Citations

Abstract BACKGROUND While glioblastoma (GBM) is more prevalent in males, studies show that females with GBM tend to have longer overall survival (OS) than males. Pretreatment neutrophil to lymphocyte ratio (NLR) has also proven to be prognostic in GBM, with lower NLR having favorable outcomes. This secondary analysis of a prospective randomized trial of proton vs. photon intensity modulated radiotherapy aims to explore the interaction of gender and NLR on GBM outcomes. METHODS Analysis was performed on the full patient population. Kaplan-Meier methods estimated OS with censoring at last follow-up for those who were alive. Univariate (UVA) and multivariate (MVA)Cox proportional hazards models assessed predictors of OS. RESULTS Of 90 patients, 77 were included (43 males; 34 females) with median age of 52 years (range: 26–82 years). Median OS was longer for females than males (30.7 vs 18.2 months, p=0.004). On UVA, patients with NLR below median value (NLR= 3.1) tended to have longer OS than those above median, though not meeting statistical significance (23.1 vs. 17.9 months, p=0.051). Difference in OS was statistically significant in females (OS 36.4 months NLR >median vs. 16.7 months NLR< median, p=0.002), but not in males (OS 17.8 months NLR >median vs. 19.1 NLR< median, p=0.95). MVA analysis was consistent, with female gender predicting reduced hazard ratio (HR) (0.28, p=0.034) and females with below median NLR showing a reduced HR over those with above median (0.28, p=0.005). Again, males did not benefit (HR 0.90, p=0.77). CONCLUSION Consistent with prior publications, females and all patients with lower pre-treatment NLR with newly diagnosed GBM had longer OS. However, combining these two factors revealed that the benefits from lower pre-treatment NLR were conferred only in females with no impact on males. This different impact of NLR between genders may suggest innate immune differences in gender during response to malignancy.

Abstract PURPOSE/OBJECTIVE Dose constraints for re-irradiation of recurrent primary brain tumors are not well-established, especially for treatment volumes too large for stereotactic radiotherapy. This prospective trial was performed to test dose constraints for conventionally-fractionated re-irradiation of recurrent primary brain tumors MATERIALS/METHODS A single-institution, prospective trial of 21 adults with recurrent brain tumors was performed. Electronic dosimetry records from the first course of radiation (RT1) were obtained and deformed onto the simulation CT for the second course of radiation (RT2). Treatment plans for RT2 were developed that met protocol-assigned dose constraints for RT2 alone and the composite dose of RT1+RT2. Dose constraints were also based on histology and interval since RT1. The primary endpoint was the rate of symptomatic brain necrosis after RT2. RESULTS Twenty one adults enrolled from March 2017 to May 2018. Twelve had glioblastoma, four had oligodendroglioma, two had anaplastic astrocytoma, and one each had choroid plexus papilloma, hemangiopericytoma, and pleomorphic xanthroastrocytoma (PXA). Twenty patients were treated with VMAT and one was treated with proton CSI. Median RT1-RT2 interval was 45 months (range, 9–141 months). Median RT2 dose was 42.8 Gy (range, 17.5–60 Gy). Median PTV volume was 208 cc (range, 7–1537 cc). Median imaging followup was 9 months (range, 1–20 months). Two months after RT2, the patient with PXA developed a trapped temporal horn adjacent to the RT2 treatment volume; pathology from emergent resection revealed necrotic brain tissue. The patient recovered fully and lived another 18 months until dying of disease progression. No other patient developed symptomatic radionecrosis. Median overall survival from RT2 for all patients was 11 months (range, 3–20 months). CONCLUSION Re-irradiation can be performed with conventionally fractionated schemes. Given the low rate of symptomatic radionecrosis, the dose constraints described here are a starting point for future studies of conventionally fractionated re-irradiation.

Abstract PURPOSE To compare tumor progression based on clinical radiological assessment and on Response Assessment in Neuro-Oncology (RANO) criteria between GBM patients treated with proton radiotherapy (PT) vs. photon intensity modulated radiotherapy (IMRT). METHODS Eligible patients were enrolled on the described prospective phase II trial and had MR imaging at baseline and follow-up beyond 12 weeks from treatment completion. ‘Clinical’ progression was based on a radiology report of progression in combination with changes in treatment due to suspected disease progression. A single blinded observer applied RANO criteria to determine the RANO-based tumor progression. RESULTS Of 90 enrolled patients, 66 were evaluable, with median follow-up of 19.8 (Range: 3.2–65.1) months; median of 22.6 months for PT (n=25) vs. 18.9 months for IMRT (n=41). Median time to progression (TTP) was 7.9 months based on clinical progression criteria (8.1 months IMRT, 6.3 months PT) and 7.2 months (7.3 months IMRT, 5.7 months PT) by RANO criteria (p=ns for all). Median ‘clinical’ progression-free survival (PFS) was 8.7 (Range: 6.4–11.1) months; 8.9 months IMRT vs. 8.7 months PT (p=0.065). Median RANO PFS was 8.3 (range, 5.8–11.6) months: 8.3 months IMRT vs. 6.9 months PT (p=0.226). There were 14 discrepant cases: 3 had progression based on ‘clinical’ but not RANO criteria, and 11 had progression based on RANO but not ‘clinical’ criteria. CONCLUSION Based on this secondary analysis of a randomized trial of PT vs. IMRT for GBM, there was no difference in tumor progression relative to treatment technique used. There was no statistical difference in PFS noted between clinical and RANO-based assessments, but RANO criteria identified progression more often than clinical assessment, and TTP was shortened with the use of RANO criteria alone. Further development of tumor assessment tools that improve consistency and accuracy of determining tumor progression are needed to guide therapeutic trials in GBM.

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.

Abstract Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.

Abstract BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The pathological tumor cell density was available for each sample. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. A Gaussian Process (GP) classifier was built to predict tumor cell density using radiomic features. Another 6 patients were used to test the training model. These patients had a total of 31 biopsy samples. The images of each test patient were purposely normalized using a different approach, i.e., using the CSF instead of the whole brain as the reference. This was to mimic the practical scenario of image source discrepancy between different patients. Domain adaptation was applied to each test patient. RESULTS Among the 18 training patients, the leave-one-patient-out cross validation accuracy is 0.81 AUC, 0.78 sensitivity, and 0.83 specificity. When the trained model was applied to the 6 test patients (purposely normalized using a different approach than that of the training data), the accuracy dramatically reduced to 0.39 AUC, 0.08 sensitivity, and 0.61 specificity. After using domain adaption, the accuracy improved to 0.68 AUC, 0.62 sensitivity, and 0.72 specificity. CONCLUSION We provide candidate enabling tools to address reproducibility in radiomics models by using domain adaption algorithms to account for discrepancy of the images between different patients.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Abstract High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

Abstract BACKGROUND NRG-CC001 sought to evaluate the neuro-protective effects of avoiding the peri-hippocampal stem cell niche using intensity-modulated radiotherapy during WBRT. METHODS Patients with brain metastasis were stratified by RPA class and prior radiosurgery/surgery and randomized to WBRT+M or HA-WBRT+ Memantine (M) (30Gy in 10 fractions). Standardized NCF tests and the M.D. Anderson Symptom Inventory Brain Tumor (MDASI-BT) were obtained at baseline, 2, 4, 6, and 12 months (mos). The primary endpoint was NCF failure defined using the reliable change index. Pre-specified secondary endpoints included patient-reported symptoms using the MDASI-BT. Time to NCF was reported as cumulative incidence (with death without NCF failure as a competing risk); between-arms differences were tested using Gray’s test. Deterioration at discrete time-points were tested using chi-square tests. MDASI-BT symptom burden, interference, and cognitive and neurologic burden were analyzed using mixed effects models and t-tests within the model using Hochberg’s multiplicity adjustment. RESULTS A total of 518 patients were randomized from 7/2016 to 3/2018. Median follow-up for alive patients was 12.1 mos, with no difference between arms in terms of toxicity, overall survival or intracranial progression-free survival. HA-WBRT+M was associated with lower risk of NCF failure (adjusted HR=0.739, 95% CI: 0.577–0.945, p=0.0.016), with differences first noted at 4 mos in Trail Making Test Part-B (23.3% vs. 40.4% deteriorated, p=0.012). Age did not dilute treatment effect. HA-WBRT+M was associated with reduced cognitive symptom burden in an imputed model (estimate=-0.29, p=0.0425), and also reduced overall symptom burden (p< 0.0001) and interference (p< 0.0016) at 6 mos. CONCLUSIONS The addition of HA to WBRT+M preserved NCF and reduced patient reported cognitive symptom burden, overall symptom burden and symptom interference and should be considered standard of care for any patient fit enough to have WBRT. Supported by grants UG1CA189867 (NCORP), U10CA180868 (NRG Oncology Operations), DCP from the National Cancer Institute.