Bendok, Bernard R.

Abstract INTRODUCTION Locations of gliomas may influence clinical presentations, molecular profiles, treatment options, and prognoses. Using the Mayo Clinic Arizona Cancer Center registry, we analyzed the frequency at which gliomas were identified in different regions of the brain. We evaluated molecular profiles, clinical courses and survival by anatomic location. METHODS Registry was queried to include patients with glioma over a 10-year period. Statistical analyses were used to compare demographic, genetic, and clinical characteristics among patients with gliomas in different locations. RESULTS 182 gliomas were identified. Of the tumors confined to a single lobe, there were 51 frontal (28.0%), 50 temporal (27.5%), 22 parietal (12.1%), and 7 occipital tumors (3.8%) identified. Multifocal disease was noted in 38 patients (20.9%). Tumors affecting temporal lobe were associated with reduced overall survival when compared to all other tumors (11.0 months vs. 13.0 months, log-rank p=0.0068). However, this disparity became insignificant when adjusted for tumor grade, age, and surgical approach [HR(95% CI) 1.26(0.87, 1.82), p=0.212]. Out of 82 cases tested for IDH-1, 10 were mutated (5.5%). IDH-1 mutation was present in 6 frontal, 2 temporal, 1 thalamic, and 1 multifocal tumor. Out of 21 cases tested for 1p19q deletions, 12 were co-deleted, 9 of which were frontal lobe tumors. MGMT methylation was assessed in 45 cases; 7 of 14 frontal tumors and 6 of 13 temporal tumors were methylated. ATRX loss was detected in 2/42 assessed cases. CONCLUSION Our results support the hypothesis that the anatomical locations of gliomas influence patients’ clinical courses. Tumors involving the temporal lobe were associated with poorer survival, though this association appeared to be driven by these patients’ more aggressive tumor profiles and higher risk baseline demographics. Molecular analysis was limited by low prevalence of genetic testing in the study sample, highlighting the importance of capturing this information for all gliomas.

Abstract Determining effective therapies for patients with recurrent glioblastoma remains difficult especially in small early phase clinical trials. Currently utilized estimators of treatment response rely on two-dimensional measurements of tumor progression (e.g. RANO, RECIST, MacDonald) that have often fallen short in reliably connecting patients who respond with positive survival outcomes. This has prompted consideration of advanced imaging and volumetric measurements by the RANO working group and others. Our proposed approach, Days Gained (DG), uses volumetric measurements to calculate a personalized response metric based on change in tumor growth characteristics pre/post-treatment. DG has shown promise for connecting imageable treatment response with clinically meaningful (statistically significant) survival benefit when applied in upfront and recurrent therapy settings, even in the context of coincident bevacizumab treatment. In this study, we seek to consider a new cohort of patients receiving bevacizumab in combination with other novel therapies to distinguish any subpopulation of patients with meaningful response. In this work, we identified a subset of 16 patients from bevacizumab clinical trials with data available for DG analysis requiring serial imaging (T1Gd and FLAIR) prior to and after the first cycle of therapy. Each trial evaluated bevacizumab alone versus bevacizumab plus either dasatinib or TRC105 in recurrent disease. Discrimination of time to progression (TTP) was evaluated at DG thresholds identified from our prior analysis of patients treated with bevacizumab. Using previously identified DG thresholds for clinically significant treatment benefit, in our cohort, DG measured by serial FLAIR imaging was significant in predicting longer TTP (120 DG, p=0.014), but T1Gd DG scores did not attain significance (p=0.630). Given the limited insights in connecting response metrics to accurately predicted outcomes, these data further support the use of DG as a burgeoning marker of treatment response (or lack of response) that may be useful for more routine integration into clinical trials.

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 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 5-aminolevulinic acid is a reliable tool for optimizing high-grade glioma resection. However, its efficacy in low-grade glioma resection remains unclear. OBJECTIVE To study the role of 5-aminolevulinic acid in low-grade glioma resection and assess positive fluorescence rates and effect on the extent of resection. METHODS A systematic review of PubMed, Google Scholar, and Cochrane was performed from the date of inception to February 1, 2019. Studies that correlated 5-aminolevulinic acid fluorescence with low-grade glioma in the setting of operative resection were selected. Studies with biopsy only were excluded. Positive fluorescence rates were calculated. Quality index of the selected papers using the Downs and Black criteria checklist was provided. RESULTS Twelve articles met the selection criteria with 244 histologically-confirmed low-grade glioma patients who underwent microsurgical resection. All patients received 20 mg/kg body weight of 5-aminolevulinic acid. Only 60 patients (n=60/244; 24.5%) demonstrated visual intra-operative 5-aminolevulinic acid fluorescence. The extent of resection was reported in 4 studies, however, the data combined low- and high-grade tumors. Only 2 studies reported on tumor location. Only 3 studies reported on clinical outcomes. The Zeiss OPMI Pentero microscope was most commonly used across all studies. The average quality index was 14.58 (range: 10–17) which correlated with an overall good quality. CONCLUSION There is an overall low correlation between 5-aminolevulinic acid fluorescence and low-grade glioma. Advances in visualization technology and using standardized fluorescence quantification methods may further improve the visualization and reliability of 5-aminolevulinic acid fluorescence in low-grade glioma resection.

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumor in adults with a median overall survival (OS) of 15months. Despite advancements in treatments, prognosis is dismal and the prognostic significance of tumor location is not entirely understood. METHODOLOGY: In our study, we investigated sex-specific volumetric, tumor growth kinetics, and outcome differences among GBMs in various brain locations. Primary GBM patients with pretreatment magnetic resonance imaging (MRI) data (N=289, 173 males, 116 females) were selected from our brain tumor repository. Tumor abnormality was segmented on T1-weighted post-gadolinium contrast agent (T1Gd) MRIs. We utilized the Harvard-Oxford brain atlases to determine the location of GBMs. RESULTS Overall, our study found smaller tumors in the left hemisphere. This may be expected as left-hemispheric GBM symptoms could present earlier, leading to earlier diagnosis and treatment. However, when the cohort was split by sex, we found this observation significant for females only in the parietal lobe (p < 0.0001). Further, female GBMs demonstrated smaller necrotic volume in the left hemisphere (p = 0.030). Sex-specific differences in incidence were noted in the temporal and occipital lobes (2M:1F). Comparing tumor growth kinetics in different brain locations and hemispheres, females had significantly lower tumor proliferation rates in the left hemisphere (p = 0.009) and lower tumor proliferation rates in the left frontal lobe (p = 0.031). Controlling for treatment, patients with frontal lobe tumors had significantly longer OS compared to those with GBMs in the temporal lobe (p = 0.046, 312 days). Differences in growth velocities were noted between frontal and parietal lobe with frontal GBMs having lower velocities in comparison to parietal lobe GBMs. CONCLUSION Together, our results demonstrate that tumor growth and proliferation rates may vary based on location and sex. Additional research is needed to further explore the clinical significance of tumor location.

Roberts Bartholow, a physician, born and raised in Maryland, was a surgeon and Professor in Medicine who had previously served the Union during the Civil War. His interest in scientific research drove him to perform the first experiment that tested the excitability of the human brain cortex. His historical experiment on one of his patients, Mary Rafferty, with a cancerous ulcer on the skull, was one of his great accomplishments. His inference from this experiment and proposed scientific theory of cortical excitation and localization in humans was one of the most critically acclaimed topics in the medical community, which attracted the highest commendation for the unique discovery as well as criticism for possible ethical violations. Despite that criticism, his theory and methods of cortical localization are the cornerstone of modern brain mapping and have, in turn, led to countless medical innovations.