Nicholas S. Cho

Purpose:Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy.Experimental Design:N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival.Results:Optimized thresholds occurred at g = 0.07 months−1 (phase II: HR = 0.2579, P = 5 × 10−20; phase III: HR = 0.2197, P = 5 × 10−5); d = 0.11 months−1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10−17; HR = 0.2061, P = 2 × 10−5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth.Conclusions:Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

Background: This study explored MRI characteristics at the time of tumor progression to study pathologically confirmed MT in IDHm 1p/19q-intact astrocytomas (IDHm-A) and IDHm 1p/19q-co-deleted oligodendrogliomas (IDHm-O). Methods: N = 64 patients with initial pathological grade 2 IDH-mutant glioma diagnosis who underwent repeated tissue sampling and were classified as pathologically confirmed MT (n = 35) or non-MT (n = 29) with available presurgical anatomical (n = 64), diffusion-weighted (n = 61), and dynamic susceptibility contrast perfusion MRI (n = 53) were retrospectively studied. Measurable contrast enhancement (> 1000 mm3), tumor volume, tumor growth rate, sphericity, median apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) were compared between MT vs non-MT IDHm-A and IDHm-O. Results: 81% of contrast-enhancing IDHm-A and 100% of contrast-enhancing IDHm-O demonstrated MT, while 41% of IDHm-A and 62% IDHm-O exhibited both nonenhancing tumor progression and MT. Tumor volumes were significantly larger in patients with MT compared to non-MT groups for IDHm-A (P = .02) and IDHm-O (P = .04). T2/FLAIR tumor volume growth rate was significantly higher (P = .003), nrCBV was significantly higher (P = .002), and ADC trended lower (P = .06) in MT vs non-MT IDHm-A. There were no significant differences in growth rate, ADC, nrCBV, or sphericity when comparing MT vs non-MT IDHm-O (P > .05). Conclusions: Many MT IDHm gliomas remain nonenhancing. Growth rate, diffusion, and perfusion MRI show differences between MT and non-MT in IDHm-A but not IDHm-O, which may reflect the different tumor biology of these IDHm molecular subtypes and their need for separate imaging biomarkers. Tumor volumes can help determine MT for both IDHm-A and IDHm-O.

Background: MRI-derived apparent diffusion coefficient (ADC) and contrast-enhancement (CE) may represent noninvasive biomarkers to evaluate microstructural tissue changes and histopathological immune cell infiltration induced by immune checkpoint inhibitors (ICI). Methods: One hundred and twenty-five lesions across 93 patients were analyzed. ADC (reflecting water diffusivity) and CE T1-subtraction maps (reflecting blood-brain barrier permeability) tumor values were used for bivariate histograms of ADC and CE, and to quantify voxel fractions belonging to highADC-highCE and lowADC-lowCE quadrants. The association between ICI exposure and voxel frequency in ADC-CE quadrants was evaluated with multivariate mixed-effects models accounting for corticosteroid exposure and other variables. In a subset of patients (n = 23), the association between immune cell counts (CD45 + cell density) from tissue samples and ADC-CE phenotypes was tested. Results: ICI and corticosteroid exposure were associated with distinct ADC-CE phenotypes on bivariate histograms. ICI-exposed lesions showed significantly higher voxel frequency in the highADC-highCE quadrant (P < .001) and lower voxel frequency in the lowADC-lowCE quadrant (P < .01), compared to ICI-naïve lesions. Multivariate analyses confirmed that ICI exposure was an independent determinant of higher voxel frequency in the highADC-highCE quadrant (P = .001) and lower voxel frequency in the lowADC-lowCE quadrant (P = .01) in the absence of corticosteroid treatment. Corticosteroid usage significantly influenced the relationship between ADC-CE phenotypes and ICI exposure. Voxel frequency in ADC-CE quadrants was correlated with CD45 + cell density in histopathology (partial ρ = 0.56 P = .01 for highADC-highCE, partial ρ = –0.64 P < .01 for lowADC-lowCE). Conclusions: ADC-CE phenotypes are associated with ICI exposure and immune cell infiltration in BM, representing potential noninvasive markers to monitor ICI-induced pathophysiological changes.

Purpose: This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. Methods: A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. Results: T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). Conclusion: T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.