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Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Methods: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Results: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Conclusion: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Reliable differentiation between true tumor progression and treatment-related changes is a challenging situation in the management of glioma patients. Both amino-acid PET and perfusion MRI, as well as their combination, play a central role in this decision. In clinical practice, PET and MRI are usually acquired at two separate time points, so the question arises if and how this affects their diagnostic performance. In our study, we investigated a unique cohort of 38 glioblastoma patients (IDH wild-type), who received both a PET–MRI (with simultaneous acquisition of FET-PET and DSC perfusion) as well as an MRI exam with DSC perfusion within a month of each other. For all global and local image metrics, and importantly also for the diagnostic performance, we found no significant difference between the simultaneous and sequential acquisition of PET and MRI. These results are reassuring for routine clinical management and support further investigation into advanced, multi-parametric models for improving personalized decision-making in neuro-oncology when PET and MRI are not acquired simultaneously. Both positron emission tomography (PET) and magnetic resonance imaging (MRI), including dynamic susceptibility contrast perfusion (DSC-PWI), are crucial for treatment monitoring of patients with high-grade gliomas. In clinical practice, they are usually conducted at separate time points. Whether this affects their diagnostic performance is presently unclear. To this end, we retrospectively reviewed 38 patients with pathologically confirmed glioblastoma (IDH wild-type) and suspected tumor recurrence after radiotherapy. Only patients who received both a PET–MRI (where DSC perfusion was acquired simultaneously with a FET-PET) and a separate MRI exam (including DSC perfusion) were included. Tumors were automatically segmented into contrast-enhancing tumor (CET), necrosis, and edema. To compare the simultaneous as well as the sequential DSC perfusion to the FET-PET, we calculated Dice overlap, global mutual information as well as voxel-wise Spearman correlation of hotspot areas. For the joint assessment of PET and MRI, we computed logistic regression models for the differentiation between true progression (PD) and treatment-related changes (TRC) using simultaneously or sequentially acquired images as input data. We observed no significant differences between Dice overlap (p = 0.17; paired t-test), mutual information (p = 0.18; paired t-test) and Spearman correlation (p = 0.90; paired t-test) when comparing simultaneous PET–MRI and sequential PET/MRI acquisition. This also held true for the subgroup of patients with >14 days between exams. Importantly, for the diagnostic performance, ROC analysis showed similar AUCs for differentiation of PD and TRC (AUC simultaneous PET: 0.77; AUC sequential PET: 0.78; p = 0.83, DeLong's test). We found no relevant differences between simultaneous and sequential acquisition of FET-PET and DSC perfusion, also regarding their diagnostic performance. Given the increasing attention to multi-parametric assessment of glioma treatment response, our results reassuringly suggest that sequential acquisition is clinically and scientifically acceptable. [ABSTRACT FROM AUTHOR]

Abstract 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

Metastatic cancer to the central nervous system is primarily deposited by hematogenous spread in various anatomically distinct regions: calvarial, pachymeningeal, leptomeningeal, and brain parenchyma. A patient's overall clinical status and the information needed to make treatment decisions are the primary considerations in initial imaging modality selection. Contrast-enhanced MR imaging is the preferred imaging modality. Morphologic MR imaging is limited to delineating anatomic deraignment of tissues. Dynamic susceptibility contrast-enhanced perfusion and diffusion-weighted physiology-based MR imaging sequences have been developed that complement morphologic MR imaging by providing additional diagnostic information.

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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods: At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier's performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results: In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion: Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas. [ABSTRACT FROM AUTHOR]

Objective: To determine diagnostic accuracy of visualized dynamic susceptibility contrast (DSC) perfusion curve pattern in differentiation between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG). Materials and Methods: Forty-one patients (nineteen cases of PCNSL and twenty-two cases of HGG) acquired between January 2010 and November 2017 were included in the present study. The DSC perfusion curve patterns were retrospectively reviewed by two neuroradiologists, divided into two patterns as 1) return of time-intensity curve above the baseline after initial drop, and 2) return of time-intensity curve below or equal to baseline. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were analyzed. Results: Return of time-intensity curve above the baseline on DSC perfusion was found in 15 of 19 (78.9%) PCNSL cases, and only two of 22 (9.1%) HGG cases, p-value<0.001; whereas return of time-intensity curve below or equal to baseline was found in only four of 19 (21.1%) PCNSL cases and 20 of 22 (90.9%) HGG cases, p-value<0.001. Return of time-intensity signal curve above the baseline pattern had sensitivity of 78.9%, specificity of 90.9%, PPV of 88.2%, NPV of 88.3%, and accuracy of 85.4% for diagnosis of PCNSL, respectively. Conclusion: Visualized DSC curve pattern has a role in differentiation between PCNSL and HGG. Presence of return of time-signal intensity curve above the baseline could suggest the diagnosis of PCNSL. [ABSTRACT FROM AUTHOR]

Purpose: Dynamic susceptibility contrast (DSC) MR-perfusion is becoming a standard of care for the monitoring of glioblastoma. Yet, technical standards are lacking and measurements without leakage correction are still common. Also, data on leakage corrected measurements during stable disease is scarce. In this study we hypothesized that basic leakage correction would significantly enhance data quality during stable disease and improve progress detection. We furthermore investigated whether longitudinal data could increase diagnostic performance. Methods: Patients with histologically proven glioblastoma undergoing first-line therapy were prospectively recruited. We conducted DSC perfusion measurements without prebolus administration in 6-week intervals from the end of radiotherapy until progression. Maximum relative cerebral volume values (rCBVmax) with and without leakage correction were calculated using Philips IntelliSpace®. Results: We recruited 16 patients and conducted 82 MRI scans with a mean follow up of 7.2 month. During stable disease, corrected rCBVmax was significantly more stable than uncorrected rCBVmax. Detection of progression with a rCBVmax cutoff was better for corrected (specificity 86%) than for uncorrected rCBVmax (specificity 41%). Interestingly, the increase of corrected rCBVmax upon progression also had a good diagnostic performance with a combination of both cutoffs delivering the best result (sensitivity/specificity 89%/93%). Conclusion: Corrected rCBVmax supports the imaging finding of a stable disease and large increases during longitudinal observation support the diagnosis of tumor progression. rCBV values without prebolus or leakage correction are not reliable to monitor glioblastomas. Further studies to investigate the value of longitudinal rCBV dynamics for the differentiation of real tumor progression from pseudoprogression are warranted. [ABSTRACT FROM AUTHOR]

Purpose: To investigate possible relationships between the presence and location of arterial transit artifacts (ATA) and clinical symptoms, digital subtraction angiography (DSA), and dynamic susceptibility contrast (DSC) perfusion imaging abnormalities in patients with carotid artery stenosis (CAS)., Methods: Forty-seven patients who underwent arterial spin labeling (ASL) and DSC perfusion imaging in the same period diagnosed with > 50% unilateral internal carotid artery (ICA) stenosis by DSA performed 24 h after perfusion imaging were included. The presence of ATA, localization and hypoperfusion were evaluated using ASL interpretation. Maps derived from DSC perfusion, symptomatology, stenosis rates, and collateralization findings observed in DSA were investigated. Probable relationships were evaluated., Results: ATA on ASL were detected in 68.1% (32/47); 40.6% (13/32) of ATAs were observed in the distal middle cerebral artery (MCA) trace, 50% (16/32) in the intracranial ICA and MCA traces, and 9.4% (3/32) in the intracranial ICA trace. When classifications based on the ATA presence and localization was made, qualitative and quantitative CBF, MTT, and TTP abnormalities, symptomatology, stenosis rates, and collateralization findings significantly differed between groups (p < 0.05)., Conclusion: The presence and localization of ATA in patients with CAS may provide essential insights into cerebral hemodynamics and the CAS severity. ATAs observed only in the distal MCA trace may represent early-stage perfusion abnormalities and a moderate level of stenosis. ATA in the ICA trace may related to a more advanced level of perfusion abnormalities, critical stenosis rates, symptom or collateralization presence., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022 Elsevier Masson SAS. All rights reserved.)