Salvage reirradiation for recurrent glioblastoma: a retrospective case series analysis.

PURPOSE: To assess the clinical outcome of patients with recurrent glioblastoma treated with salvage reirradiation. METHODS: Between 2005 and 2022, data from adult patients with glioblastoma treated with surgery and radio-chemotherapy Stupp regimen who developed a local in-field relapse and received stereotactic radiotherapy (SRT) were retrospectively reviewed. RESULTS: The study population included 44 patients with recurrent glioblastoma (median of 9.5 months after the first radiotherapy). Reirradiation alone was given to 47.7% of patients. The median maximum diameter of the recurrence was 13.5 mm. The most common SRT regimen (52.3%) was 35 Gy in 10 fractions. Acute toxicity was mild, with transient worsening of previous neurological symptoms in only 15% of patients. After a median follow-up of 15 months, 40% presented radiological response, but a remarkable number of early distant progressions were recorded (32.5%). The median time to progression was 4.8 months, being the dose, the scheme, the size of the recurrence or the strategy (exclusive RT vs. combined) unrelated factors. The median overall survival (OS) was 14.9 months. Karnofsky index < 70 and the size of the recurrence (maximum diameter < 25 mm) were significant factors associated with OS. Radiological changes after reirradiation were commonly seen (> 50% of patients) hindering the response assessment. CONCLUSIONS: Reirradiation is a feasible and safe therapeutic option to treat localized glioblastoma recurrences, able to control the disease for a few months in selected patients, especially those with good functional status and small lesions. Hypofractionated schemes provided a suitable toxicity profile. Radiological changes were common.

DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions.

PURPOSE: The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values. METHODS: This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC. RESULTS: The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values. CONCLUSION: Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

The T1-dark-rim: A novel imaging sign for detecting smoldering inflammation in multiple sclerosis.

PURPOSE: Paramagnetic rim lesions (PRLs), usually identified in susceptibility-weighted imaging (SWI), are a promising prognostic biomarker of disability progression in multiple sclerosis (MS). However, SWI is not routinely performed in clinical practice. The objective of this study is to define a novel imaging sign, the T1-dark rim, identifiable in a standard 3DT1 gradient-echo inversion-recovery sequence, such as 3D T1 turbo field echo (3DT1FE) and explore its performance as a SWI surrogate to define PRLs. METHODS: This observational cross-sectional study analyzed MS patients who underwent 3T magnetic resonance imaging (MRI) including 3DT1TFE and SWI. Rim lesions were evaluated in 3DT1TFE, processed SWI, and SWI phase and categorized as true positive, false positive, or false negative based on the value of the T1-dark rim in predicting SWI phase PRLs. Sensitivity and positive predictive values of the T1-dark rim for detecting PRLs were calculated. RESULTS: Overall, 80 rim lesions were identified in 63 patients (60 in the SWI phase and 78 in 3DT1TFE; 58 true positives, 20 false positives, and two false negatives). The T1-dark rim demonstrated 97% sensitivity and 74% positive predictive value for detecting PRLs. More PRLs were detected in the SWI phase than in processed SWI (60 and 57, respectively). CONCLUSION: The T1-dark rim sign is a promising and accessible novel imaging marker to detect PRLs whose high sensitivity may enable earlier detection of chronic active lesions to guide MS treatment escalation. The relevance of T1-dark rim lesions that are negative on SWI opens up a new field for analysis.

Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous.

The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast-enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow-up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast-enhancing regions with a blind-source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns-proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast-enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast-enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers.

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles.

OBJECTIVE: Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. METHODS: Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. RESULTS: The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. CONCLUSIONS: Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. CLINICAL RELEVANCE STATEMENT: The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. KEY POINTS: • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.

Proton MR spectroscopy shows improved performance to segregate high-grade astrocytoma subgroups when defined with the new 2021 World Health Organization classification of central nervous system tumors.

OBJECTIVES: The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors prioritizes isocitrate dehydrogenase (IDH) mutation to define tumor types in diffuse gliomas, in contrast to the 2016 classification, which prioritized histological features. Our objective was to investigate the influence of this change in the performance of proton MR spectroscopy (1H-MRS) in segregating high-grade diffuse astrocytoma subgroups. METHODS: Patients with CNS WHO grade 3 and 4 diffuse astrocytoma, known IDH mutation status, and available 1H-MRS were retrospectively retrieved and divided into 4 groups based on IDH mutation status and histological grade. Differences in 1H-MRS between groups were analyzed with the Kruskal-Wallis test. The points on the spectrum that showed the greatest differences were chosen to evaluate the performance of 1H-MRS in discriminating between grades 3 and 4 tumors (WHO 2016 defined), and between IDH-mutant and IDH-wildtype tumors (WHO 2021). ROC curves were constructed with these points, and AUC values were calculated and compared. RESULTS: The study included 223 patients with high-grade diffuse astrocytoma. Discrimination between IDH-mutant and IDH-wildtype tumors showed higher AUC values (highest AUC short TE, 0.943; long TE, 0.864) and more noticeable visual differences than the discrimination between grade 3 and 4 tumors (short TE, 0.885; long TE, 0.838). CONCLUSION: Our findings suggest that 1H-MRS is more applicable to classify high-grade astrocytomas defined with the 2021 criteria. Improved metabolomic robustness and more homogeneous groups yielded better tumor type discrimination by 1H-MRS with the new criteria. CLINICAL RELEVANCE STATEMENT: The 2021 World Health Organization classification of brain tumors empowers molecular criteria to improve tumor characterization. This derives in greater segregation of high-grade diffuse astrocytoma subgroups by MR spectroscopy and warrants further development of brain tumor classification tools with spectroscopy. KEY POINTS: • The new 2021 updated World Health Organization classification of central nervous system tumors maximizes the role of molecular diagnosis in the classification of brain tumors. • Proton MR spectroscopy performs better to segregate high-grade astrocytoma subgroups when defined with the new criteria. • The study provides additional evidence of improved metabolic characterization of brain tumor subgroups with the new criteria.

Deeply 3D-T1-TFE hypointense voxels are characteristic of phase-rim lesions in multiple sclerosis.

OBJECTIVES: The development of new drugs for the treatment of progressive multiple sclerosis (MS) highlights the need for new prognostic biomarkers. Phase-rim lesions (PRLs) have been proposed as markers of progressive disease but are difficult to identify and quantify. Previous studies have identified T1-hypointensity in PRLs. The aim of this study was to compare the intensity profiles of PRLs and non-PRL white-matter lesions (nPR-WMLs) on three-dimensional T1-weighted turbo field echo (3DT1TFE) MRI. We then evaluated the performance of a derived metric as a surrogate for PRLs as potential markers for risk of disease progression. METHODS: This study enrolled a cohort of relapsing-remitting (n = 10) and secondary progressive MS (n = 10) patients for whom 3 T MRI was available. PRLs and nPR-WMLs were segmented, and voxel-wise normalized T1-intensity histograms were analyzed. The lesions were divided equally into training and test datasets, and the fifth-percentile (p5)-normalized T1-intensity of each lesion was compared between groups and used for classification prediction. RESULTS: Voxel-wise histogram analysis showed a unimodal histogram for nPR-WMLs and a bimodal histogram for PRLs with a large peak in the hypointense limit. Lesion-wise analysis included 1075 nPR-WMLs and 39 PRLs. The p5 intensity of PRLs was significantly lower than that of nPR-WMLs. The T1 intensity-based PRL classifier had a sensitivity of 0.526 and specificity of 0.959. CONCLUSIONS: Profound hypointensity on 3DT1TFE MRI is characteristic of PRLs and rare in other white-matter lesions. Given the widespread availability of T1-weighted imaging, this feature might serve as a surrogate biomarker for smoldering inflammation. CLINICAL RELEVANCE STATEMENT: Quantitative analysis of 3DT1TFE may detect deeply hypointense voxels in multiple sclerosis lesions, which are highly specific to PRLs. This could serve as a specific indicator of smoldering inflammation in MS, aiding in early detection of disease progression. KEY POINTS: • Phase-rim lesions (PRLs) in multiple sclerosis present a characteristic T1-hypointensity on 3DT1TFE MRI. • Intensity-normalized 3DT1TFE can be used to systematically identify and quantify these deeply hypointense foci. • Deep T1-hypointensity may act as an easily detectable, surrogate marker for PRLs.

Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

OBJECTIVES: The differential between high-grade glioma (HGG) and metastasis remains challenging in common radiological practice. We compare different natural language processing (NLP)-based deep learning models to assist radiologists based on data contained in radiology reports. METHODS: This retrospective study included 185 MRI reports between 2010 and 2022 from two different institutions. A total of 117 reports were used for the training and 21 were reserved for the validation set, while the rest were used as a test set. A comparison of the performance of different deep learning models for HGG and metastasis classification has been carried out. Specifically, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), a hybrid version of BiLSTM and CNN, and a radiology-specific Bidirectional Encoder Representations from Transformers (RadBERT) model were used. RESULTS: For the classification of MRI reports, the CNN network provided the best results among all tested, showing a macro-avg precision of 87.32%, a sensitivity of 87.45%, and an F1 score of 87.23%. In addition, our NLP algorithm detected keywords such as tumor, temporal, and lobe to positively classify a radiological report as HGG or metastasis group. CONCLUSIONS: A deep learning model based on CNN enables radiologists to discriminate between HGG and metastasis based on MRI reports with high-precision values. This approach should be considered an additional tool in diagnosing these central nervous system lesions. CLINICAL RELEVANCE STATEMENT: The use of our NLP model enables radiologists to differentiate between patients with high-grade glioma and metastasis based on their MRI reports and can be used as an additional tool to the conventional image-based approach for this challenging task. KEY POINTS: • Differential between high-grade glioma and metastasis is still challenging in common radiological practice. • Natural language processing (NLP)-based deep learning models can assist radiologists based on data contained in radiology reports. • We have developed and tested a natural language processing model for discriminating between high-grade glioma and metastasis based on MRI reports that show high precision for this task.

MR dynamic-susceptibility-contrast perfusion metrics in the presurgical discrimination of adult solitary intra-axial cerebellar tumors.

OBJECTIVES: Adult solitary intra-axial cerebellar tumors are uncommon. Their presurgical differentiation based on neuroimaging is crucial, since management differs substantially. Comprehensive full assessment of MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) may reveal key differences between entities. This study aims to provide new insights on perfusion patterns of these tumors and to explore the potential of DSC-PWI in their presurgical discrimination. METHODS: Adult patients with a solitary cerebellar tumor on presurgical MR and confirmed histological diagnosis of metastasis, medulloblastoma, hemangioblastoma, or pilocytic astrocytoma were retrospectively retrieved (2008-2023). Volumetric segmentation of tumors and normal-appearing white matter (for normalization) was semi-automatically performed on CE-T1WI and coregistered with DSC-PWI. Mean normalized values per patient tumor-mask of relative cerebral blood volume (rCBV), percentage of signal recovery (PSR), peak height (PH), and normalized time-intensity curves (nTIC) were extracted. Statistical comparisons were done. Then, the dataset was split into training (75%) and test (25%) cohorts and a classifier was created considering nTIC, rCBV, PSR, and PH in the model. RESULTS: Sixty-eight patients (31 metastases, 13 medulloblastomas, 13 hemangioblastomas, and 11 pilocytic astrocytomas) were included. Relevant differences between tumor types' nTICs were demonstrated. Hemangioblastoma showed the highest rCBV and PH, pilocytic astrocytoma the highest PSR. All parameters showed significant differences on the Kruskal-Wallis tests (p < 0.001). The classifier yielded an accuracy of 98% (47/48) in the training and 85% (17/20) in the test sets. CONCLUSIONS: Intra-axial cerebellar tumors in adults have singular and significantly different DSC-PWI signatures. The combination of perfusion metrics through data-analysis rendered excellent accuracies in discriminating these entities. CLINICAL RELEVANCE STATEMENT: In this study, the authors constructed a classifier for the non-invasive imaging presurgical diagnosis of adult intra-axial cerebellar tumors. The resultant tool can be a support for decision-making in the clinical practice and enables optimal personalized patient management. KEY POINTS: • Adult intra-axial cerebellar tumors exhibit specific, singular, and statistically significant different MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) signatures. • Data-analysis, applied to MR DSC-PWI, could provide added value in the presurgical diagnosis of solitary cerebellar metastasis, medulloblastoma, hemangioblastoma, and pilocytic astrocytoma. • A classifier based on DSC-PWI metrics yields excellent accuracy rates and could be used as a support tool for radiologic diagnosis with clinician-friendly displays.

Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study.

In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. PURPOSE: To test whether MV grids can be classified with models trained with SV. METHODS: Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. RESULTS: The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. DISCUSSION: The reasons for failure in the classification of the MV test set were related to the presence of artifacts.

Tumoral and normal brain tissue extraction protocol for wide-scope screening of organic pollutants.

Little is known about the presence of organic pollutants in human brain (and even less in brain tumors). In this regard, it is necessary to develop new analytical protocols capable of identifying a wide range of exogenous chemicals in this type of samples (by combining target, suspect and non-target strategies). These methodologies should be robust and simple. This is particularly challenging for solid samples, as reliable extraction and clean-up techniques should be combined to obtain an optimal result. Hence, the present study focuses on the development of an analytical methodology that allows the screening of a wide range of organic chemicals in brain and brain tumor samples. This protocol was based on a solid-liquid extraction based on bead beating, solid-phase extraction clean-up with multi-layer mixed-mode cartridges, reconstitution and LC-HRMS analysis. To evaluate the performance of the extraction methodology, a set of 66 chemicals (e.g., pharmaceuticals, biocides, or plasticizers, among others) with a wide range of physicochemical properties was employed. Quality control parameters (i.e., linear range, sensitivity, matrix effect (ME%), and recoveries (R%)) were calculated and satisfactory results were obtained for them (e.g., R% within 60-120% for 32 chemicals, or ME% higher than 50% (signal suppression) for 79% of the chemicals).

High Myoinositol on Proton MR Spectroscopy Could Be a Potential Signature of Papillary Tumors of the Pineal Region-Case Report of Two Patients.

Papillary tumor of the pineal region (PTPR) is an uncommon entity in which a presurgical suspicion may be crucial for patient management. Maximal safe neurosurgical resection is of choice when PTPR is suspected, whereas non-surgical approaches can be considered in other tumors of the pineal region, such as pineocytoma or concrete subtypes of germ-cell tumors. In general terms, imaging features of tumors of the pineal region have been reported to be unspecific. Nevertheless, in this report, we describe two pathology-confirmed PTPRs in which presurgical proton MR spectroscopy demonstrated extremely high myoinositol, a pattern which drastically differs from that of other pineal tumors. We hypothesize that this high myoinositol may be related to PTPR's known ependymal component, and that it could be used as a specific non-invasive diagnostic signature.

Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis.

OBJECTIVE: Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. METHODS: In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. RESULTS: A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%. CONCLUSION: This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. KEY POINTS: • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.