Losartan controls immune checkpoint blocker-induced edema and improves survival in glioblastoma mouse models.

Immune checkpoint blockers (ICBs) have failed in all phase III glioblastoma trials. Here, we found that ICBs induce cerebral edema in some patients and mice with glioblastoma. Through single-cell RNA sequencing, intravital imaging, and CD8+ T cell blocking studies in mice, we demonstrated that this edema results from an inflammatory response following antiprogrammed death 1 (PD1) antibody treatment that disrupts the blood-tumor barrier. Used in lieu of immunosuppressive corticosteroids, the angiotensin receptor blocker losartan prevented this ICB-induced edema and reprogrammed the tumor microenvironment, curing 20% of mice which increased to 40% in combination with standard of care treatment. Using a bihemispheric tumor model, we identified a "hot" tumor immune signature prior to losartan+anti-PD1 therapy that predicted long-term survival. Our findings provide the rationale and associated biomarkers to test losartan with ICBs in glioblastoma patients.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Interpreting deep learning models for glioma survival classification using visualization and textual explanations.

BACKGROUND: Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency map. This study proposes to enhance the interpretability of saliency-based deep learning model for survival classification of patients with gliomas, by extracting domain knowledge-based information from the raw saliency maps. MATERIALS AND METHODS: Our study includes presurgical T1-weighted (pre- and post-contrast), T2-weighted and T2-FLAIR MRIs of 147 glioma patients from the BraTs 2020 challenge dataset aligned to the SRI 24 anatomical atlas. Each image exam includes a segmentation mask and the information of overall survival (OS) from time of diagnosis (in days). This dataset was divided into training ([Formula: see text]) and validation ([Formula: see text]) datasets. The extent of surgical resection for all patients was gross total resection. We categorized the data into 42 short (mean [Formula: see text] days), 30 medium ([Formula: see text] days), and 46 long ([Formula: see text] days) survivors. A 3D convolutional neural network (CNN) trained on brain tumour MRI volumes classified all patients based on expected prognosis of either short-term, medium-term, or long-term survival. We extend the popular 2D Gradient-weighted Class Activation Mapping (Grad-CAM), for the generation of saliency map, to 3D and combined it with the anatomical atlas, to extract brain regions, brain volume and probability map that reveal domain knowledge-based information. RESULTS: For each OS class, a larger tumor volume was associated with a shorter OS. There were 10, 7 and 27 tumor locations in brain regions that uniquely associate with the short-term, medium-term, and long-term survival, respectively. Tumors located in the transverse temporal gyrus, fusiform, and palladium are associated with short, medium and long-term survival, respectively. The visual and textual information displayed during OS prediction highlights tumor location and the contribution of different brain regions to the prediction of OS. This algorithm design feature assists the physician in analyzing and understanding different model prediction stages. CONCLUSIONS: Domain knowledge-based information extracted from the saliency map can enhance the interpretability of deep learning models. Our findings show that tumors overlapping eloquent brain regions are associated with short patient survival.

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue.

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as 'chemo fog'. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research.

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma.

INTRODUCTION: IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. PURPOSE: To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. MATERIALS AND METHODS: Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS ≥ 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). RESULTS: For LTSs, we found a significant association between a moderate value of rCBVmean and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBVmean and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. CONCLUSION: We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBVmean at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.

Robustness of MR Elastography in the Healthy Brain: Repeatability, Reliability, and Effect of Different Reconstruction Methods.

BACKGROUND: Changes in brain stiffness can be an important biomarker for neurological disease. Magnetic resonance elastography (MRE) quantifies tissue stiffness, but the results vary between acquisition and reconstruction methods. PURPOSE: To measure MRE repeatability and estimate the effect of different reconstruction methods and varying data quality on estimated brain stiffness. STUDY TYPE: Prospective. SUBJECTS: Fifteen healthy subjects. FIELD STRENGTH/SEQUENCE: 3T MRI, gradient-echo elastography sequence with a 50 Hz vibration frequency. ASSESSMENT: Imaging was performed twice in each subject. Images were reconstructed using a curl-based and a finite-element-model (FEM)-based method. Stiffness was measured in the whole brain, in white matter, and in four cortical and four deep gray matter regions. Repeatability coefficients (RC), intraclass correlation coefficients (ICC), and coefficients of variation (CV) were calculated. MRE data quality was quantified by the ratio between shear waves and compressional waves. STATISTICAL TESTS: Median values with range are presented. Reconstruction methods were compared using paired Wilcoxon signed-rank tests, and Spearman's rank correlation was calculated between MRE data quality and stiffness. Holm-Bonferroni corrections were employed to adjust for multiple comparisons. RESULTS: In the whole brain, CV was 4.3% and 3.8% for the curl and the FEM reconstruction, respectively, with 4.0-12.8% for subregions. Whole-brain ICC was 0.60-0.74, ranging from 0.20 to 0.89 in different regions. RC for the whole brain was 0.14 kPa and 0.17 kPa for the curl and FEM methods, respectively. FEM reconstruction resulted in 39% higher stiffness than the curl reconstruction (P < 0.05). MRE data quality, defined as shear-compression wave ratio, was higher in peripheral regions than in central regions of the brain (P < 0.05). No significant correlations were observed between MRE data quality and stiffness estimates. DATA CONCLUSION: MRE of the human brain is a robust technique in terms of repeatability. Caution is warranted when comparing stiffness values obtained with different techniques. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas.

OBJECTIVES: To assess the combined role of tumor vascularity, estimated from perfusion MRI, and MGMT methylation status on overall survival (OS) in patients with glioblastoma. METHODS: A multicentric international dataset including 96 patients from NCT03439332 clinical study were used to study the prognostic relationships between MGMT and perfusion markers. Relative cerebral blood volume (rCBV) in the most vascularized tumor regions was automatically obtained from preoperative MRIs using ONCOhabitats online analysis service. Cox survival regression models and stratification strategies were conducted to define a subpopulation that is particularly favored by MGMT methylation in terms of OS. RESULTS: rCBV distributions did not differ significantly (p > 0.05) in the methylated and the non-methylated subpopulations. In patients with moderately vascularized tumors (rCBV < 10.73), MGMT methylation was a positive predictive factor for OS (HR = 2.73, p = 0.003, AUC = 0.70). In patients with highly vascularized tumors (rCBV > 10.73), however, there was no significant effect of MGMT methylation (HR = 1.72, p = 0.10, AUC = 0.56). CONCLUSIONS: Our results indicate the existence of complementary prognostic information provided by MGMT methylation and rCBV. Perfusion markers could identify a subpopulation of patients who will benefit the most from MGMT methylation. Not considering this information may lead to bias in the interpretation of clinical studies. KEY POINTS: • MRI perfusion provides complementary prognostic information to MGMT methylation. • MGMT methylation improves prognosis in glioblastoma patients with moderate vascular profile. • Failure to consider these relations may lead to bias in the interpretation of clinical studies.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma.

Purpose: There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network "Glioma MR Imaging 2.0" (GliMR) which we present in this article. Methods: GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current "state-of-the-art" in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019. Results: GliMR's first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website. Conclusion: The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Phase II trial of carboplatin and bevacizumab in patients with breast cancer brain metastases.

BACKGROUND: We aimed to examine the safety and efficacy of bevacizumab and carboplatin in patients with breast cancer brain metastases. METHODS: We enrolled patients with breast cancer and > 1 measurable new or progressive brain metastasis. Patients received bevacizumab 15 mg/kg intravenously (IV) on cycle 1 day 1 and carboplatin IV AUC = 5 on cycle 1 day 8. Patients with HER2-positive disease also received trastuzumab. In subsequent cycles, all drugs were administered on day 1 of each cycle. Contrast-enhanced brain MRI was performed at baseline, 24-96 h after the first bevacizumab dose (day + 1), and every 2 cycles. The primary endpoint was objective response rate in the central nervous system (CNS ORR) by composite criteria. Associations between germline VEGF single nucleotide polymorphisms (rs699947, rs2019063, rs1570360, rs833061) and progression-free survival (PFS) and overall survival (OS) were explored, as were associations between early (day + 1) MRI changes and outcomes. RESULTS: Thirty-eight patients were enrolled (29 HER2-positive, 9 HER2-negative); all were evaluable for response. The CNS ORR was 63% (95% CI, 46-78). Median PFS was 5.62 months and median OS was 14.10 months. As compared with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0, patients with ECOG PS 1-2 had significantly worse PFS and OS (all P < 0.01). No significant associations between VEGF genotypes or early MRI changes and clinical outcomes were observed. CONCLUSIONS: The combination of bevacizumab and carboplatin results in a high rate of durable objective response in patients with brain metastases from breast cancer. This regimen warrants further investigation. TRIAL REGISTRATION: NCT01004172 . Registered 28 October 2009.

Blood Oxygen Level-Dependent MRI of the Myocardium with Multiecho Gradient-Echo Spin-Echo Imaging.

Background Myocardial oxygenation imaging could help determine the presence of microvascular dysfunction associated with increased cardiovascular risk. However, it is challenging to depict the potentially small oxygenation alterations with current noninvasive cardiac MRI blood oxygen level-dependent (BOLD) techniques. Purpose To demonstrate the cardiac application of a gradient-echo spin-echo (GESE) echo-planar imaging sequence for dynamic and quantitative heartbeat-to-heartbeat BOLD MRI and evaluate the sequence in populations both healthy and with hypertension in combination with a breath hold-induced CO2 intervention. Materials and Methods GESE echo-planar imaging sequence was performed in 18 healthy participants and in eight prospectively recruited participants with hypertension on a 3.0-T MRI system. T2 and T2* maps were calculated per heartbeat with a four-parameter fitting technique. Septal regions of interests were used to determine T2 and T2* values per heartbeat and examined over the course of a breath hold to determine BOLD changes. T2 and T2* changes of healthy participants and participants with hypertension were compared by using a nonparametric Mann-Whitney test. Results GESE echo-planar imaging approach gave spatially stable T2 and T2* maps per heartbeat for healthy participants and participants with hypertension, with mean T2 values of 43 msec ± 5 (standard deviation) and 46 msec ± 9, respectively, and mean T2* values of 28 msec ± 5 and 22 msec ± 5, respectively. The healthy participants exhibited increasing T2 and T2* values over the course of a breath hold with a mean positive slope of 0.2 msec per heartbeat ± 0.1 for T2 and 0.2 msec per heartbeat ± 0.1 for T2*, whereas for participants with hypertension these dynamic T2 and T2* values had a mean negative slope of -0.2 msec per heartbeat ± 0.2 for T2 and -0.1 msec per heartbeat ± 0.2 for T2*. The difference in these mean slopes between healthy participants and participants with hypertension was significant for both T2 (P < .001) and T2* (P < .001). Conclusion Gradient-echo spin-echo echo-planar imaging sequence provided quantitative T2 and T2* maps per heartbeat and enabled dynamic heartbeat-to-heartbeat blood oxygen level-dependent (BOLD)-response imaging by analyzing changes in T2 and T2* over the time of a breath-hold intervention. This approach could identify differences in the BOLD response between healthy participants and participants with hypertension. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Friedrich in this issue.

Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study.

BACKGROUND: Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a heterogeneous and abnormal vascularity. Subtypes of vascular habitats within the tumor and edema can be distinguished: high angiogenic tumor (HAT), low angiogenic tumor (LAT), infiltrated peripheral edema (IPE), and vasogenic peripheral edema (VPE). PURPOSE: To validate the association between hemodynamic markers from vascular habitats and overall survival (OS) in glioblastoma patients, considering the intercenter variability of acquisition protocols. STUDY TYPE: Multicenter retrospective study. POPULATION: In all, 184 glioblastoma patients from seven European centers participating in the NCT03439332 clinical study. FIELD STRENGTH/SEQUENCE: 1.5T (for 54 patients) or 3.0T (for 130 patients). Pregadolinium and postgadolinium-based contrast agent-enhanced T1 -weighted MRI, T2 - and FLAIR T2 -weighted, and dynamic susceptibility contrast (DSC) T2 * perfusion. ASSESSMENT: We analyzed preoperative MRIs to establish the association between the maximum relative cerebral blood volume (rCBVmax ) at each habitat with OS. Moreover, the stratification capabilities of the markers to divide patients into "vascular" groups were tested. The variability in the markers between individual centers was also assessed. STATISTICAL TESTS: Uniparametric Cox regression; Kaplan-Meier test; Mann-Whitney test. RESULTS: The rCBVmax derived from the HAT, LAT, and IPE habitats were significantly associated with patient OS (P < 0.05; hazard ratio [HR]: 1.05, 1.11, 1.28, respectively). Moreover, these markers can stratify patients into "moderate-" and "high-vascular" groups (P < 0.05). The Mann-Whitney test did not find significant differences among most of the centers in markers (HAT: P = 0.02-0.685; LAT: P = 0.010-0.769; IPE: P = 0.093-0.939; VPE: P = 0.016-1.000). DATA CONCLUSION: The rCBVmax calculated in HAT, LAT, and IPE habitats have been validated as clinically relevant prognostic biomarkers for glioblastoma patients in the pretreatment stage. This study demonstrates the robustness of the hemodynamic tissue signature (HTS) habitats to assess the GBM vascular heterogeneity and their association with patient prognosis independently of intercenter variability. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1478-1486.

Phase II study of tivozanib, an oral VEGFR inhibitor, in patients with recurrent glioblastoma.

Targeting tumor angiogenesis is a potential therapeutic strategy for glioblastoma because of its high vascularization. Tivozanib is an oral pan-VEGF receptor tyrosine kinase inhibitor that hits a central pathway in glioblastoma angiogenesis. We conducted a phase II study to test the effectiveness of tivozanib in patients with recurrent glioblastoma. Ten adult patients were enrolled and treated with tivozanib 1.5 mg daily, 3 weeks on/1 week off in 28-day cycles. Brain MRI and blood biomarkers of angiogenesis were performed at baseline, within 24-72 h of treatment initiation, and monthly thereafter. Dynamic contrast enhanced MRI, dynamic susceptibility contrast MRI, and vessel architecture imaging were used to assess vascular effects. Resting state MRI was used to assess brain connectivity. Best RANO criteria responses were: 1 complete response, 1 partial response, 4 stable diseases, and 4 progressive disease (PD). Two patients were taken off study for toxicity and 8 patients were taken off study for PD. Median progression-free survival was 2.3 months and median overall survival was 8.1 months. Baseline abnormal tumor vascular permeability, blood flow, tissue oxygenation and plasma sVEGFR2 significantly decreased and plasma PlGF and VEGF increased after treatment, suggesting an anti-angiogenic effect of tivozanib. However, there were no clear structural changes in vasculature as vessel caliber and enhancing tumor volume did not significantly change. Despite functional changes in tumor vasculature, tivozanib had limited anti-tumor activity, highlighting the limitations of anti-VEGF monotherapy. Future studies in glioblastoma should leverage the anti-vascular activity of agents targeting VEGF to enhance the activity of other therapies.

Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation.

Antiangiogenic therapy has shown clear activity and improved survival benefit for certain tumor types. However, an incomplete understanding of the mechanisms of action of antiangiogenic agents has hindered optimization and broader application of this new therapeutic modality. In particular, the impact of antiangiogenic therapy on tumor blood flow and oxygenation status (i.e., the role of vessel pruning versus normalization) remains controversial. This controversy has become critical as multiple phase III trials of anti-VEGF agents combined with cytotoxics failed to show overall survival benefit in newly diagnosed glioblastoma (nGBM) patients and several other cancers. Here, we shed light on mechanisms of nGBM response to cediranib, a pan-VEGF receptor tyrosine kinase inhibitor, using MRI techniques and blood biomarkers in prospective phase II clinical trials of cediranib with chemoradiation vs. chemoradiation alone in nGBM patients. We demonstrate that improved perfusion occurs only in a subset of patients in cediranib-containing regimens, and is associated with improved overall survival in these nGBM patients. Moreover, an increase in perfusion is associated with improved tumor oxygenation status as well as with pharmacodynamic biomarkers, such as changes in plasma placenta growth factor and sVEGFR2. Finally, treatment resistance was associated with elevated plasma IL-8 and sVEGFR1 posttherapy. In conclusion, tumor perfusion changes after antiangiogenic therapy may distinguish responders vs. nonresponders early in the course of this expensive and potentially toxic form of therapy, and these results may provide new insight into the selection of glioblastoma patients most likely to benefit from anti-VEGF treatments.

A generic support vector machine model for preoperative glioma survival associations.

PURPOSE: To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of this model in autonomous patient data. MATERIALS AND METHODS: Institutional and regional medical ethics committees approved the study, and all patients signed a consent form. Two hundred thirty-five preoperative adult patients from two institutions with a subsequent histologically confirmed diagnosis of glioma after surgery were included retrospectively. An SVM learning technique was applied to MR imaging-based whole-tumor relative cerebral blood volume (rCBV) histograms. SVM models with the highest diagnostic accuracy for 6-month and 1-, 2-, and 3-year survival associations were trained on 101 patients from the first institution. With Cox survival analysis, the diagnostic effectiveness of the SVM models was tested on independent data from 134 patients at the second institution. RESULTS: were adjusted for known survival predictors, including patient age, tumor size, neurologic status, and postsurgery treatment, and were compared with survival associations from an expert reader. RESULTS: Compared with total qualitative assessment by an expert reader, the whole-tumor rCBV-based SVM model was the strongest parameter associated with 6-month and 1-, 2-, and 3-year survival in the independent patient data (area under the receiver operating characteristic curve, 0.794-0.851; hazard ratio, 5.4-21.2). DISCUSSION: Machine learning by means of SVM in combination with whole-tumor rCBV histogram analysis can be used to identify early patient survival in aggressive gliomas. The SVM model returned higher diagnostic accuracy values than an expert reader, and the model appears to be insensitive to patient, observer, and institutional variations.

Low incidence of pseudoprogression by imaging in newly diagnosed glioblastoma patients treated with cediranib in combination with chemoradiation.

BACKGROUND: Chemoradiation (CRT) can significantly modify the radiographic appearance of malignant gliomas, especially within the immediate post-CRT period. Pseudoprogression (PsP) is an increasingly recognized phenomenon in this setting, and is thought to be secondary to increased permeability as a byproduct of the complex process of radiation-induced tissue injury, possibly enhanced by temozolomide. We sought to determine whether the addition of a vascular endothelial growth factor (VEGF) signaling inhibitor (cediranib) to conventional CRT had an impact on the frequency of PsP, by comparing two groups of patients with newly diagnosed glioblastoma before, during, and after CRT. METHODS: All patients underwent serial magnetic resonance imaging as part of institutional review board-approved clinical studies. Eleven patients in the control group received only chemoradiation, whereas 29 patients in the study group received chemoradiation and cediranib until disease progression or toxicity. Response assessment was defined according to Response Assessment in Neuro-Oncology criteria, and patients with enlarging lesions were classified into true tumor progressions (TTP) or PsP, based on serial radiographic follow-up. RESULTS: Two patients in the study group (7%) showed signs of apparent early tumor progression, and both were subsequently classified as TTP. Six patients in the control group (54%) showed signs of apparent early tumor progression, and three were subsequently classified as TTP and three as PsP. The frequency of PsP was significantly higher in the control group. CONCLUSION: Administration of a VEGF inhibitor during and after CRT modifies the expression of PsP by imaging.

Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI.

PURPOSE: To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion-based dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI. MATERIALS AND METHODS: The study was approved by the Ethics Committee and informed consent was signed. Structural, diffusion- and perfusion-weighted MRI was performed at 1.5-T preoperatively in 94 adult patients (49 males, 45 females, 23-82 years; mean 51 years) later diagnosed with a primary glioma. Patients were randomly assigned in training and test datasets and the resulting DSC-based survival associations by SVM were compared to traditional MRI features including contrast-agent enhancement, perfusion- and diffusion-weighted imaging, tumor size, and location. The results were adjusted for age, neurological status, and postoperative factors associated with survival, including surgery and adjuvant therapy. RESULTS: For 1- (26/33 alive, 11/14 deceased), 2- (15/21, 21/26), 3- (12/16, 27/31) and 4- (12/15, 28/32) year survival associations in the test dataset (47 patients), the SVM routine was the only biomarker to consistently associate with survival (Cox; P < 0.001). CONCLUSION: The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.

Identifying the central sulcus in patients with intra-axial lesions: a multicenter study comparing conventional presurgical MRI to topographical analysis and BOLD-fMRI.

OBJECTIVES: Identification of eloquent brain areas in patients with intra-axial lesions is important to minimize the risk of neurological deficit. We performed a multicenter study comparing conventional 2-dimensional magnetic resonance imaging (MRI) for identification of the central sulcus to topographical MRI and blood-oxygenation-level-dependent functional MRI (BOLD-fMRI). METHODS: Seventy-seven unoperated patients with brain lesions were imaged at 1.5 or 3 T. The central sulcus was identified by an experienced neuroradiologist on 2-dimensional MRI, by topographic analysis of 3-dimensional MRI in BrainVoyager, and by BOLD-fMRI analysis in BrainVoyager or SPM5. RESULTS: The central sulcus in the affected hemisphere was readily identified in a significantly higher percentage of patients by 2-dimensional MRI and topographical analysis (77/77 patients) compared to BOLD-fMRI (57 patients; P < 0.001). The topographical analysis identified a significantly larger portion of the total central sulcus than 2-dimensional MRI (P < 0.05). No differences were found between institutions, histological versus radiological diagnoses, MRI sequence parameters, age, or sex. CONCLUSIONS: Identification of the central sulcus is best performed using topographical analysis; however, 2-dimensional analysis may suffice for daily routine work.

Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.