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BACKGROUND Tumor-associated macrophages (TAMs) are a key component of glioblastoma (GBM) tumor microenvironment. Considering the differential role of different TAM phenotypes in iron metabolism with the M1 phenotype storing intracellular iron, and M2 phenotype releasing iron in the tumor microenvironment, here we investigated non-invasive quantitative susceptibility mapping (QSM) and T2* MRI relaxometry to quantify iron as imaging biomarkers for TAMs in adult patients with GBM. METHODS In this prospective study, 21 adult patients with GBM were enrolled between 2016 to 2019. Patients underwent a 3D single echo gradient echo sequence in addition to standard anatomical sequences on a 3 Tesla MRI. QSM images were reconstructed using the morphology-enabled dipole inversion (MEDI) algorithm. In 3 subjects, ex vivo imaging of surgical specimens was performed on a Bruker 9.4 Tesla 8.9 cm vertical bore MR using 3D multi-echo GRE scans, and R2* (1/T2*) maps were generated by a pixel-wise monoexponential fitting. Each specimen was stained with hematoxylin and eosin, as well as with CD68, CD86, CD206, and L-Ferritin. RESULTS Significant positive correlation was observed between mean susceptibility for the tumor enhancing zone and the L-ferritin positivity percent (r =0.56, p=0.018) and the combination of tumor’s enhancing zone and necrotic core and the L-Ferritin positivity percent (r=0.72; p=0.001). Moreover, the mean susceptibility significantly correlated with positivity percent for CD68 (ρ=0.52, p=0.034) and CD86 (r=0.7 p=0.001), but not for CD206 (ρ=0.09; p=0.7). There was a positive correlation between mean R2* values and CD68 positive cell counts (r =0.6, p=0.016). Similarly, mean R2* values significantly correlated with CD86 (r=0.54, p=0.03) but not with CD206 (r=0.15, p=0.5). High mean susceptibility in the necrotic core was associated with inferior PFS (hazard ratio,4.5, p=0.016). CONCLUSION MR Susceptibility Imaging can quantify the iron content of GBM and provide a non-invasive method for TAM quantification and phenotyping.

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman’s r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p

Background Plasma cell-free DNA (cfDNA) concentration is lower in glioblastoma (GBM) compared to other solid tumors, which can lead to low circulating tumor DNA (ctDNA) detection. In this study, we investigated the relationship between multimodality magnetic resonance imaging (MRI) and histopathologic features with plasma cfDNA concentration and ctDNA detection in patients with treatment-naive GBM. Methods We analyzed plasma cfDNA concentration, MRI scans, and tumor histopathology from 42 adult patients with newly diagnosed GBM. Linear regression analysis was used to examine the relationship of plasma cfDNA concentration before surgery to imaging and histopathologic characteristics. In a subset of patients, imaging and histopathologic metrics were also compared between patients with and without a detected tumor somatic mutation. Results Tumor volume with elevated (>1.5 times contralateral white matter) rate transfer constant (Kep, a surrogate of blood–brain barrier [BBB] permeability) was independently associated with plasma cfDNA concentration (P = .001). Histopathologic characteristics independently associated with plasma cfDNA concentration included CD68+ macrophage density (P = .01) and size of tumor vessels (P = .01). Patients with higher (grade ≥3) perivascular CD68+ macrophage density had lower volume transfer constant (Ktrans, P = .01) compared to those with lower perivascular CD68+ macrophage density. Detection of at least 1 somatic mutation in plasma cfDNA was associated with significantly lower perivascular CD68+ macrophages (P = .01). Conclusions Metrics of BBB disruption and quantity and distribution of tumor-associated macrophages are associated with plasma cfDNA concentration and ctDNA detection in GBM patients. These findings represent an important step in understanding the factors that determine plasma cfDNA concentration and ctDNA detection.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and carries a dismal prognosis. Significant challenges in the care of patients with GBM include marked vascular heterogeneity and arteriovenous (AV) shunting, which results in tumor hypoxia and inadequate delivery of systemic treatments to reach tumor cells. In this study, we investigated the utility of different MR perfusion techniques to detect and quantify arteriovenous (AV) shunting and tumor hypoxia in patients with GBM. Macrovascular shunting was present in 33% of subjects, with the degree of shunting ranging from (37–60%) using arterial spin labeling perfusion. Among the dynamic susceptibility contrast-enhanced perfusion curve features, there were a strong negative correlation between hypoxia score, DSC perfusion curve recovery slope (r = −0.72, P = 0.018) and angle (r = −0.73, P = 0.015). The results of this study support the possibility of using arterial spin labeling and pattern analysis of dynamic susceptibility contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma.