Hu, Leland

Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.

Abstract Background : Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods : Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results : Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Conclusion : Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Abstract Background : Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods : Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results : Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Conclusion : Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Abstract Background : Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods : Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results : Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Conclusion : Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Abstract INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with a median overall survival of 15 months with standard-of-care treatment. GBM patients sometimes present with a cystic component, which can be identified through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7–22% of GBM patients and have reported mixed results regarding whether cystic GBM have a survival benefit compared to noncystic GBM. Using our large retrospective cohort of 493 first-diagnosis GBM patients, we aim to elucidate this link between cystic GBM and survival. Within this cohort, 88 patients had a significant cystic component at presentation as identified on MRI. Compared to noncystic GBM (n=405), cystic GBM patients had significantly better overall survival (15 vs 22 months median, log-rank, p=0.001) and were significantly younger at the time of presentation (t-test, p=0.002). However, within patients that received current standard-of-care treatment (n=184), cystic GBM (n=40) was not as beneficial for outcome (22 vs 25 months, log-rank, p=0.3). We also did not observe a significant survival benefit when comparing this standard-of-care cystic cohort to cystic GBM patients diagnosed before the standard was established (n=19, 25 vs 23 months, log-rank, p=0.3), but the analogous result for noncystic GBM patients gives a sizeable benefit, as expected (n=144, n=111, respectively, 22 vs 12 months, log-rank p < 0.0001). Together, these results on current standard-of-care may explain later studies that note no significant survival benefit for cystic GBM patients receiving current standard-of-care. We also report differences in the absolute and relative sizes of imaging abnormalities on MRI and in prognostic impact of cysts based on sex. We discuss current hypotheses for these observed differences, including the possibility that the presence of a cyst could be indicative of a less aggressive tumor.

Abstract There have been increasing efforts to standardize MRI scanning protocols for both routine and clinical trial imaging of glioma. The recent BTIP guidelines propose acquisition of T2-weighted images after gadiolinium (Gd) based contrast injection and before post-contrast 3D T1-weighted (T1W) images to control timing of images after contrast administration. While Gd-contrast shortens T1W signal and appears hyperintense on magnetic resonance imaging (MRI), Gd-contrast also shortens T2W signal, which could result in unintended alternations in T2W signal intensities. This has potential implications for model-based approaches for GBM diagnosis and the differential diagnosis of other tumor types (e.g., lymphoma) that rely on T2W signal characteristics. To our knowledge, no neuroimaging studies have formally evaluated the impact of Gd contrast on T2W imaging. We acquired multi-echo T2 sequences (TE=14, 28, 41, 55, 69 ms) both prior to and after the administration of Gd for 5 patients suspected of having a brain tumor. Quantitative T2 maps were created by fitting the signal decay to a signal exponential with the Levenburg-Marqurdt algorithm. Percent difference maps were calculated to compare the quantitative T2 changes between the pre- and post-Gd T2 maps. Additionally, we performed a Wilcoxon Rank-Sum test comparing T2 values within regions of interest (ROIs) representing the tumor and normal appearing white matter (NAWM). Significant differences between the T2 values in the tumor and NAWM ROIs were seen for 4 and 3 of the patients, respectively (all p-values < 0.01). Our initial data suggests that the administration of Gd contrast may affect T2 values in both the tumor and NAWM, which could impact quantitative image-based models and diagnoses in glioma. We are currently replicating these preliminary results in an expanded cohort.

Abstract Dynamic susceptibility contrast (DSC) MRI measures of brain tumor cerebral blood volume (CBV) are able to predict grade, overall survival and response to treatment. Wide-spread acceptance of DSC-MRI has been challenged by the need to balance contrast agent dose and CBV accuracy. The goal of this study was to identify and validate single-dose, BTIP compliant, DSC-MRI protocols. Using a validated, patient-based DRO, we evaluated CBV accuracy across a range of acquisition parameters (field strength, TR, TE, flip angle, multi-echo acquisitions, dosing protocols) and post-processing steps. To validate the optimal protocols, we next collected DSC-MRI data following ASFNR’s recommended “double – dose” approach, where a single-dose preload (to minimize T1 effects) is given prior to a second bolus injection (for DSC-MRI data acquisition). The single-dose DSC-MRI data was collected during the preload bolus injection. Consistency of the derived CBV data, visual agreement and data characteristics (e.g. CNR) was statistically evaluated. When using a single-dose and routine single-echo pulse sequence, the DRO analysis found that a low flip angle (LFA = 30o) and 30ms TE provided the highest CBV accuracy (concordance correlation coefficient (CCC) = 0.92) and precision (coefficient of variation (CV) = 8.2%)). For comparison, the maximum accuracy found with the DRO utilizes a double-dose injection protocol and yielded a CCC of 0.98 and CV of 6.8%. Single-dose, multi-echo acquisitions provided higher accuracy than the LFA data and matched that found with the double-dose approach. In patients (data collection ongoing), the agreement between single-dose LFA (n > 40) or multi-echo (n > 40) based CBV values and the reference double-dose approach was very high (CCC > 0.94) and were statistically equivalent. Optimized single-dose DSC-MRI protocols provide highly accurate CBV data, use lower doses of contrast agent, and simplify scan procedures, indicating their potential for robust use in clinical practice and trials.

Abstract BACKGROUND Conventional magnetic resonance imaging (MR) guides patient care in GBM. However, there is mounting awareness that MR enhancement is non-specific reflecting either tumor progression or non-tumoral inflammatory changes. Histological evaluation of GBM is held as the gold standard for disease assessment. However, the invasiveness of this methodology and the sample sparsity limit its usefulness. Methods to infer histological underpinnings of MRI are needed to improve clinical care. METHODS A transfer learning mixed effects model based on T1Gd and FLAIR MR voxel based image features trained and cross-validated to predict FPKM values of CD68, CASP3, CD8A and IL13RA2. Training data included RNAseq from 38 image-localized biopsies from 15 newly-diagnosed GBM patients. These models were then applied to two independent patients, chosen based on therapy and the quality of image registration, at three different time points, just prior to receiving IL13RA2 targeted CAR-T therapy for rGBM, after 2 cycles and after 4 cycles, resulting in a voxel-based prediction of the relative expression levels of these genes. RESULTS Cross-validation of the proposed machine learning models demonstrated a Pearson Correlation coefficient between predicted and observed FPKM values of 0.89 (CD68), 0.92 (CASP3), 0.93 (CD8A), and 0.88 (IL13RA2). When the models were applied to the two CAR-T patients, CD68-modeled expression levels were seen to increase throughout therapy for one patient, while remaining stable for the other. Survival from first CAR-T infusion was, respectively, 412 and 83 days suggesting that the increased CD68 was indicative of therapeutic effectivity. Further, the spatial activity predictions were consistent with expected therapeutic action as the correlation coefficient between CASP3 and the product of IL13R2 and CD8A expression was 0.81, suggesting the T-cells targeting IL13RA2 were inducing cell death. CONCLUSIONS Preliminary results with our model highlights the potential of spatially resolved radiomic maps to provide insight into regional therapeutic effectivity.