2016 Background: Bevacizumab therapy (Bev) for recurrent glioblastoma (rGBM) has not demonstrated improved overall survival (OS) in randomized clinical trials. However, single-center and preliminary multi-center studies suggest that relative cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MRI (DSC-MRI) at baseline or shortly after treatment initiation may predict response to Bev. The primary aim of ECOG-ACRIN EAF151 was to evaluate whether early binary change in rCBV, assessed 2–3 weeks after initiation of Bev-containing therapy, identifies patients with an OS benefit of ≥4 mos. Pre-specified secondary aims assessed baseline rCBV; post-hoc exploratory analyses examined early post-Rx rCBV. Methods: A prospective, phase II multi-center trial without randomization enrolled subjects with rGBM receiving their first Bev-containing therapy. Progression was determined by local sites (RANO criteria; MRI within 28 days of registration, ≥42 days since end of chemoradiation). Baseline (S0) and follow-up (S1) DSC-MRI were performed before the first Bev infusion (within 3 days), and 12-25 days post-infusion but before the second infusion. Anatomic MRI and DSC-MRI (double-dose Gadavist injection, full-dose preload) complied with recommended protocols. Mean normalized (nRCBV) and standardized (sRCBV) rCBV were extracted from contrast-enhancing tumor ROIs. A 2-sided logrank test (α=0.1) was used to test the primary aim (change in mean nRCBV from S0 to S1, ≥0 vs. <0). Cox regression with restricted cubic splines was used to assess the association between continuous rCBV markers and OS. Results: 146 subjects were accrued from 33 sites. 134 completed S0 and 118 completed both S0 and S1. After exclusion of uninterpretable scans, 107 were evaluable for change in rCBV, and 124 for baseline rCBV. There was no statistically significant difference in OS between subjects with binary increase (n=40; median OS 8.0 mos [90% CI 5.5–9.2]) vs. decrease (n=67; 7.9 mos [90% CI 6.3–10.3]) in mean nRCBV (p=0.67). Based on the spline fits, we observed a strong nonlinear association between OS and continuous nRCBV and sRCBV at both S0 and S1: low marker values are associated with lower risk, but beyond a threshold, higher values are not associated with greater risk. As an exploratory analysis, we estimated optimal cut points (nRCBV: S0=1.11, S1=1.16; sRCBV: S0=1.13, S1=0.99) and found that subjects whose S1 rCBV decreased below threshold had a median OS 2.7 mos (nRCBV, HR 0.61 [90% CI 0.41–0.88]) and 5.2 mos (sRCBV, HR 0.43 [90% CI 0.28–0.63]) longer than subjects whose rCBV either increased or did not decrease below threshold. Conclusions: EAF151 prospectively evaluated whether DSC-MRI markers predict OS in patients with rGBM treated with Bev. Although the change in these markers was not predictive, both baseline and early post-Rx markers were predictive of OS. Clinical trial information: NCT03115333 .

BACKGROUND:Ischemic stroke in deep brain regions is commonly attributed to small vessel ischemic disease (SVID) or branch atheromatous disease (BAD). Differentiating these mechanisms is clinically important, as BAD is associated with progressive symptoms, early neurological deterioration, and poorer outcomes, whereas SVID typically follows a more stable course. Conventional imaging is limited in distinguishing these entities. High-resolution vessel wall imaging enables direct visualization of intracranial vessel wall pathology and may refine risk stratification. METHODS:We conducted a prospective, single-center study of patients with acute subcortical infarcts admitted between 2023 and 2025. Eligible patients underwent magnetic resonance imaging with high-resolution vessel wall imaging within 1 week of admission. SVID was defined as lacunar infarction without evidence of parent artery plaque or vessel wall enhancement. BAD was defined as infarction in the territory of a penetrating artery with associated parent artery enhancement. The primary outcome was differentiation of BAD from SVID based on vessel wall enhancement. Secondary outcomes included 90-day functional outcomes. RESULTS:Of 23 patients enrolled, 10 underwent magnetic resonance imaging with high-resolution vessel wall imaging. Vessel wall enhancement was observed in 5 patients (50%). Patients with enhancement were more often male (100% versus 40%) and had a higher prevalence of hyperlipidemia (100% versus 20%) compared with those without enhancement. Functional outcomes at 90 days were similar between the 2 groups. CONCLUSIONS:High-resolution vessel wall imaging can identify parent artery pathology not evident on conventional imaging, helping to distinguish BAD from SVID. This differentiation is clinically meaningful, as BAD may require more intensive secondary prevention. Larger studies are needed to validate these findings.

Background:While both MRI radiomics and miRNA liquid biopsy have shown promise for glioblastoma prognostication, state-of-the-art methods may enable novel integration of multimodal data to further optimize performance. Methods:Serum samples (n = 193) were collected from 73 patients with pathology-confirmed glioblastoma at a single center. We quantified 798 miRNAs per sample using nCounter. Radiomic features were automatically extracted from MRI scans (n = 306) obtained during the same follow-up period. A new data integration pipeline was applied to evaluate machine learning-based outcome predictions using miRNA, radiomic, and miRNA+radiomic input datasets. All models included a set of clinical covariates of known prognostic value. Time-averaged AUC analysis was used to incorporate longitudinal sampling. In experiments to classify postoperative samples labeled by likely disease burden, performance-driving miRNAs were further investigated using counterfactual analysis (CA) and Shapley Additive Explanations (SHAP). Results:The recurrence rate was 75% (median 222 days, IQR [93-378]), and post-recurrence mortality was 62% (412 days, [274-695]) during the follow-up period. Radiomics outperformed miRNAs for recurrence prediction (time-averaged AUC = 0.66 [0.60-0.71] versus AUC = 0.56 [0.53-0.59]), while miRNAs outperformed radiomics for survival prediction (AUC = 0.70 [0.64-0.77] versus AUC = 0.55 [0.47-0.60]). Combined miRNA+radiomics models performed well for recurrence (AUC = 0.64 [0.44-0.80]) and best overall for survival (AUC = 0.76 [0.63-0.86]). Several performance-driving miRNAs were also identified on CA and SHAP analyses. Conclusions:Serum miRNA profiling combined with MRI radiomics may improve longitudinal approximation of postoperative glioblastoma prognosis. Integrating multimodal data is feasible and could enable more informed counseling of patients and families over the disease course.

Vascular remodeling is inherent to the pathogenesis of many diseases including cancer, neurodegeneration, fibrosis, hypertension, and diabetes. In this paper, a new susceptibility-contrast based MRI approach is established to non-invasively image intravoxel vessel size distribution (VSD), enabling a more comprehensive and quantitative assessment of vascular remodelling. The approach utilizes high-resolution light-sheet fluorescence microscopy (LSFM) images of rodent brain vasculature, gradient echo sampling of free induction decay and spin echo (GESFIDE) MRI signal simulation from the three-dimensional (3D) vascular networks, and training a deep learning (DL) model to predict cerebral blood volume (CBV) and VSD from GESFIDE signals. Specifically, small voxel-size volumes of interest (VOI) (n = 32,000) were extracted from LSFM images of rodent brain and the vascular structure was segmented. Next, two DL models were trained to predict the CBV and VSD from the ratio of pre- and post-contrast GESFIDE signals simulated from these VOIs. The results from ex vivo experiments on test VOIs (n = 3,132) demonstrated strong linear correlation (r = 0.95) and high similarity (mean Bhattacharya Coefficient (BC) = 0.87) between the true and predicted CBV and VSDs, respectively. The DL models outperformed the traditional dictionary-matching approach and demonstrated high accuracy in predicting CBV and VSD, even when the GESFIDE signals were degraded with varying noise levels (SNR: 15, 30, 45, and 60 dB). The DL model showed comparable results to those observed in the test VOIs on a public mouse brain vasculature dataset (n = 1,000), demonstrating the generalizability of the DL models. The accuracy of the predicted CBV (r = 0.78) and VSD (mean BC = 0.82) on the tumor VOIs (n = 706) was moderately high but lower than the accuracy of predicted CBV and VSD observed for the healthy VOIs. Hence, with further in vivo validation, intravoxel VSD imaging could become a transformative preclinical and clinical tool for interrogating disease and treatment-induced vascular remodeling.

BACKGROUND AND PURPOSE:Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. MATERIALS AND METHODS:Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. RESULTS:Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. CONCLUSIONS:Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

BackgroundIGV-001 is a type of cellular immunotherapy currently being investigated for treating glioblastoma (NCT04485949). It uses the patient’s tumor to elicit an autologous immune response.MethodsThe process involves (i) craniotomy for maximum safe resection of the glioblastoma, (ii) ex-vivo treatment of the tumor with an anti-sense oligodeoxynucleotide against insulin-like growth factor 1 receptor followed by irradiation, (iii) placement of the treated tumor in multiple bio-diffusion chambers, which are implanted into the patient’s abdominal sheath to elicit an immune response, and (iv) explantation of the chambers 48 hours later. The clinical trial was open at 32 sites in the United States, and eligible subjects were randomized in a 2:1 ratio to receive bio-diffusion chambers containing either conditioned glioblastoma tissue or a placebo. Patients subsequently proceeded to standard-of-care treatment with concomitant radiation-temozolomide, followed by 6 cycles of adjuvant temozolomide.ResultsThe execution of the IGV-001 protocol procedure is complicated and involves a multi-step process requiring mobilization of multiple services within the cancer center of a tertiary care hospital, including neurosurgery, neuro-oncology, radiation oncology, neuroradiology, cancer clinical trial office, and operating room personnel to fulfill the pre-specified protocol requirements in a timely fashion.ConclusionsWe have learned a great deal in the process of developing and executing our internal procedures for this clinical trial. Our description of the IGV-001 protocol workflow may serve as a “blueprint” for future implementation of this type of cellular immunotherapy at other centers. We further discuss some of the lessons we have learned during the trial.

Predictive tools for stratifying neonatal hydrocephalus into low- and high-risk groups for cerebrospinal fluid (CSF) diversion are currently lacking. We developed and validated an artificial intelligence (AI) model that integrates multimodal imaging and clinical data to predict CSF diversion needs. The development cohort included 116 neonates with suspicion of raised intracranial pressure (ICP) from a Chinese tertiary referral hospital (80 with intracranial pressure > 80 mm H2O, 36 with intracranial pressure ≤ 80 mm H2O). The external validation cohort consisted of 21 neonates with hydrocephalus from an American medical center, categorized by etiology: prenatal myelomeningocele (MMC) closure (n = 5), postnatal MMC closure (n = 6), and post-hemorrhagic hydrocephalus (PHH) (n = 10). Inclusion criteria required available MRI and complete clinical follow-up to confirm CSF diversion outcomes. The primary outcome was the need for CSF diversion. Model performance was assessed using under the receiver operating characteristics curve (AUC), sensitivity, and specificity. The hybrid AI model achieved an AUC of 0.824 in the development cohort in predicting raised ICP, outperforming both the clinical-only model (AUC 0.528, p < 0.001) and the image-only model (AUC 0.685, p = 0.007). In the external validation cohort, the fused MRI-based model achieved an AUC of 0.808. The model correctly predicted CSF diversion in 4/5 prenatal MMC, 4/6 postnatal MMC, and 9/10 PHH cases. The AI model demonstrated robust performance in predicting the need for CSF diversion, particularly in PHH cases, and has the potential to assist decision-making, especially in settings with limited pediatric neurosurgical expertise. Future work should focus on further refining model performance for complex etiologies such as MMC-associated hydrocephalus.

miRNA liquid biopsy and MRI radiomics have both shown promise for prognostication in glioblastoma, though neither has been translated to clinical use. Leveraging a prospective, longitudinal, dataset from a major tertiary care center (2019–2024), we implemented a novel multimodal data integration pipeline and an unbiased approach to feature identification to evaluate the utility of miRNAs versus MR radiomics for predicting glioblastoma recurrence and survival. Serum samples were collected longitudinally from 73 patients with pathology-confirmed glioblastoma (193 total samples). 798 miRNAs were quantified per sample (count per 40 µL serum) using the Nanostring nCounter platform. Automated radiomic feature extraction was performed on MRI data obtained during the same follow-up period. A novel data integration pipeline was employed to evaluate miRNA, radiomic, and combined miRNA+radiomic data as input datasets to machine learning -based outcome prediction (80/20 test-train split). Multiple feature selection techniques were tested per input dataset. All models also included a set of demographic and clinical variables (age, gender, extent of resection, and MGMT promotor methylation). Time-averaged AUC analysis was utilized to incorporate longitudinal sampling in outcome prediction. miRNAs of importance were further confirmed using counterfactual analysis (CA) and SHapley Additive exPlanations (SHAP), which were applied to classify a subset of postoperative samples labelled by disease burden (early post-op versus likely recurrence) based upon miRNAs alone. During the follow-up period, the recurrence rate was 75% (median 222 days, interquartile range [93–378]) and mortality was 62% (412 [274–695]). miRNA-only and radiomics-only models performed well for survival prediction (time-averaged AUC=0.77 [0.62–0.87] and 0.75 [0.56–0.93], respectively), but the fully integrated (miRNA+MRI) model offered the best performance (AUC=0.90 [0.81–0.99]). Results were similar for recurrence prediction. A subset of miRNAs (e.g., miR-320e, miR-451a) were observed as important across CA, SHAP, and recurrence prediction analyses. Serum miRNA profiling may offer an inexpensive means of estimating glioblastoma prognosis, alone or in combination with modern radiomics-based methods.

Vascular remodelling is inherent to the pathogenesis of many diseases including cancer, neurodegeneration, fibrosis, hypertension, and diabetes. In this paper, a new susceptibility-contrast based MRI approach is established to non-invasively image intravoxel vessel size distribution (VSD), enabling a more comprehensive and quantitative assessment of vascular remodelling. The approach is founded on imaging vascular structures across a rodent brain using high-resolution, light-sheet fluorescence microscopy, simulating gradient echo sampling of free induction decay and spin echo (GESFIDE) MRI signals for the three-dimensional vascular networks, and training a deep learning model to predict cerebral blood volume (CBV) and VSD from GESFIDE signals. The results from ex vivo experiments demonstrated strong correlation (r = 0.96) between the true and predicted CBV. Also, high similarity between true and predicted VSDs was observed (mean Bhattacharya Coefficient = 0.92). With further in vivo validation, intravoxel VSD imaging could become a transformative preclinical and clinical tool for interrogating disease and treatment induced vascular remodelling.

OBJECTIVE Innovations in robotics continue to reshape the landscape of neurosurgery. Here, the authors evaluated the safety and efficacy of the ExcelsiusGPS robot in the treatment of neuro-oncological, intracranial lesions. METHODS The authors conducted a retrospective analysis of 19 consecutive adult patients with a neuro-oncological diagnosis who underwent intracranial biopsy and/or laser interstitial thermal therapy (LITT) with the assistance of the ExcelsiusGPS robot and intraoperative CT. Demographic and clinical data were collected from the electronic medical record and the robot software. RESULTS All 19 patients harbored lesions that were deep seated, involving the eloquent cortex, or subcentimeter. Definitive tissue diagnosis was achieved in all cases involving stereotactic biopsy (n = 16), with glioblastoma as the most common diagnosis. The mean +/- SD time for setting up the robotic stereotaxis system was 57.4 +/- 10.7 minutes. The mean procedural time after that was 71.6 +/- 41.0 minutes for stereotactic needle biopsy and 188.4 +/- 61.2 minutes for procedures involving LITT. The mean radial errors of the actual trajectory relative to the planned trajectory at the entry and target points were 0.625 +/- 0.443 mm and 0.745 +/- 0.472 mm, respectively. There were no procedural complications or new postoperative deficits, although routine postoperative CT showed new hyperdensity at the target site in 3/19 patients (15.7%). All patients who underwent elective procedures were discharged by postoperative day 3 (mean 1.38 +/- 0.619 days). There were two 30-day readmissions (pulmonary embolus and general weakness), and neither was attributable to the surgical procedure. CONCLUSIONS The authors' pilot experience with the ExcelsiusGPS robot in neuro-oncology procedures indicates a favorable efficacy and safety profile.

ObjectivesWe present an adolescent in whom olfactory neuroblastoma (ONB) was detected on follow-up magnetic resonance imaging (MRI) 2.5 years after SIADH diagnosis. Our case contrasts prior pediatric reports in which ONB and SIADH were diagnosed concurrently.Case presentationA previously healthy 13-year-old girl was found to have SIADH during evaluation for restrictive eating. Work-up ruled out adrenal, thyroid and paraneoplastic causes, diuretic use, and vasopressin receptor and aquaporin channel mutations. Brain MRI was normal except for paranasal sinus (PNS) inflammatory changes to the left fronto-maxillary sinuses and frontoethmoidal recess. The sodium levels normalized with fluid restriction (800-900 ml/m2/day). Multiple repeated attempts to liberalize fluid intake resulted in recurrent hyponatremia. Follow-up brain MRIs 4 and 11 months after the initial presentation showed persistent PNS inflammatory changes. A subsequent brain MRI 31 months after initial presentation demonstrated a lesion in the left frontoethmoidal recess extending into the left nasal cavity and biopsy showed low grade ONB. The patient underwent surgery with normalization of serum sodium on liberalized fluid intake. Seven days after surgery, she had recurrence of SIADH, and brain MRI showed remnant of the ONB at the fovea ethmoidalis. She completed adjuvant radiotherapy though her SIADH persisted.ConclusionsOur case highlights the importance of considering ONB in the evaluation of children with SIADH. Idiopathic SIADH is rare in children and if no cause is identified, computed tomography of sinuses and nasal endoscopy should be considered earlier in the work-up of these patients, particularly in the absence of sinus symptoms.

Background and Purpose Infarcts in acute ischemic stroke (AIS) patients may continue to grow even after reperfusion, due to mechanisms such as microvascular obstruction and reperfusion injury. We investigated whether and how much infarcts grow in AIS patients after near-complete (expanded Thrombolysis in Cerebral Infarction [eTICI] 2c/3) reperfusion following endovascular treatment (EVT), and to assess the association of post-reperfusion infarct growth with clinical outcomes.Methods Data are from a single-center retrospective observational cohort study that included AIS patients undergoing EVT with near-complete reperfusion who received diffusion-weighted magnetic resonance imaging (MRI) within 2 hours post-EVT and 24 hours after EVT. Association of infarct growth between 2 and 24 hours post-EVT and 24-hour National Institutes of Health Stroke Scale (NIHSS) as well as 90-day modified Rankin Scale score was assessed using multivariable logistic regression.Results Ninety-four of 155 (60.6%) patients achieved eTICI 2c/3 and were included in the analysis. Eighty of these 94 (85.1%) patients showed infarct growth between 2 and 24 hours post-reperfusion. Infarct growth ≥5 mL was seen in 39/94 (41.5%) patients, and infarct growth ≥10 mL was seen in 20/94 (21.3%) patients. Median infarct growth between 2 and 24 hours post-reperfusion was 4.5 mL (interquartile range: 0.4–9.2 mL). Post-reperfusion infarct growth was associated with the 24-hour NIHSS in multivariable analysis (odds ratio: 1.16 [95% confidence interval 1.09–1.24], P<0.01).Conclusion Infarcts continue to grow after EVT, even if near-complete reperfusion is achieved. Investigating the underlying mechanisms may inform future therapeutic approaches for mitigating the process and help improve patient outcome.

The dynamic susceptibility contrast (DSC) MRI measures of relative cerebral blood volume (rCBV) play a central role in monitoring therapeutic response and disease progression in patients with gliomas. Previous investigations have demonstrated promise of using rCBV in classifying tumor grade, elucidating tumor viability after therapy, and differentiating pseudoprogression and pseudoresponse. However, the quantification and reproducibility of rCBV measurements across patients, devices, and software remain a critical barrier to routine or clinical trial use of longitudinal DSC MRI in patients with gliomas. To address this limitation, the RSNA DSC MRI Biomarker Committee of the Quantitative Imaging Biomarkers Alliance developed a Profile that defines statistics-based claims for the precision of longitudinal measurements. Although rCBV is the clinical marker of interest, the Profile focused on the reproducibility of the measured quantitative imaging biomarker, which is the area under the contrast agent concentration-time curve (AUC) normalized by the mean value of normal-appearing contralateral white matter tissue (tissue-normalized AUC values). Based on previous reports of within-subject coefficient of variation (wCV) in the tissue-normalized AUC values for enhancing gliomas (wCV = 0.31), an increase of 182% or more with respect to the baseline tissue-normalized AUC value indicates that an increase has occurred with 95% confidence. In contrast, a decrease of 64% or more with respect to baseline suggests that a decrease has occurred with 95% confidence. Similarly, an increase of 399% or more in the tissue-normalized AUC values in normal brain gray matter tissue (wCV = 0.40) suggests that an increase has occurred with 95% confidence, whereas a decrease of 80% or more with respect to baseline suggests that a decrease has occurred with 95% confidence. This article provides the rationale for these claims and the compliance activities needed to achieve these claims. Potential updates to incorporate new data based on advances in technology and clinical care in the Profile are also discussed.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.

Background: The invasion of glioblastoma cells beyond the visible tumor margin depicted by conventional neuroimaging is believed to mediate recurrence and predict poor survival. Radiomic biomarkers that are associated with the direction and extent of tumor infiltration are, however, non-existent. Methods: Patients from a single center with newly diagnosed glioblastoma (n = 7) underwent preoperative Q-space magnetic resonance imaging (QSI; 3T, 64 gradient directions, b = 1000 s/mm2) between 2018 and 2019. Tumors were manually segmented, and patterns of inter-voxel coherence spatially intersecting each segmentation were generated to represent tumor-associated tractography. One patient additionally underwent regional biopsy of diffusion tract- versus non-tract-associated tissue during tumor resection for RNA sequencing. Imaging data from this cohort were compared with a historical cohort of n = 66 glioblastoma patients who underwent similar QSI scans. Associations of tractography-derived metrics with survival were assessed using t-tests, linear regression, and Kaplan-Meier statistics. Patient-derived glioblastoma xenograft (PDX) mice generated with the sub-hippocampal injection of human-derived glioblastoma stem cells (GSCs) were scanned under high-field conditions (QSI, 7T, 512 gradient directions), and tumor-associated tractography was compared with the 3D microscopic reconstruction of immunostained GSCs. Results: In the principal enrollment cohort of patients with glioblastoma, all cases displayed tractography patterns with tumor-intersecting tract bundles extending into brain parenchyma, a phenotype which was reproduced in PDX mice as well as in a larger comparison cohort of glioblastoma patients (n = 66), when applying similar methods. Reconstructed spatial patterns of GSCs in PDX mice closely mirrored tumor-associated tractography. On a Kaplan-Meier survival analysis of n = 66 patients, the calculated intra-tumoral mean diffusivity predicted the overall survival (p = 0.037), as did tractography-associated features including mean tract length (p = 0.039) and mean projecting tract length (p = 0.022). The RNA sequencing of human tissue samples (n = 13 tumor samples from a single patient) revealed the overexpression of transcripts which regulate cell motility in tract-associated samples. Conclusions: QSI discriminates tumor-specific patterns of inter-voxel coherence believed to represent white matter pathways which may be susceptible to glioblastoma invasion. These findings may lay the groundwork for future work on therapeutic targeting, patient stratification, and prognosis in glioblastoma.

Diffusion-weighted imaging (DWI) lesion expansion after endovascular thrombectomy (EVT) is not well characterized. We used serial diffusion-weighted magnetic resonance imaging (MRI) to measure lesion expansion between 2 and 24 h after EVT. In this single-center observational analysis of patients with acute ischemic stroke due to large vessel occlusion, DWI was performed post-EVT (< 2 h after closure) and 24-h later. DWI lesion expansion was evaluated using multivariate generalized linear mixed modeling with various clinical moderators. We included 151 patients, of which 133 (88

BackgroundRelative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol.MethodsThe study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV < 1.0), tumor with moderate angiogenesis (1.0 < sRCBV < 1.75), and tumor with high angiogenesis (sRCBV > 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold.ResultsThe mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%).ConclusionsThe optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.

Delirium occurs frequently in patients with stroke, but the role of preexisting neural substrates in delirium pathogenesis remains unclear. We sought to explore associations between acute and chronic neural substrates of delirium in patients with intracerebral hemorrhage (ICH). Using data from a single-center ICH registry, we identified consecutive patients with acute nontraumatic ICH and available magnetic resonance imaging scans. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria were used to classify each patient as delirious or nondelirious during their hospitalization. Magnetic resonance imaging scans were processed and analyzed using semiautomated software, with volumetric measurement of acute ICH volume as well as white matter hyperintensity volume (WMHV) and gray and white matter volumes from the contralateral hemisphere. We tested associations between WMHV and incident delirium using multivariable regression models, and then determined the predictive accuracy of these neuroimaging models via area under the curve (AUC) analysis. Of 139 patients in our cohort (mean [standard deviation] age 67.3 [17.3] years, 53