Purpose To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV) and maximum relative cerebral blood flow (rCBF) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV or rCBF at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities. RSNA, 2018.

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Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

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.

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.

Advanced MRI and molecular markers have been raised as crucial to improve prognostic models for patients having glioblastoma (GBM) lesions. In particular, different MR perfusion based markers describing vascular intrapatient heterogeneity have been correlated with tumor aggressiveness, and represent key information to understand tumor resistance against effective therapies of these neoplasms. Recently, hemodynamic tissue signature (HTS) markers based on MR perfusion images have been demonstrated to be useful for describing the heterogeneity of GBM at the voxel level, as well as demonstrating significant correlations with the patient's overall survival. In this work, we analyze the abilities of these markers to improve the conventional prognostic models based on clinical, morphological, and demographic features. Our results, in both the regression and classification tests, show that inclusion of the HTS markers improves the reliability of prognostic models. The HTS method is fully automatic and it is available for research use at http://www.oncohabitats.upv.es.

The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC 2 , a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC 2 showed a macro F1score of 0.759 in life-threatening classification, 0.592 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5%, 17.5% and 5.1%, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC 2 significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (α=0.05). Hence, DeepEMC 2 is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.

Background The microvessels area (MVA), derived from microvascular proliferation, is a biomarker useful for high-grade glioma classification. Nevertheless, its measurement is costly, labor-intense, and invasive. Finding radiologic correlations with MVA could provide a complementary non-invasive approach without an extra cost and labor intensity and from the first stage. This study aims to correlate imaging markers, such as relative cerebral blood volume (rCBV), and local MVA in IDH-wildtype glioblastoma, and to propose this imaging marker as useful for astrocytoma grade 4 classification. Methods Data from 73 tissue blocks belonging to 17 IDH-wildtype glioblastomas and 7 blocks from 2 IDH-mutant astrocytomas were compiled from the Ivy GAP database. MRI processing and rCBV quantification were carried out using ONCOhabitats methodology. Histologic and MRI co-registration was done manually with experts’ supervision, achieving an accuracy of 88.8% of overlay. Spearman’s correlation was used to analyze the association between rCBV and microvessel area. Mann-Whitney test was used to study differences of rCBV between blocks with presence or absence of microvessels in IDH-wildtype glioblastoma, as well as to find differences with IDH-mutant astrocytoma samples. Results Significant positive correlations were found between rCBV and microvessel area in the IDH-wildtype blocks (p < 0.001), as well as significant differences in rCBV were found between blocks with microvascular proliferation and blocks without it (p < 0.0001). In addition, significant differences in rCBV were found between IDH-wildtype glioblastoma and IDH-mutant astrocytoma samples, being 2–2.5 times higher rCBV values in IDH-wildtype glioblastoma samples. Conclusions The proposed rCBV marker, calculated from diagnostic MRIs, can detect in IDH-wildtype glioblastoma those regions with microvessels from those without it, and it is significantly correlated with local microvessels area. In addition, the proposed rCBV marker can differentiate the IDH mutation status, providing a complementary non-invasive method for high-grade glioma classification.