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Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20,101 MRI images) was used for training, while C2 (197, 11,333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class’ added value was evaluated via radiomics model performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95% Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. A total of 620 images were misclassified; manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased the PFS model C-I by 14.6% on average, compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for the sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Background To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria. Methods A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling. Results The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02). Conclusions AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Background. To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.Methods. A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.Results. The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).Conclusions. AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

BackgroundPseudoprogression (PsPD) is a major diagnostic challenge in the follow-up of patients with glioblastoma (GB) after chemoradiotherapy (CRT). Conventional imaging signs and parameters derived from diffusion and perfusion-MRI have yet to prove their reliability in clinical practice for an accurate differential diagnosis. Here, we tested these parameters and combined them with radiomic features (RFs), clinical data, and MGMT promoter methylation status using machine- and deep-learning (DL) models to distinguish PsPD from Progressive disease.MethodsIn a single-center analysis, 105 patients with GB who developed a suspected imaging PsPD in the first 7 months after standard CRT were identified retrospectively. Imaging data included standard MRI anatomical sequences, apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) maps. Median values (ADC, nrCBV) and RFs (all sequences) were calculated from DL-based tumor segmentations. Generalized linear models with LASSO feature-selection and DL models were built integrating clinical data, MGMT methylation status, median ADC and nrCBV values and RFs.ResultsA model based on clinical data and MGMT methylation status yielded an areas under the receiver operating characteristic curve (AUC) = 0.69 (95% CI 0.55–0.83) for detecting PsPD, and the addition of median ADC and nrCBV values resulted in a nonsignificant increase in performance (AUC = 0.71, 95% CI 0.57–0.85, P = .416). Combining clinical/MGMT information with RFs derived from ADC, nrCBV, and from all available sequences both resulted in significantly (both P < .005) lower model performances, with AUC = 0.52 (0.38–0.66) and AUC = 0.54 (0.40–0.68), respectively. DL imaging models resulted in AUCs ≤ 0.56.ConclusionCurrently available imaging biomarkers could not reliably differentiate PsPD from true tumor progression in patients with glioblastoma; larger collaborative efforts are needed to build more reliable models.

BackgroundQuantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort.MethodsA total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman’s coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong’s test.ResultsPatients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7–21.7) vs 18.8 mL (IQR 9.4–33.9), p0.05).ConclusionAIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.

Background and Purpose: This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke. Methods: A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90. Results: Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727]; P =0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810]; P Conclusions: Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.

Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Background Relevance of antiangiogenic treatment with bevacizumab in patients with glioblastoma is controversial because progression-free survival benefit did not translate into an overall survival (OS) benefit in randomized phase III trials. Purpose To perform longitudinal characterization of intratumoral angiogenesis and oxygenation by using dynamic susceptibility contrast agent-enhanced (DSC) MRI and evaluate its potential for predicting outcome from administration of bevacizumab. Materials and Methods In this secondary analysis of the prospective randomized phase II/III European Organization for Research and Treatment of Cancer 26101 trial conducted between October 2011 and December 2015 in 596 patients with first recurrence of glioblastoma, the subset of patients with availability of anatomic MRI and DSC MRI at baseline and first follow-up was analyzed. Patients were allocated into those administered bevacizumab (hereafter, the BEV group; either bevacizumab monotherapy or bevacizumab with lomustine) and those not administered bevacizumab (hereafter, the non-BEV group with lomustine monotherapy). Contrast-enhanced tumor volume, noncontrast-enhanced T2 fluid-attenuated inversion recovery (FLAIR) signal abnormality volume, Gaussian-normalized relative cerebral blood volume (nrCBV), Gaussian-normalized relative blood flow (nrCBF), and tumor metabolic rate of oxygen (nTMRO2) was quantified. The predictive ability of these imaging parameters was assessed with multivariable Cox regression and formal interaction testing. Results A total of 254 of 596 patients were evaluated (mean age, 57 years ± 11; 155 men; 161 in the BEV group and 93 in non-BEV group). Progression-free survival was longer in the BEV group (3.7 months; 95% confidence interval [CI]: 3.0, 4.2) compared with the non-BEV group (2.5 months; 95% CI: 1.5, 2.9; P = .01), whereas OS was not different (P = .15). The nrCBV decreased for the BEV group (-16.3%; interquartile range [IQR], -39.5% to 12.0%; P = .01), but not for the non-BEV group (1.2%; IQR, -17.9% to 23.3%; P = .19) between baseline and first follow-up. An identical pattern was observed for both nrCBF and nTMRO2 values. Contrast-enhanced tumor and noncontrast-enhanced T2 FLAIR signal abnormality volumes decreased for the BEV group (-66% [IQR, -83% to -35%] and -33% [IQR, -71% to -5%], respectively; P < .001 for both), whereas they increased for the non-BEV group (30% [IQR, -17% to 98%], P = .001; and 10% [IQR, -13% to 82%], P = .02, respectively) between baseline and first follow-up. None of the assessed MRI parameters were predictive for OS in the BEV group. Conclusion Bevacizumab treatment decreased tumor volumes, angiogenesis, and oxygenation, thereby reflecting its effectiveness for extending progression-free survival; however, these parameters were not predictive of overall survival (OS), which highlighted the challenges of identifying patients that derive an OS benefit from bevacizumab. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Dillon in this issue.

Background Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset. Conclusion The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

Background Magnetic resonance imaging is essential for monitoring people with multiple sclerosis, but the diagnostic value of gadolinium contrast administration in spine magnetic resonance imaging is unclear. Objective To assess the diagnostic value of gadolinium contrast administration in spine magnetic resonance imaging follow-up examinations and identify imaging markers correlating with lesion enhancement. Methods A total of 65 multiple sclerosis patients with at least 2 spinal magnetic resonance imaging follow-up examinations were included. Spine magnetic resonance imaging was performed at 3 Tesla with a standardized protocol (sagittal and axial T2-weighted turbo spin echo and T1-weighted post-contrast sequences). T2 lesion load and enhancing lesions were assessed by two independent neuroradiologists for lesion size, localization, and T2 signal ratio (T2 signallesion/T2 signalnormal appearing spinal cord). Results A total of 68 new spinal T2 lesions and 20 new contrast-enhancing lesions developed during follow-up. All enhancing lesions had a discernable correlate as a new T2 lesion. Lesion enhancement correlated with a higher T2 signal ratio compared to non-enhancing lesions (T2 signal ratio: 2.0 ± 0.4 vs. 1.4 ± 0.2, **** p < 0.001). Receiver operating characteristics analysis showed an optimal cutoff value of signal ratio 1.78 to predict lesion enhancement (82% sensitivity and 97% specificity). Conclusion Gadolinium contrast administration is dispensable in follow-up spine magnetic resonance imaging if no new T2 lesions are present. Probability of enhancement correlates with the T2 signal ratio.

BACKGROUND Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm$^{3}$ (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm$^{3}$ (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p

Summary Background The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. Methods In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial ( NCT01290939 ), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). Findings For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86–0·90], and for NEs 0·93 [0·92–0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90–0·92], NEs 0·93 [0·93–0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86–3·60] vs central RANO 2·07 [1·46–2·92]; p Interpretation Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. Funding Medical Faculty Heidelberg Postdoc-Program, Else Kroner-Fresenius Foundation.

Drivers of glioblastoma progression include the immunosuppressive tumor microenvironment (TME), dominated by tumor-associated myeloid cells. Therefore, we investigated a new approach targeting the myeloid compartment to reprogram myeloid cells in the TME using a β-cyclodextrin nanoparticle (CDNP) formulation encapsulating the toll-like receptor 7 and 8 (TLR7/8) agonist R848. Biodistribution confirmed specific targeting of CDNP-R848 to tumor-associated macrophages (TAMs) (labeling efficiency: 34.0% ± 22.2%), whereas tumor microglia (5.4% ± 4.4%) and splenic macrophages (13.2% ± 0.7%) revealed less uptake. Interestingly, intravenous application of CDNP-R848 induced strong tumor regression with an overall response rate of 80% (2.5% complete response, 52.5% partial response and 25% stable disease, n=40 mice) in Gl261 syngeneic experimental gliomas, while CDNP vehicle treated animals showed exponential tumor growth (100% progressive disease, n=12 mice). As advanced imaging is essential to monitor intracranial disease and possibly predict response and resistance, we performed high resolution magnetic resonance imaging using ultrasmall iron oxide nanoparticles (USPIO) for macrophage tracking. Increased levels of USPIO uptake in vehicle treated animals compared to CDNP-R848 treated animals were found as an early marker of responding mice (ΔT2*: -11.7 ± 4.2 vs -4.0 ± 2.8 ms, p=0.01). This correlated with an increased influx of myeloid cells into the TME of vehicle treated animals and showed a strong correlation of macrophage recruitment and USPIO uptake (R2: 0.78, p=0.004). Mechanistically, phenotyping of macrophages (CD45high/CD11b+) indicated a pro-inflammatory shift of TAMs with an increased infiltration of pro-inflammatory F4/80+/MHCII+ macrophages during CDNP-R848 treatment. Surprisingly, the anti-tumor effect of CDNP-R848 was independent of CD8+ T cells, CD4+ T cells or NK cells during selective depletion experiments. In summary, this work demonstrates the ability of myeloid-targeted therapies to re-shape the tumor microenvironment for an effective immunotherapy of glioma.

Background This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma. Methods Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed. Results The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s kappa = 0.75 [95% CI, 0.57–0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005). Conclusions This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

In this work we optimized a novel approach for combining in vivo MRI and ex vivo high-resolution fluorescence microscopy that involves: (i) a method for slicing rat brain tissue into sections with the same thickness and spatial orientation as in in vivo MRI, to better correlate in vivo MRI analyses with ex-vivo imaging via scanning confocal microscope and (ii) an improved clearing protocol compatible with lipophilic dyes that highlight the neurovascular network, to obtain high tissue transparency while preserving tissue staining and morphology with no significant tissue shrinkage or expansion. We applied this methodology in two rat models of glioblastoma (GBM; U87 human glioma cells and patient-derived human glioblastoma cancer stem cells) to demonstrate how vital the information retrieved from the correlation between MRI and confocal images is and to highlight how the increased invasiveness of xenografts derived from cancer stem cells may not be clearly detected by standard in vivo MRI approaches. The protocol studied in this work could be implemented in pre-clinical GBM research to further the development and validation of more predictive and translatable MR imaging protocols that can be used as critical diagnostic and prognostic tools. The development of this protocol is part of the quest for more efficacious treatment approaches for this devastating and still uncurable disease. In particular, this approach could be instrumental in validating novel MRI-based techniques to assess cellular infiltration beyond the macroscopic tumor margins and to quantify neo-angiogenesis.

Glioblastoma (GBM) is the most malignant brain tumor of adults and is characterized by extensive cell dissemination within the brain parenchyma and enhanced angiogenesis. Effective preclinical modeling of these key features suffers from several shortcomings. Aim of this study was to determine whether modulating the expression of extracellular matrix (ECM) modifiers in proneural (PN) and mesenchymal (MES) cancer stem cells (CSCs) and in conventional glioma cell lines (GCLs) might improve tumor invasion and vascularization. To this end, we selected secreted, acidic and rich in cysteine-like 1 (SPARCL1) as a potential mediator of ECM remodeling in GBM. SPARCL1 transcript and protein expression was assessed in PN and MES CSCs as well as GCLs, in their xenografts and in patient-derived specimens by qPCR, WB and IHC. SPARCL1 expression was then enforced in both CSCs and GCLs by lentiviral-based transduction. The effect of SPARCL1 gain-of-function on microvascular proliferation, microglia activation and advanced imaging features was tested in intracranial xenografts by IHC and MRI and validated by chorioallantoic membrane (CAM) assays. SPARCL1 expression significantly enhanced the infiltrative and neoangiogenic features of PN and MES CSC/GCL-induced tumors, with the concomitant activation of inflammatory responses associated with the tumor microenvironment, thus resulting in experimental GBMs that reproduced both the parenchymal infiltration and the increased microvascular density, typical of GBM. Overall, these results indicate that SPARCL1 overexpression might be instrumental for the generation of CSC-derived preclinical models of GBM in which the main pathognomonic hallmarks of GBMs are retrievable, making them suitable for effective preclinical testing of therapeutics.

Background This study aims to evaluate the impact of tumor location on key molecular alterations on a single voxel level in patients with newly diagnosed glioma. Methods A consecutive series of n = 237 patients with newly diagnosed glioblastoma and n = 131 patients with lower-grade glioma was analyzed. Volumetric tumor segmentation was performed on preoperative MRI with a semi-automated approach and images were registered to the standard Montreal Neurological Institute 152 space. Using a voxel-based lesion symptom mapping (VLSM) analysis, we identified specific brain regions that were associated with tumor-specific molecular alterations. We assessed a predefined set of n = 17 molecular characteristics in the glioblastoma cohort and n = 2 molecular characteristics in the lower-grade glioma cohort. Permutation adjustment (n = 1000 iterations) was used to correct for multiple testing, and voxel t-values that were greater than the t-value in >95% of the permutations were retained in the VLSM results (α = 0.05, power > 0.8). Results Tumor location predilection for isocitrate dehydrogenase (IDH) mutant tumors was found in both glioblastoma and lower-grade glioma cohorts, each showing a concordant predominance in the frontal lobe adjacent to the rostral extension of the lateral ventricles (permutation-adjusted P = 0.021 for the glioblastoma and 0.013 for the lower-grade glioma cohort). Apart from that, the VLSM analysis did not reveal a significant association of the tumor location with any other key molecular alteration in both cohorts (permutation-adjusted P > 0.05 each). Conclusion Our study highlights the unique properties of IDH mutations and underpins the hypothesis that the rostral extension of the lateral ventricles is a potential location for the cell of origin in IDH-mutant gliomas.

Intrinsic malignant brain tumors, such as glioblastomas are frequently resistant to immune checkpoint blockade (ICB) with few hypermutated glioblastomas showing response. Modeling patient-individual resistance is challenging due to the lack of predictive biomarkers and limited accessibility of tissue for serial biopsies. Here, we investigate resistance mechanisms to anti-PD-1 and anti-CTLA-4 therapy in syngeneic hypermutated experimental gliomas and show a clear dichotomy and acquired immune heterogeneity in ICB-responder and non-responder tumors. We made use of this dichotomy to establish a radiomic signature predicting tumor regression after pseudoprogression induced by ICB therapy based on serial magnetic resonance imaging. We provide evidence that macrophage-driven ICB resistance is established by CD4 T cell suppression and Treg expansion in the tumor microenvironment via the PD-L1/PD-1/CD80 axis. These findings uncover an unexpected heterogeneity of response to ICB in strictly syngeneic tumors and provide a rationale for targeting PD-L1-expressing tumor-associated macrophages to overcome resistance to ICB., Modeling patient-individual resistance to immunotherapy is challenging. Here, the authors use a syngeneic experimental hypermutated orthotopic glioma model to define radiological and biological features that can predict or explain the mechanistic differences between responders and non-responders to immunotherapy.

Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data., Fabian Isensee, Marianne Schell and Irada Tursunova share the first authorship

Achaete-scute homolog 1 gene (ASCL1) is a gene classifier for the proneural (PN) transcriptional subgroup of glioblastoma (GBM) that has a relevant role in the neuronal-like differentiation of GBM cancer stem cells (CSCs) through the activation of a PN gene signature. Besides prototypical ASCL1 PN target genes, the molecular effectors mediating ASCL1 function in regulating GBM differentiation and, most relevantly, subgroup specification are currently unknown. Here we report that ASCL1 not only promotes the acquisition of a PN phenotype in CSCs by inducing a glial-to-neuronal lineage switch but also concomitantly represses mesenchymal (MES) features by directly downregulating the expression of N-Myc downstream-regulated gene 1 (NDRG1), which we propose as a novel gene classifier of MES GBMs. Increasing the expression of ASCL1 in PN CSCs results in suppression of self-renewal, promotion of differentiation and, most significantly, decrease in tumorigenesis, which is also reproduced by NDRG1 silencing. Conversely, both abrogation of ASCL1 expression in PN CSCs and enforcement of NDRG1 expression in either PN or MES CSCs induce proneural-to-mesenchymal transition (PMT) and enhanced mesenchymal features. Surprisingly, ASCL1 overexpression in MES CSCs increases malignant features and gives rise to a neuroendocrine-like secretory phenotype. Altogether, our results propose that the fine interplay between ASCL1 and its target NDRG1 might serve as potential subgroup-specific targetable vulnerability in GBM; enhancing ASCL1 expression in PN GBMs might reduce tumorigenesis, whereas repressing NDRG1 expression might be actionable to hamper the malignancy of GBM belonging to the MES subgroup.

BackgroundWe studied the effects of endovascular treatment (EVT) and the impact of the extent of recanalization on cerebral perfusion and oxygenation parameters in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO).MethodsForty-seven patients with anterior LVO underwent computed tomography perfusion (CTP) before and immediately after EVT. The entire ischemic region (Tmax >6 s) was segmented before intervention, and tissue perfusion (time-to-maximum (Tmax), time-to-peak (TTP), mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF)) and oxygenation (coefficient of variation (COV), capillary transit time heterogeneity (CTH), metabolic rate of oxygen (CMRO2), oxygen extraction fraction (OEF)) parameters were quantified from the segmented area at baseline and the corresponding area immediately after intervention, as well as within the ischemic core and penumbra. The impact of the extent of recanalization (modified Treatment in Cerebral Infarction (mTICI)) on CTP parameters was assessed with the Wilcoxon test and Pearson’s correlation coefficients.ResultsThe Tmax, MTT, OEF and CTH values immediately after EVT were lower in patients with complete (as compared with incomplete) recanalization, whereas CBF and COV values were higher (PConclusionsOur study demonstrates that the ischemic penumbra is the key target of successful EVT in patients with AIS and largely determines its efficacy on a tissue level. Furthermore, we confirm the validity of the mTICI score as a surrogate parameter of interventional success on a tissue perfusion level.

Epidermal growth factor receptor variant III is a common mutation in glioblastoma, found in approximately 25% of tumors. Epidermal growth factor receptor variant III may accelerate angiogenesis in malignant gliomas. We correlated T1-weighted dynamic contrast-enhanced MR imaging perfusion parameters with epidermal growth factor receptor variant III status.Eighty-two consecutive patients with glioblastoma and known epidermal growth factor receptor variant III status who had dynamic contrast-enhanced MR imaging before surgery were evaluated. Volumes of interest were drawn around the entire enhancing tumor on contrast T1-weighted images and then were transferred onto coregistered dynamic contrast-enhanced MR imaging perfusion maps. Histogram analysis with normalization was performed to determine the relative mean, 75th percentile, and 90th percentile values for plasma volume and contrast transfer coefficient. A Wilcoxon rank sum test was applied to assess the relationship between baseline perfusion parameters and positive epidermal growth factor receptor variant III status. The receiver operating characteristic method was used to select the cutoffs of the dynamic contrast-enhanced MR imaging perfusion parameters.Increased relative plasma volume and increased relative contrast transfer coefficient parameters were both significantly associated with positive epidermal growth factor receptor variant III status. For epidermal growth factor receptor variant III-positive tumors, relative plasma volume mean was 9.3 and relative contrast transfer coefficient mean was 6.5; for epidermal growth factor receptor variant III-negative tumors, relative plasma volume mean was 3.6 and relative contrast transfer coefficient mean was 3.7 (relative plasma volume mean, P.001, and relative contrast transfer coefficient mean, P = .008). The predictive powers of relative plasma volume histogram metrics outperformed those of the relative contrast transfer coefficient histogram metrics (P= .004).Dynamic contrast-enhanced MR imaging shows greater perfusion and leakiness in epidermal growth factor receptor variant III-positive glioblastomas than in epidermal growth factor receptor variant III-negative glioblastomas, consistent with the known effect of epidermal growth factor receptor variant III on angiogenesis. Quantitative evaluation of dynamic contrast-enhanced MR imaging may be useful as a noninvasive tool for correlating epidermal growth factor receptor variant III expression and related tumor neoangiogenesis. This potential may have implications for monitoring response to epidermal growth factor receptor variant III-targeted therapies.

OBJECTIVE To assess the validity and pathophysiology of the T2/FLAIR mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted glioma. METHODS MRI scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade glioma and 295 glioblastoma) were evaluated for the presence of a T2/FLAIR-mismatch sign (defined as complete/near-complete hyperintense signal on T2w, simultaneous hypointense signal on FLAIR except for a hyperintense peripheral rim) by two independent reviewers. Sensitivity, specificity, accuracy, positive and negative predictive value (PPV, NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of spatial differences in ADC and rCBV values comparing the FLAIR-hypointense core vs. hyperintense rim in cases with presence of a T2/FLAIR-mismatch sign was performed. RESULTS There was substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s Kappa = 0.75 [95% CI 0.57–0.93]). The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them were IDH-mutant, 1p/19q non-codeleted tumors (sensitivity=10.9%, specificity=100%, PPV=100%, NPV=3.0%, accuracy=13.3%). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, especially not in any of the IDH-mutant glioblastoma cases (n=5). In tumors with T2/FLAIR-mismatch sign the ADC values were significantly lower in the rim as compared to the core (p=0.0005) whereas there was no difference in rCBV values (p=0.4258). CONCLUSION This study confirms the high specificity of the T2/FLAIR-mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted gliomas, although sensitivity is low and applicability is limited to lower-grade gliomas. The identified spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflects differences in tumor cellularity and microenvironment.

PURPOSE Spinocerebellar ataxias type 2 and 28 (SCA2, SCA28) are autosomal dominant disorders characterized by progressive cerebellar and oculomotor abnormalities. We aimed to investigate cerebellar, brainstem, and extraocular muscle involvement in the mitochondrial SCA28 disease compared with SCA2. METHODS We obtained orbital and brain 1.5 T-magnetic resonance images (MRI) in eight SCA28 subjects, nine SCA2, and nine age-matched healthy subjects. Automated segmentation of cerebellum and frontal lobe was performed using Freesurfer software. Manual segmentations for midbrain, pons, and extraocular muscles were performed using OsiriX. RESULTS Eye movement abnormalities in SCA2 subjects were characterized by slow horizontal saccades. Subjects with SCA28 variably presented hypometric saccades, saccadic horizontal pursuit, impaired horizontal gaze holding, and superior eyelid ptosis. Quantitative brain MRI demonstrated that cerebellar and pons volumes were significantly reduced in both SCA2 and SCA28 subjects compared with controls (P < 0.03), and in SCA2 subjects compared with SCA28 (P < 0.01). Midbrain and frontal lobe volumes were also significantly reduced in SCA2 compared to controls (P < 0.03), whereas these volumes did not differ between SCA2 and SCA28 and between SCA28 and control subjects. The extraocular muscle areas were 37% to 48% smaller in SCA28 subjects compared with controls (P < 0.002), and 14% to 36% smaller compared with SCA2 subjects (P < 0.03). Extraocular muscle areas did not differ between SCA2 and controls. CONCLUSIONS Our MRI findings support the hypothesis of different cerebellar and extraocular myopathic contributions in the eye movement abnormalities in SCA2 and SCA28 diseases. In SCA28, a myopathic defect selectively involving the extraocular muscles supports a specific impairment of mitochondrial energy metabolism.

Adrenomyeloneuropathy is the late-onset form of X-linked adrenoleukodystrophy, and is considered the most frequent metabolic hereditary spastic paraplegia. In adrenomyeloneuropathy the spinal cord is the main site of pathology. Differently from quantitative magnetic resonance imaging of the brain, little is known about the feasibility and utility of advanced neuroimaging in quantifying the spinal cord abnormalities in hereditary diseases. Moreover, little is known about the subtle pathological changes that can characterize the brain of adrenomyeloneuropathy subjects in the early stages of the disease. We performed a cross-sectional study on 13 patients with adrenomyeloneuropathy and 12 age-matched healthy control subjects who underwent quantitative magnetic resonance imaging to assess the structural changes of the upper spinal cord and brain. Total cord areas from C2-3 to T2-3 level were measured, and diffusion tensor imaging metrics, i.e. fractional anisotropy, mean, axial and radial diffusivity values were calculated in both grey and white matter of spinal cord. In the brain, grey matter regions were parcellated with Freesurfer and average volume and thickness, and mean diffusivity and fractional anisotropy from co-registered diffusion maps were calculated in each region. Brain white matter diffusion tensor imaging metrics were assessed using whole-brain tract-based spatial statistics, and tractography-based analysis on corticospinal tracts. Correlations among clinical, structural and diffusion tensor imaging measures were calculated. In patients total cord area was reduced by 26.3% to 40.2% at all tested levels (P < 0.0001). A mean 16% reduction of spinal cord white matter fractional anisotropy (P