to estimate both T2* and T2-weighted rCBV from the same contrast agent injection. Effects of distortion correction on the positive phase-encoded T2- and T2*-images were assessed in healthy anatomical brain regions in terms of Wilcoxon signed rank tests on median rCBV change and on Dice coefficients, as well as in tumor lesions in terms of Wilcoxon signed rank tests on median rCBV change.Our results show that following distortion correction, both gradient-echo and spin-echo rCBV increased in cortical areas of the frontal, temporal and occipital lobe, including the posterior orbital gyri in the frontal lobe and middle frontal gyri (p 0.0008). Similar, improved Dice coefficients were observed for gradient-echo EPI in temporal, occipital and frontal lobe. Only spin-echo rCBV in enhancing lesion increased with correction (p = 0.0002).Our study sheds light on the importance of performing geometric distortion correction on EPI-based MRI data before assessing functional information such as rCBV values. Our findings may indicate that uncorrected rCBV values can be underestimated from positive phase-encoding EPI and that geometric distortion correction is warranted when comparing EPI-based data to conventional MRI.

Background: Radiolabeled prostate-specific membrane antigen PSMA-based PET/CT or PET/MRI is a whole-body imaging technique currently performed for the detection of prostate cancer lesions. PSMA has been also demonstrated to be expressed by the neovasculature of many other solid tumors. Objective: The aim of this review is to evaluate the possible diagnostic role of radiolabeled PSMA PET/CT or PET/MRI in patients with gliomas and glioblastomas, by summarizing the available literature data. Methods: A comprehensive literature search of the PubMed/MEDLINE, Scopus, Embase and Cochrane library databases was conducted to find relevant published articles about the diagnostic performance of radiolabeled PSMA binding agents in PET/CT or PET/MRI imaging of patients with suspected gliomas or glioblastomas. Results: Seven case reports or case series and 3 studies enrolling more than 10 patients showed that gliomas and glioblastoma are PSMA-avid tumors. Conclusion: Radiolabeled PSMA imaging seems to be useful in analyzing glioma/glioblastoma. Further studies enrolling a wider population are needed to clarify the real clinical and diagnostic role of radiolabeled PSMA in this setting and its possible position in the diagnostic flow-chart.

BACKGROUND: There is paucity of evidence regarding the optimal follow-up (FU) regimen for lung cancer. Consequently, FU is organized differently across countries. The Danish FU regimen has short FU intervals with a computed tomography (CT) scan of the chest and upper abdomen every three months in the early phase (first 2 years), then every six months in the late phase of FU (3rd, 5th year). Characterizing recurrences missed by the FU program in terms of site, tumor histology, department, and phase of FU, could improve the FU program.MATERIAL AND METHOD: A case-control study of curatively treated stage I lung cancer patients who attended the Danish FU-program and had recurrence identified through the follow-up program (controls, FU group) or outside FU program (cases, symptomatic group).RESULTS: Of 233 included patients with recurrence, the FU group constituted 85% (n = 197). Among the 15% (n = 36) in the symptomatic group, 53% had involvement of the central nervous system compared with 3% in the FU group. The unadjusted odds ratio (OR) for having an isolated brain recurrence (IBR) in the symptomatic group was 52.3 (95%CI: 15.1-181.4) as compared with the FU group. The OR for having a symptomatic recurrence in the early phase of FU was 2.5 (95%CI: 0.7-8.7) compared with the late phase.CONCLUSIONS: The FU program did not identify the majority of patients with IBR. Including cerebral imaging in the FU program may result in an earlier detection of brain metastases. These matters should be studied in a prospective setting.

The anticancer drug temozolomide is the only drug with proven activity against high-grade gliomas and has therefore become a part of the standard treatment of these tumors. P-glycoprotein (P-gp; ABCB1) and breast cancer resistance protein (BCRP; ABCG2) are transport proteins, which are present at the blood-brain barrier and limit the brain uptake of substrate drugs. We have studied the effect of P-gp and BCRP on the pharmacokinetics and pharmacodynamics of temozolomide, making use of a comprehensive set of in vitro transport experiments and in vivo pharmacokinetic and antitumor efficacy experiments using wild-type, Abcg2−/−, Abcb1a/b−/−, and Abcb1a/b;Abcg2−/− mice. We here show that the combined deletion of Abcb1a/b and Abcg2 increases the brain penetration of temozolomide by 1.5-fold compared to wild-type controls (P < .001) without changing the systemic drug exposure. Moreover, the same increase was achieved when temozolomide was given to wild-type mice in combination with the dual P-gp/BCRP inhibitor elacridar (GF120918). The antitumor efficacy of temozolomide against three different intracranial tumor models was significantly enhanced when Abcb1a/b and Abcg2 were genetically deficient or pharmacologically inhibited in recipient mice. These findings call for further clinical testing of temozolomide in combination with elacridar for the treatment of gliomas, as this offers the perspective of further improving the antitumor efficacy of this already active agent.

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11-22.

Several meta-analyses reporting data on the diagnostic performance or prognostic value of positron emission tomography (PET) with different tracers in detecting brain tumors have been published so far. This review article was written to summarize the evidence-based data in these settings. We have performed a comprehensive literature search of meta-analyses published in the Cochrane library and PubMed/Medline databases (from inception through July 2019) about the diagnostic performance or prognostic value of PET with different tracers in patients with brain tumors. We have summarized the results of 24 retrieved meta-analyses on the use of PET or PET/computed tomography (CT) with different tracers in brain tumors. The tracers included were: fluorine-18 fluorodeoxyglucose ( 18 F-FDG), carbon-11 methionine ( 11 C-methionine), fluorine-18 fluoroethyltyrosine ( 18 F-FET), fluorine-18 dihydroxyphenylalanine ( 18 F-FDOPA), fluorine-18 fluorothymidine ( 18 F-FLT), and carbon-11 choline ( 11 C-choline). Evidence-based data demonstrated good diagnostic performance of PET with different tracers in detecting brain tumors, in particular, radiolabelled amino acid tracers showed the highest diagnostic performance values. All the PET tracers evaluated had significant prognostic value in patients with glioma. Evidence-based data showed a good diagnostic performance for some PET tracers in specific indications and significant prognostic value in brain tumors.

Convection-enhanced delivery of rhenium-186 (186Re)-nanoliposomes is a promising approach to provide precise delivery of large localized doses of radiation for patients with recurrent glioblastoma multiforme. Current approaches for treatment planning utilizing convection-enhanced delivery are designed for small molecule drugs and not for larger particles such as 186Re-nanoliposomes. To enable the treatment planning for 186Re-nanoliposomes delivery, we have developed a computational fluid dynamics approach to predict the distribution of nanoliposomes for individual patients. In this work, we construct, calibrate, and validate a family of computational fluid dynamics models to predict the spatio-temporal distribution of 186Re-nanoliposomes within the brain, utilizing patient-specific pre-operative magnetic resonance imaging (MRI) to assign material properties for an advection-diffusion transport model. The model family is calibrated to single photon emission computed tomography (SPECT) images acquired during and after the infusion of 186Re-nanoliposomes for five patients enrolled in a Phase I/II trial (NCT Number NCT01906385), and is validated using a leave-one-out bootstrapping methodology for predicting the final distribution of the particles. After calibration, our models are capable of predicting the mid-delivery and final spatial distribution of 186Re-nanoliposomes with a Dice value of 0.69 ± 0.18 and a concordance correlation coefficient of 0.88 ± 0.12 (mean ± 95% confidence interval), using only the patient-specific, pre-operative MRI data, and calibrated model parameters from prior patients. These results demonstrate a proof-of-concept for a patient-specific modeling framework, which predicts the spatial distribution of nanoparticles. Further development of this approach could enable optimizing catheter placement for future studies employing convection-enhanced delivery.

Background Glioblastoma patients show a great variability in progression free survival (PFS) and overall survival (OS). To gain additional pretherapeutic information, we explored the potential of O-(2-18F-fluoroethyl)-L-tyrosine (FET) PET as an independent prognostic biomarker. Methods We retrospectively analyzed 146 consecutively treated, newly diagnosed glioblastoma patients. All patients were treated with temozolomide and radiation therapy (RT). CT/MR and FET PET scans were obtained postoperatively for RT planning. We used Cox proportional hazards models with OS and PFS as endpoints, to test the prognostic value of FET PET biological tumor volume (BTV). Results Median follow-up time was 14 months, and median OS and PFS were 16.5 and 6.5 months, respectively. In the multivariate analysis, increasing BTV (HR = 1.17, P

Introduction Pineal cysts are a common incidental finding on brain MRI with resulting difficulties in differentiation between normal glands and pineal pathologies. The aim of this study was to assess the size and morphology of the cystic pineal gland in children (0–5 years) and compare the findings with published pineoblastoma cases. Methods In this retrospective multicenter study, 257 MR examinations (232 children, 0–5 years) were evaluated regarding pineal gland size (width, height, planimetric area, maximal cyst(s) size) and morphology. We performed linear regression analysis with 99 % prediction intervals of gland size versus age for the size parameters. Results were compared with a recent meta-analysis of pineoblastoma by de Jong et al. Results Follow-up was available in 25 children showing stable cystic findings in 48 %, cyst size increase in 36 %, and decrease in 16 %. Linear regression analysis gave 99 % upper prediction bounds of 10.8 mm, 10.9 mm, 7.7 mm and 66.9 mm2, respectively, for cyst size, width, height, and area. The slopes (size increase per month) of each parameter were 0.030, 0.046, 0.021, and 0.25, respectively. Most of the pineoblastomas showed a size larger than the 99 % upper prediction margin, but with considerable overlap between the groups. Conclusion We presented age-adapted normal values for size and morphology of the cystic pineal gland in children aged 0 to 5 years. Analysis of size is helpful in discriminating normal glands from cystic pineal pathologies such as pineoblastoma. We also presented guidelines for the approach of a solid or cystic pineal gland in hereditary retinoblastoma patients. Electronic supplementary material The online version of this article (doi:10.1007/s00234-016-1683-0) contains supplementary material, which is available to authorized users.

The phase III ALEX study in patients with treatment-naive advanced anaplastic lymphoma kinase mutation-positive (ALK+) non-small-cell lung cancer (NSCLC) met its primary end point of improved progression-free survival (PFS) with alectinib versus crizotinib. Here, we present detailed central nervous system (CNS) efficacy data from ALEX. Overall, 303 patients aged ≥18 years underwent 1:1 randomization to receive twice-daily doses of alectinib 600 mg or crizotinib 250 mg. Brain imaging was conducted in all patients at baseline and every subsequent 8 weeks. End points (analyzed by subgroup: patients with/without baseline CNS metastases; patients with/without prior radiotherapy) included PFS, CNS objective response rate (ORR), and time to CNS progression. In total, 122 patients had Independent Review Committee-assessed baseline CNS metastases (alectinib, n = 64; crizotinib, n = 58), 43 had measurable lesions (alectinib, n = 21; crizotinib, n = 22), and 46 had received prior radiotherapy (alectinib, n = 25; crizotinib, n = 21). Investigator-assessed PFS with alectinib was consistent between patients with baseline CNS metastases [hazard ratio (HR) 0.40, 95% confidence interval (CI): 0.25-0.64] and those without (HR 0.51, 95% CI: 0.33-0.80, P interaction = 0.36). Similar results were seen in patients regardless of prior radiotherapy. Time to CNS progression was significantly longer with alectinib versus crizotinib and comparable between patients with and without baseline CNS metastases (P < 0.0001). CNS ORR was 85.7% with alectinib versus 71.4% with crizotinib in patients who received prior radiotherapy and 78.6% versus 40.0%, respectively, in those who had not. Alectinib demonstrated superior CNS activity and significantly delayed CNS progression versus crizotinib in patients with previously untreated, advanced ALK+ NSCLC, irrespective of prior CNS disease or radiotherapy. ClinicalTrials.gov NCT02075840.

PET using the amino-acid O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) is gaining increasing interest for brain tumour management. Semi-quantitative analysis of tracer uptake in brain tumours is based on the standardized uptake value (SUV) and the tumour-to-brain ratio (TBR). The aim of this study was to explore physiological factors that might influence the relationship of SUV of 18F-FET uptake in various brain areas, and thus affect quantification of 18F-FET uptake in brain tumours. Negative 18F-FET PET scans of 107 subjects, showing an inconspicuous brain distribution of 18F-FET, were evaluated retrospectively. Whole-brain quantitative analysis with Statistical Parametric Mapping (SPM) using parametric SUV PET images, and volumes of interest (VOIs) analysis with fronto-parietal, temporal, occipital, and cerebellar SUV background areas were performed to study the effect of age, gender, height, weight, injected activity, body mass index (BMI), and body surface area (BSA). After multivariate analysis, female gender and high BMI were found to be two independent factors associated with increased SUV of 18F-FET uptake in the brain. In women, SUVmean of 18F-FET uptake in the brain was 23% higher than in men (p, Highlights • SUVmean of 18F-FET in the normal brain is influenced by gender. • SUVmean of 18F-FET in the normal brain is weekly influenced by BMI. • The influence of these factors on SUV of 18F-FET is similar in all brain areas.

Brain MRI in 3966 children from the population-based Generation R Study (mean age, 10.1 years) revealed incidental findings in 25.6%. Most findings did not require neurosurgical intervention, but 7 children (0.18%) had suspected primary brain tumors.

Diaschisis denotes brain dysfunction remote from a focal brain lesion. We have quantified diaschisis and investigated its prognostic value in glioma. Methods: We compared 50 18F-FDG PET/CT studies collected prospectively from 14 patients with supratentorial glioma (5 men and 9 women; age range, 35-77 y) with 10 single scans from healthy controls (age range, 43-75 y). Dedicated 3-dimensional segmentation software was used to obtain total hemispheric glucose metabolic ratios (THGr) by dividing total hemispheric 18F-FDG uptake in each diaschitic hemisphere-that is, the ipsilateral cerebral hemisphere (THGr(Ce)) and the contralateral cerebellar hemisphere (THGr(Cb))-by its respective contralateral side. Receiver-operatingcharacteristic (ROC) analysis was performed to determine optimal cut-offs for combinations of THGr(Ce) and THGr(Cb). Two independent observers obtained data for reproducibility analysis, and THGr values were compared with qualitative assessment of diaschisis performed by a PET neuroimaging specialist. Results: Qualitative analysis confirmed cerebrocerebellar diaschisis in all glioblastoma PET studies performed within 1 y of death. Healthy subjects had significantly higher THGr(Ce) values (P = 0.0007) and THGr (Cb) values (P = 0.02) than glioblastoma patients. ROC analysis yielded diaschisis thresholds of 0.62 for THGr(Ce) and 0.84 for THGr (Cb). Qualitative assessment demonstrated cerebral diaschisis in 16 of 17 (94%) cases with THGr(Ce) below the determined threshold and cerebellar diaschisis in 25 of 26 (96%) cases with THGr(Cb) below the determined threshold. When both THGr(Ce) and THGr(Cb) were below the ROC threshold, the combined diaschisis measures had a positive predictive value for survival below 1 y of 100%. When one parameter was below the threshold, it had a positive predictive value of 75%, and when both parameters exceeded thresholds, the negative predictive value for survival above 1 y was 79%. Median interrater variability was 3.3% and 5.9% for THGr(Ce) and THGr(Cb), respectively. Conclusion: The THGr measures demonstrated diaschisis in the cerebrum and cerebellum of patients with glioma. Combined cerebrocerebellar diaschisis ratios with ROC thresholds for both forebrain and hindbrain had high negative and positive predictive values for survival for less than a year. The THGr method allows comparison of data obtained at different institutions and is now open for further validation in gliomas and other cerebral diseases.

Background For the past decade (18)F-fluoro-ethyl-l-tyrosine (FET) and (18)F-fluoro-deoxy-glucose (FDG) positron emission tomography (PET) have been used for the assessment of patients with brain tumor. However, direct comparison studies reported only limited numbers of patients. Our purpose was to compare the diagnostic performance of FET and FDG-PET. Methods We examined studies published between January 1995 and January 2015 in the PubMed database. To be included the study should: (i) use FET and FDG-PET for the assessment of patients with isolated brain lesion and (ii) use histology as the gold standard. Analysis was performed on a per patient basis. Study quality was assessed with STARD and QUADAS criteria. Results Five studies (119 patients) were included. For the diagnosis of brain tumor, FET-PET demonstrated a pooled sensitivity of 0.94 (95% CI: 0.79-0.98) and pooled specificity of 0.88 (95% CI: 0.37-0.99), with an area under the curve of 0.96 (95% CI: 0.94-0.97), a positive likelihood ratio (LR+) of 8.1 (95% CI: 0.8-80.6), and a negative likelihood ratio (LR-) of 0.07 (95% CI: 0.02-0.30), while FDG-PET demonstrated a sensitivity of 0.38 (95% CI: 0.27-0.50) and specificity of 0.86 (95% CI: 0.31-0.99), with an area under the curve of 0.40 (95% CI: 0.36-0.44), an LR+ of 2.7 (95% CI: 0.3-27.8), and an LR- of 0.72 (95% CI: 0.47-1.11). Target-to-background ratios of either FDG or FET, however, allow distinction between low- and high-grade gliomas (P > .11). Conclusions For brain tumor diagnosis, FET-PET performed much better than FDG and should be preferred when assessing a new isolated brain tumor. For glioma grading, however, both tracers showed similar performances.

Introduction Differentiation between normal solid (non-cystic) pineal glands and pineal pathologies on brain MRI is difficult. The aim of this study was to assess the size of the solid pineal gland in children (0–5 years) and compare the findings with published pineoblastoma cases. Methods We retrospectively analyzed the size (width, height, planimetric area) of solid pineal glands in 184 non-retinoblastoma patients (73 female, 111 male) aged 0–5 years on MRI. The effect of age and gender on gland size was evaluated. Linear regression analysis was performed to analyze the relation between size and age. Ninety-nine percent prediction intervals around the mean were added to construct a normal size range per age, with the upper bound of the predictive interval as the parameter of interest as a cutoff for normalcy. Results There was no significant interaction of gender and age for all the three pineal gland parameters (width, height, and area). Linear regression analysis gave 99 % upper prediction bounds of 7.9, 4.8, and 25.4 mm2, respectively, for width, height, and area. The slopes (size increase per month) of each parameter were 0.046, 0.023, and 0.202, respectively. Ninety-three percent (95 % CI 66–100 %) of asymptomatic solid pineoblastomas were larger in size than the 99 % upper bound. Conclusion This study establishes norms for solid pineal gland size in non-retinoblastoma children aged 0–5 years. Knowledge of the size of the normal pineal gland is helpful for detection of pineal gland abnormalities, particularly pineoblastoma. Electronic supplementary material The online version of this article (doi:10.1007/s00234-016-1684-z) contains supplementary material, which is available to authorized users.

Glioma models have provided important insights into human brain cancers. Among the investigative tools, MRI has allowed their characterization and diagnosis. In this study, we investigated whether diffusion MRI might be a useful technique for early detection and characterization of slow-growing and diffuse infiltrative gliomas, such as the proposed new models, LN-2669GS and LN-2540GS glioma sphere xenografts. Tumours grown in these models are not visible in conventional T2 -weighted or contrast-enhanced T1 -weighted MRI at 14.1 T. Diffusion-weighted imaging and diffusion tensor imaging protocols were optimized for contrast by exploring long diffusion times sensitive for probing the microstructural alterations induced in the normal brain by the slow infiltration of glioma sphere cells. Compared with T2 -weighted images, tumours were properly identified in their early stage of growth using diffusion MRI, and confirmed by localized proton MR spectroscopy as well as immunohistochemistry. The first evidence of tumour presence was revealed for both glioma sphere xenograft models three months after tumour implantation, while no necrosis, oedema or haemorrhage were detected either by MRI or by histology. Moreover, different values of diffusion indices, such as mean diffusivity and fractional anisotropy, were obtained in tumours grown from LN-2669GS and LN-2540GS glioma sphere lines. These observations highlighted diverse tumour microstructures for both xenograft models, which were reflected in histology. This study demonstrates the ability of diffusion MRI techniques to identify and investigate early stages of slow-growing, invasive tumours in the mouse brain, thus providing a potential imaging biomarker for early detection of tumours in humans.