Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

Magnetic resonance imaging of brain angiogenesis after stroke

Stroke is a major cause of mortality and long-term disability worldwide. The initial changes in local perfusion and tissue status underlying loss of brain function are increasingly investigated with noninvasive imaging methods. In addition, there is a growing interest in imaging of processes that contribute to post-stroke recovery. In this review, we discuss the application of magnetic resonance imaging (MRI) to assess the formation of new vessels by angiogenesis, which is hypothesized to participate in brain plasticity and functional recovery after stroke. The excellent soft tissue contrast, high spatial and temporal resolution, and versatility render MRI particularly suitable to monitor the dynamic processes involved in vascular remodeling after stroke. Here we review recent advances in the field of MR imaging that are aimed at assessment of tissue perfusion and microvascular characteristics, including cerebral blood flow and volume, vascular density, size and integrity. The potential of MRI to noninvasively monitor the evolution of post-ischemic angiogenic processes is demonstrated from a variety of in vivo studies in experimental stroke models. Finally, we discuss some pitfalls and limitations that may critically affect the accuracy and interpretation of MRI-based measures of (neo)vascularization after stroke

Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II–III diffuse gliomas

The value of perfusion and diffusion-weighted MRI in differentiating histological subtypes according to the 2007 WHO glioma classification scheme (i.e. astrocytoma vs. oligodendroglioma) and genetic subtypes according to the 2016 WHO reclassification (e.g. 1p/19q co-deletion and IDH1 mutation status) in WHO grade II and III diffuse gliomas remains controversial. In the current study, we describe unique perfusion and diffusion MR signatures between histological and genetic glioma subtypes. Sixty-five patients with 2007 histological designations (astrocytomas and oligodendrogliomas), 1p/19q status (+ = intact/- = co-deleted), and IDH1 mutation status (MUT/WT) were included in this study. In all patients, median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) were estimated within T2 hyperintense lesions. Bootstrap hypothesis testing was used to compare subpopulations of gliomas, separated by WHO grade and 2007 or 2016 glioma classification schemes. A multivariable logistic regression model was also used to differentiate between 1p19q+ and 1p19q- WHO II-III gliomas. Neither rCBV nor ADC differed significantly between histological subtypes of pure astrocytomas and pure oligodendrogliomas. ADC was significantly different between molecular subtypes (p = 0.0016), particularly between IDHWT and IDHMUT/1p19q+ (p = 0.0013). IDHMUT/1p19q+ grade III gliomas had higher median ADC; IDHWT grade III gliomas had higher rCBV with lower ADC; and IDHMUT/1p19q- had intermediate rCBV and ADC values, similar to their grade II counterparts. A multivariable logistic regression model was able to differentiate between IDHWT and IDHMUT WHO II and III gliomas with an AUC of 0.84 (p < 0.0001, 74% sensitivity, 79% specificity). Within IDHMUT WHO II-III gliomas, a separate multivariable logistic regression model was able to differentiate between 1p19q+ and 1p19q- WHO II-III gliomas with an AUC of 0.80 (p = 0.0015, 64% sensitivity, 82% specificity). ADC better differentiated between genetic subtypes of gliomas according to the 2016 WHO guidelines compared to the classification scheme outlined in the 2007 WHO guidelines based on histological features of the tissue. Results suggest a combination of rCBV, ADC, T2 hyperintense volume, and presence of contrast enhancement together may aid in non-invasively identifying genetic subtypes of diffuse gliomas

Analysis Of Postprocessing Steps For Residue Function Dependent Dynamic Susceptibility Contrast (Dsc)-Mri Biomarkers And Their Clinical Impact On Glioma Grading For Both 1.5 And 3T

Background: Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers. Purpose/Hypothesis: To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading. Study Type: Retrospective study from The Cancer Imaging Archive (TCIA). Population: Forty-nine subjects with low- and high-grade gliomas. Field Strength/Sequence: 1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition. Assessment: Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2). Statistical Tests: Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests. Results: MTT alone was unable to statistically differentiate glioma grade (P \u3e 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P \u3e 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74–0.84; rCBV AUROC increases 0.78–0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P \u3c 0.007), but not if processed by current conventional DSC methods (Method 1) (P \u3e 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone. Data Conclusion: With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading. Level of Evidence: 3. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2019

Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T.

BACKGROUND: Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers. PURPOSE/HYPOTHESIS: To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading. STUDY TYPE: Retrospective study from The Cancer Imaging Archive (TCIA). POPULATION: Forty-nine subjects with low- and high-grade gliomas. FIELD STRENGTH/SEQUENCE: 1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition. ASSESSMENT: Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2). STATISTICAL TESTS: Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests. RESULTS: MTT alone was unable to statistically differentiate glioma grade (P \u3e 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P \u3e 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74-0.84; rCBV AUROC increases 0.78-0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P \u3c 0.007), but not if processed by current conventional DSC methods (Method 1) (P \u3e 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone. DATA CONCLUSION: With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547-553