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Purpose: Although perfusion magnetic resonance imaging (MRI) is widely used to identify pseudoprogression, this advanced technique lacks clinical reliability. Our aim was to develop a parameter assessing the hypervascularized fraction of glioblastomas based on volume analysis of dynamic susceptibility contrast-enhanced MRI and evaluate its performance in the diagnosis of pseudoprogression. Methods: Patients with primary glioblastoma showing lesion progression on the first follow-up MRI after chemoradiotherapy were enrolled retrospectively. On both initial and first follow-up MRIs, the leakage-corrected cerebral blood volume (CBV) maps were post-processed using the conventional hot-spot method and a volume method, after manual segmentation of the contrast-enhanced delineated lesion. The maximum CBV (rCBVmax) was calculated with both methods. Secondly, the threshold of 2 was applied to the CBV values contained in the entire segmented volume, defining our new parameter: %rCBV>2. The probability of pseudoprogression based on rCBVmax and %rCBV>2 was calculated in logistic regression models and diagnostic performance assessed by receiving operator characteristic curves. Results: Out of 25 patients, 11 (44%) were classified with pseudoprogression and 14 (56%) with true progression based on the Response Assessement in Neuro-Oncology criteria. rCBVmax was lower for pseudoprogression (3.4 vs. 7.6; p = 0.033) on early follow-up MRI. %rCBV>2, was lower for pseudoprogression on both initial (57.5% vs. 71.3%; p = 0.033) and early follow-up MRIs (22.1% vs. 51.8%; p = 0.0006). On early follow-up MRI, %rCBV>2 had the largest area under the curve for the diagnosis of pseudoprogression: 0.909 [0.725–0.986]. Conclusion: The fraction of hypervascularization of glioblastomas as assessed by %rCBV>2 was lower in tumours that subsequently developed pseudoprogression both on the initial and early follow-up MRIs. This fractional parameter may help identify pseudoprogression with greater accuracy than rCBVmax. [ABSTRACT FROM AUTHOR]

Purpose: Despite a high variability, the hotspot method is widely used to calculate the cerebral blood volume (CBV) of glioblastomas on DSC-MRI. Our aim was to investigate inter- and intra-observer reproducibility of parameters calculated with the hotspot or a volume method and that of an original parameter assessing the fraction of pixels in the tumour volume displaying rCBV > 2: %rCBV > 2. Methods: Twenty-seven consecutive patients with untreated glioblastoma (age: 63, women: 11) were retrospectively included. Three observers calculated the maximum tumour CBV value (rCBVmax) normalized with a reference ROI in the contralateral white matter (CBVWM) with (i) the hotspot method and (ii) with a volume method following tumour segmentation on 3D contrast–enhanced T1-WI. From this volume method, %rCBV > 2 was also assessed. After 8–12 weeks, one observer repeated all delineations. Intraclass (ICC) and Lin's (LCC) correlation coefficients were used to determine reproducibility. Results: Inter-observer reproducibility of rCBVmax was fair with the hotspot and good with the volume method (ICC = 0.46 vs 0.65, p > 0.05). For CBVWM, it was fair with the hotspot and excellent with the volume method (0.53 vs 0.84, p < 0.05). Reproducibility of one pairwise combination of observers was significantly better for both rCBVmax and CBVWM (LCC = 0.33 vs 0.75; 0.52 vs 0.89, p < 0.05). %rCBV > 2 showed excellent inter- and intra-observer reproducibility (ICC = 0.94 and 0.91). Conclusion: Calculated in glioblastomas with a volume method, rCBVmax and CBVWM yielded good to excellent reproducibility but only fair with the hotspot method. Overall, the volume analysis offers a highly reproducible parameter, %rCBV > 2, that could be promising during the follow-up of such heterogeneous tumours. [ABSTRACT FROM AUTHOR]

Simple Summary: A major prognosis factor for glioblastoma patients is the methylation status of the DNA repair enzyme MGMT. Obtaining this information using deep learning models trained on non-invasive MRI data is a major challenge with no scientific consensus to date. In this study, we provide a more rigorous and comprehensive answer to this question by using confidence metrics and relating them to the exact percentage of methylation obtained at biopsy. This systematic approach confirms that the deep learning algorithms developed until now are not suitable for clinical application. We also provide, to the best of our knowledge, the first fully reproducible source code and experiments on this issue. Glioblastoma is the most aggressive primary brain tumor, which almost systematically relapses despite surgery (when possible) followed by radio-chemotherapy temozolomide-based treatment. Upon relapse, one option for treatment is another chemotherapy, lomustine. The efficacy of these chemotherapy regimens depends on the methylation of a specific gene promoter known as MGMT, which is the main prognosis factor for glioblastoma. Knowing this biomarker is a key issue for the clinician to personalize and adapt treatment to the patient at primary diagnosis for elderly patients, in particular, and also upon relapse. The association between MRI-derived information and the prediction of MGMT promoter status has been discussed in many studies, and some, more recently, have proposed the use of deep learning algorithms on multimodal scans to extract this information, but they have failed to reach a consensus. Therefore, in this work, beyond the classical performance figures usually displayed, we seek to compute confidence scores to see if a clinical application of such methods can be seriously considered. The systematic approach carried out, using different input configurations and algorithms as well as the exact methylation percentage, led to the following conclusion: current deep learning methods are unable to determine MGMT promoter methylation from MRI data. [ABSTRACT FROM AUTHOR]

The Central Nervous System (CNS) tumor with BCOR internal tandem duplication (ITD) has recently been added as a novel embryonal histomolecular tumor type to the 2021 World Health Organization (WHO) Classification of CNS Tumors. In addition, other CNS tumors harboring a BCOR/BCORL1 fusion, which are defined by a distinct DNA-methylation profile, have been recently identified in the literature but clinical, radiological and histopathological data remain scarce. Herein, we present two adult cases of CNS tumors with EP300::BCOR fusion. These two cases presented radiological, histopathological, and immunohistochemical homologies with CNS tumors having BCOR ITD in children. To compare these tumors with different BCOR alterations, we performed a literature review with a meta-analysis. CNS tumors with EP300::BCOR fusion seem to be distinct from their BCOR ITD counterparts in terms of age, location, progression-free survival, tumor growth pattern, and immunopositivity for the BCOR protein. CNS tumors from the EP300::BCOR fusion methylation class in adults may be added to the future WHO classification. [ABSTRACT FROM AUTHOR]

• Adjuvant radiation therapy (proton or photon) is essential for childhood ependymoma. • Proton therapy may spare organs at risk, but possibly induces brainstem toxicity. • We studied symptomatic brainstem injury and asymptomatic radiographic changes. • Asymptomatic punctiform brainstem change was observed in 4.8% of patients. • No new or progressive brainstem-related symptoms emerged (median follow-up: 5.6 years) Ependymoma is the third most frequent childhood braintumor. Standard treatment is surgery followed by radiation therapy including proton therapy (PBT). Retrospective studies have reported higher rates of brainstem injury after PBT than after photon therapy (XRT). We report a national multicenter study of the incidence of brainstem injury after XRT versus PBT, and their correlations with dosimetric data. We included all patients aged < 25 years who were treated with PBT or XRT for intracranial ependymoma at five French pediatric oncology reference centers between 2007 and 2020. We reviewed pre-irradiation MRI, follow-up MRIs over the 12 months post-treatment and clinical data. Of the 83 patients, 42 were treated with PBT, 37 with XRT, and 4 with both (median dose: 59.4 Gy, range: 53‑60). No new or progressive symptomatic brainstem injury was found. Four patients presented asymptomatic radiographic changes (punctiform brainstem enhancement and FLAIR hypersignal), with median onset at 3.5 months (range: 3.0‑9.4) after radiation therapy, and median offset at 7.6 months (range: 3.7‑7.9). Two had been treated with PBT, one with XRT, and one with mixed XRT-PBT. Prescribed doses were 59.4, 55.8, 59.4 and 54 Gy. Asymptomatic radiographic changes occurred in 4.8% of patients with ependymoma in a large national series. There was no correlation with dose or technique. No symptomatic brainstem injury was identified. [ABSTRACT FROM AUTHOR]

Background: Leptomeningeal enhancement (LME) is a key feature of Susac syndrome (SuS) but is only occasionally depicted on post-contrast T1-weighted images (T1-WI). Objective: As post-contrast fluid-attenuated inversion recovery (FLAIR) may be more sensitive, our aim was to assess LME in SuS on this sequence. Methods: From 2010 to 2020, 20 patients with definite SuS diagnosis were retrospectively enrolled in this multicentre study. Two radiologists independently assessed the number of LME on post-contrast FLAIR and T1-WI acquisitions performed before any treatment. A chi-square test was used to compare both sequences and the interrater agreement was calculated. Results: Thirty-five magnetic resonance imagings (MRIs) were performed before treatment, including 19 post-contrast FLAIR images in 17 patients and 25 post-contrast T1-WI in 19 patients. In terms of patients, LME was observed on all post-contrast FLAIR, contrary to post-contrast T1-WI (17/17 (100%) vs. 15/19 (79%), p < 0.05). In terms of sequences, LME was observed on all post-contrast FLAIR, contrary to post-contrast T1-WI (19/19 (100%) vs. 16/25 (64%), p < 0.005). LME was disseminated at both supratentorial (19/19) and infratentorial (18/19) levels on post-contrast FLAIR, contrary to post-contrast T1-WI (3/25 and 9/25, respectively). Interrater agreement was excellent for post-contrast FLAIR (κ = 0.95) but only moderate for post-contrast T1-WI (κ = 0.61). Conclusion: LME was always observed and easily visible on post-contrast FLAIR images prior to SuS treatment. In association with other MRI features, it is highly indicative of SuS. [ABSTRACT FROM AUTHOR]

Background: The composition of the thrombus influences its retrievability by mechanical thrombectomy. Purpose: Our study aimed to report on thrombi resistant to aspiration, regarding susceptibility vessel sign and histologic composition. Methods: This observational study was based on a prospective database of acute anterior circulation ischemic strokes treated by mechanical thrombectomy. Endovascular first-line strategy was aspiration and in case of failure, combined therapy-rescue was performed. The positivity of susceptibility vessel sign (SVSþ) or its negativity (SVS+) was assessed on T2* sequences. The thrombus composition was analyzed with hematoxylin eosin staining. Results: Histological analysis was performed on 102 clots. Thrombi with SVS+ were significantly richer in fibrin/platelets, p=0.04. Out of 210 mechanical thrombectomy, aspiration first pass strategy was performed in 131/210 (62%) patients. Combined therapy-rescue was needed in 37% of aspiration first pass strategy cases (n=131). Clots retrieved combined therapy-rescue were richer in fibrin/platelets 63.9% versus 50.8% for aspiration first pass strategy, p=0.03. Logistic regression analysis showed that fibrin/platelet-poor clots (<60%) were significantly more likely to be recanalized by aspiration first pass strategy compared to fibrin/platelet-rich clots (>60%) that were more likely recanalized by combined therapy-rescue after aspiration first pass strategy failure (OR=3.5; 95% CI=1.2-10.8; p=0.0054). Conclusions: Our results confirm that SVS-clots are rich in fibrin/platelets and suggest that these "white clots" are less likely to be retrieved by aspiration alone and more often require the use of combined therapy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Posterior fossa tumors and surgery have an impact on cortico-cerebellar connectivity. • Irradiation has an adding impact on cortico-hippocampal and -striatal connectivity. • Atypical connectivities are linked with memory outcomes in irradiated patients. • Hippocampus, striatum and cerebellum can be considered as organ at risk for memory. Memory is one of the main specific cognitive domains impaired with attention and processing speed after a pediatric brain tumor. This work explored the long-term impact of radiotherapy in children with posterior fossa tumor (PFT) on brain connectivity in neural circuits involved in memory using resting-state functional magnetic resonance imaging (rs-fMRI). A total of 20 irradiated and 15 non-irradiated PFT survivors, and 21 healthy controls, prospectively included in the IMPALA study (NCT04324450), performed memory tests assessing episodic, procedural, and working memories and were subjected to an rs-fMRI. We manually contoured main structures involved in memory to explore connectivity at rest in a seed-to-voxel analysis. The groups were compared and differences in connectivity were correlated with behavioral scores and irradiation doses. The performance of all mnesic tasks was lower in PFT survivors with a greater alteration in working and episodic memory in irradiated patients. Irradiated survivors had atypical connectivities in all memory circuits compared to controls and in cortico-caudate and cortico-cerebellar circuits compared to non-irradiated survivors. Non-irradiated survivors had only atypical connectivities in the cortico-cerebellar circuits compared to controls. In irradiated survivors, atypical connectivities in cortico-hippocampal circuits were linked with episodic memory scores and dose of irradiation to the left hippocampus and in cortico-striatal circuits with procedural memory scores and dose of irradiation to the striatum. The results of this study highlight that irradiation has a long-term impact on brain connectivity in brain circuits involved in memory after pediatric PFT with a specific radiation-dose effect in supratentorial structures. [ABSTRACT FROM AUTHOR]

• Identification of glioblastoma components prior to radiotherapy is of great importance. • Clustering large MRSI data allows pathological tissue identification in this tumor. • Dominance of clusters with metabolic abnormalities in the tumor is predictive of poor PFS. 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). 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. 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. 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. [ABSTRACT FROM AUTHOR]

• Diagnosis of pseudoprogression after glioblastoma treatment is a veritable challenge. • Multimodal MRI including MRSI with Cho/Cr and Lac/NAA should be used to differentiate pseudoprogression from true progression. • TV at first imaging 3 months post-radiotherapy appeared to be a good predictive factor for progression. To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann–Whitney tests, and p values were adjusted with Bonferroni correction. Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP. [ABSTRACT FROM AUTHOR]