scholarly journals P04.11 Voxelwise correlation between vascular MRI parameters obtained with ASL and DSC is decreased in IDH-wt non-enhancing glioma

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii31-iii31
Author(s):  
E A H Warnert ◽  
F Incekara ◽  
A J P E Vincent ◽  
J W Schouten ◽  
M J van den Bent ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Correlation Coefficients ◽  
Region Of Interest ◽  
Dynamic Susceptibility ◽  
Idh Mutation ◽  
Dynamic Susceptibility Contrast ◽  
Mutation Status ◽  
Susceptibility Contrast ◽  
Perfusion Measurements ◽  
Future Work

Abstract BACKGROUND Comparative studies of dynamic susceptibility contrast (DSC) based measurement of cerebral blood volume (CBV) or cerebral blood flow (CBF) and arterial spin labelling (ASL) based measurement of CBF have previously shown good correlation of these parameters in human glioma. However, these studies were mostly done before inclusion of the mutation status of the isocitrate dehydrogenase (IDH) encoding gene in brain tumour classification. In light of the call for gadolinium-free imaging, here we investigate the effect of IDH-mutation status on the correlation between ASL and DSC-based perfusion measurements in non-enhancing glioma. MATERIAL AND METHODS Twenty-two patients with non-enhancing glioma and confirmed IDH-mutation status (next generation sequencing, 6 IDH-wildtype and 16 IDH-mutated) underwent 3T MRI scanning (GE, Milwaukee, WI, USA). Image acquisition included a 3D spiral pseudocontinuous ASL with time-encoded labelling (7 effective label delays from 0.8 to 2 s, reconstruction matrix 128x128x42, resolution 1.9x1.9x3.5 mm3), and 2D DSC imaging (122 TRs, TR/TE 1500m/18.6ms, 15 slices, voxel size: 1.88x1.88x4 mm3) in which a bolus of 7.5ml of gadolinium-based contrast agent (Gadovist, Bayer, Leverkussen, GE) was injected. A pre-load bolus of equal size was given 5 minutes prior to DSC imaging. DSC and ASL images were motion corrected and linearly registered to high resolution FLAIR images (FSL, version 5.0.9, Oxford, UK). DSC-relative CBV (rCBV), DSC-relative CBF (rCBF), and ASL-CBF maps were calculated via previously described methods. The glioma region of interest (ROI) was determined via manual segmentation on the FLAIR images. Voxel-wise Pearson’s linear correlation coefficients (ρ) within this ROI were calculated between ASL-CBF and DSC-rCBV, and between ASL-CBF and DSC-rCBF. RESULTS Normalised histograms indicate that IDH-wt glioma has higher values for ASL-CBF, DSC-rCBV, and DSC-rCBF than IDH-mutated glioma. IDH-wildtype glioma has a significantly lower ρ ASL-CBF vs DSC-rCBV and ρ ASL-CBF vs DSC-rCBF than IDH-mutated glioma (two-sample t-tests p < 0.05). CONCLUSION IDH-mutation status of non-enhancing glioma potentially affects the correlation between ASL-CBF and DSC-rCBF/rCBV and should be taken into account when moving towards ASL-only imaging. The decreased correlation between ASL and DSC-based vascular parameters in IDH-wt gliomas may be due to more aggressive vasculature in subtypes of IDH-wt tumours. Future work includes expansion of the current patient cohort (part of the ongoing iGENE study).

scholarly journals Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

2019 ◽  
Author(s):  
Carole H. Sudre ◽  
Jasmina Panovska-Griffiths ◽  
Eser Sanverdi ◽  
Sebastian Brandner ◽  
Vasileios K. Katsaros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic Value ◽  
Dynamic Susceptibility ◽  
Idh Mutation ◽  
Dynamic Susceptibility Contrast ◽  
Mutation Status ◽  
Dsc Mri ◽  
Grade Difference ◽  
Grade Ii ◽  
Susceptibility Contrast

AbstractBackgroundMachine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.Methods333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsFeatures from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1.ConclusionDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.Key points-On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% <1 grade difference)-Shape features distinguish best grade II from grade III gliomas.-Texture and distribution features distinguish best grade III from grade IV tumours.Importance of studyThis work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% <1 grade difference) and 71% across mutation status) was obtained. Therefore, our results provide a proof-of-concept for this emerging precision medicine field that has good generalisability and scalability properties. Introspection on the classification errors highlighted mostly borderline cases and helped underline the challenges of a categorical classification in a pathological continuum.With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

scholarly journals Facing Contrast-Enhancing Gliomas: Perfusion MRI in Grade III and Grade IV Gliomas according to Tumor Area

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Anna Luisa Di Stefano ◽  
Niels Bergsland ◽  
Giulia Berzero ◽  
Lisa Farina ◽  
Elisa Rognone ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Region Of Interest ◽  
Vascular Density ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Tumor Bed ◽  
Susceptibility Contrast ◽  
Grade Iii ◽  
Reference Areas ◽  
Tumor Area

Tumoral neoangiogenesis characterizes high grade gliomas. Relative Cerebral Blood Volume (rCBV), calculated with Dynamic Susceptibility Contrast (DSC) Perfusion-Weighted Imaging (PWI), allows for the estimation of vascular density over the tumor bed. The aim of the study was to characterize putative tumoral neoangiogenesis via the study of maximal rCBV with a Region of Interest (ROI) approach in three tumor areas—the contrast-enhancing area, the nonenhancing tumor, and the high perfusion area on CBV map—in patients affected by contrast-enhancing glioma (grades III and IV). Twenty-one patients were included: 15 were affected by grade IV and 6 by grade III glioma. Maximal rCBV values for each patient were averaged according to glioma grade. Although rCBV from contrast-enhancement and from nonenhancing tumor areas was higher in grade IV glioma than in grade III (5.58 and 2.68; 3.01 and 2.2, resp.), the differences were not significant. Instead, rCBV recorded in the high perfusion area on CBV map, independently of tumor compartment, was significantly higher in grade IV glioma than in grade III (7.51 versus 3.78,P=0.036). In conclusion, neoangiogenesis encompasses different tumor compartments and CBV maps appear capable of best characterizing the degree of neovascularization. Facing contrast-enhancing brain tumors, areas of high perfusion on CBV maps should be considered as the reference areas to be targeted for glioma grading.

Attempts to improve absolute quantification of cerebral blood flow in dynamic susceptibility contrast magnetic resonance imaging: a simplified T1-weighted steady-state cerebral blood volume approach

2007 ◽  
Vol 48 (5) ◽  
pp. 550-556 ◽  
Author(s):  
R. Wirestam ◽  
L. Knutsson ◽  
J. Risberg ◽  
S. Börjesson ◽  
E.-M. Larsson ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Steady State ◽  
Water Exchange ◽  
Cerebral Blood Volume ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Contrast Magnetic Resonance ◽  
Susceptibility Contrast

Background: Attempts to retrieve absolute values of cerebral blood flow (CBF) by dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) have typically resulted in overestimations. Purpose: To improve DSC-MRI CBF estimates by calibrating the DSC-MRI-based cerebral blood volume (CBV) with a corresponding T1-weighted (T1W) steady-state (ss) CBV estimate. Material and Methods: 17 volunteers were investigated by DSC-MRI and 133Xe SPECT. Steady-state CBV calculation, assuming no water exchange, was accomplished using signal values from blood and tissue, before and after contrast agent, obtained by T1W spin-echo imaging. Using steady-state and DSC-MRI CBV estimates, a calibration factor K = CBV(ss)/CBV(DSC) was obtained for each individual. Average whole-brain CBF(DSC) was calculated, and the corrected MRI-based CBF estimate was given by CBF(ss) = K×CBF(DSC). Results: Average whole-brain SPECT CBF was 40.1±6.9 ml/min·100 g, while the corresponding uncorrected DSC-MRI-based value was 69.2±13.8 ml/min·100 g. After correction with the calibration factor, a CBF(ss) of 42.7±14.0 ml/min·100 g was obtained. The linear fit to CBF(ss)-versus-CBF(SPECT) data was close to proportionality ( R = 0.52). Conclusion: Calibration by steady-state CBV reduced the population average CBF to a reasonable level, and a modest linear correlation with the reference 133Xe SPECT technique was observed. Possible explanations for the limited accuracy are, for example, large-vessel partial-volume effects, low post-contrast signal enhancement in T1W images, and water-exchange effects.

Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas

2020 ◽  
pp. 028418512097362
Author(s):  
Xiefeng Yang ◽  
Yu Lin ◽  
Zhen Xing ◽  
Dejun She ◽  
Yan Su ◽  
...  
Keyword(s):  
Predictive Value ◽  
Operating Characteristic ◽  
Cerebral Blood Volume ◽  
Characteristic Curve ◽  
Lower Grade ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Conventional Mri ◽  
Susceptibility Contrast ◽  
Sensitivity Specificity

Background Isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas (LGGs) are further classified into two classes: with and without 1p/19q codeletion. IDH-mutant and 1p/19q codeleted LGGs have better prognosis compared with IDH-mutant and 1p/19q non-codeleted LGGs. Purpose To evaluate conventional magnetic resonance imaging (cMRI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) for predicting 1p/19q codeletion status of IDH-mutant LGGs. Material and Methods We retrospectively reviewed cMRI, DWI, SWI, and DSC-PWI in 142 cases of IDH mutant LGGs with known 1p/19q codeletion status. Features of cMRI, relative ADC (rADC), intratumoral susceptibility signals (ITSSs), and the value of relative cerebral blood volume (rCBV) were compared between IDH-mutant LGGs with and without 1p/19q codeletion. Receiver operating characteristic curve and logistic regression were used to determine diagnostic performances. Results IDH-mutant and 1p/19q non-codeleted LGGs tended to present with the T2/FLAIR mismatch sign and distinct borders ( P < 0.001 and P = 0.038, respectively). Parameters of rADC, ITSSs, and rCBVmax were significantly different between the 1p/19q codeleted and 1p/19q non-codeleted groups ( P < 0.001, P = 0.017, and P < 0.001, respectively). A combination of cMRI, SWI, DWI, and DSC-PWI for predicting 1p/19q codeletion status in IDH-mutant LGGs resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and an AUC of 80.36%, 78.57%, 83.30%, 75.00%, and 0.88, respectively. Conclusion 1p/19q codeletion status of IDH-mutant LGGs can be stratified using cMRI and advanced MRI techniques, including DWI, SWI, and DSC-PWI. A combination of cMRI, rADC, ITSSs, and rCBVmax may improve the diagnostic performance for predicting 1p/19q codeletion status.

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