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High grade gliomas (HGG) are aggressive primary brain malignancies typified by diffuse invasion, genetic heterogeneity, and a universally fatal outcome. MRI-defined contrast-enhancing (CE) tumor burden serves as the clinical standard that guides maximal surgical resection and post-therapy response assessment. However, HGGs also comprise an invasive non-enhancing (NE) tumor margin that extends beyond the CE core and harbors the cells that contribute to recurrence. Sampling restrictions have hindered the comprehensive study of these NE HGG cell populations driving tumor progression. Herein, we present an integrated multi-omic analysis of 313 spatially matched multi-regional CE and NE tumor biopsies from 68 HGG patients, performing whole exome and RNA sequencing of both IDH wild-type and IDH mutant HGGs. We report spatially restricted molecular profiles in IDH-mutant HGG, highlighting a concern for sampling bias given the importance of molecular diagnosis and prognostication in IDH-mutant HGG. Regardless of IDH status, we found that NE tumor regions harbored the highest proportion of private mutations, which reflects an increased development of regional genomic complexity in infiltrative tumor. The multiregional genomic profiling of our IDH wild-type HGG cohort reveals that EGFR and NF1 somatic alterations occur as mutually exclusive events in 98.7% of tumors. However, we resolved rare low allele frequency co-alterations of EGFR and NF1 within the NE region. We find this co-occurrence enriched in recurrent tumors, pointing to the early emergence of NF1 inactivation in the NE regions. We constructed genomic models predictive of recurrent disease from both NE and CE regions, which highlight the occurrence of clonal EGFR copy number alterations and NF1 loss as clonal or subclonal events, respectively, emphasizing the regional and temporal complexity of well-studied canonical driver alterations. We detailed the spatially unique acquisition of multiple distinct EGFR alterations giving rise to intra-tumoral EGFR mosaicism, a challenge in the implementation of EGFR directed therapies. Our study also identified two transcriptomic clusters delineated by the significant overrepresentation of neuronal (NEU) and glycolytic/plurimetabolic (GPM) pathway-based functional states in the NE region. NE regions of the NEU subtype harbor the greatest proportion of private mutations, suggesting these infiltrative tumor cells accumulate alterations without clonal expansion. GPM populations conversely displayed a less branched phylogeny and were transcriptionally enriched in immune cell signatures. This phenotypic dichotomy between GPM and NEU populations supports the growing body of evidence that invasive GBM cells either take on a neuronal phenotype for active invasion or a more metabolic phenotype involving interaction with astrocytes, other glial cells, and infiltrating immune cells. Citation Format: Mylan R. Blomquist, Leland S. Hu, Fulvio D'Angelo, Taylor M. Weiskittel, Francesca P. Caruso, Shannon P. Fortin Ensign, Christopher Sereduk, Gustavo De Leon, Lee Curtin, Javier Urcuyo, Ashlynn Gonzalez, Ashley Nespodzany, Teresa Noviello, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Bernard R. Bendok, Richard S. Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Kliment Donev, Maciej Mrugala, Alyx Porter, Anna Lasorella, Kristin R. Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan L. Tran. Multiregional sampling of high grade glioma identifies regional biologic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1507.

The influence of biological sex differences on human health and disease, while being increasingly recognized, has long been underappreciated and underexplored. While humans of all sexes are more alike than different, there is evidence for sex differences in the most basic aspects of human biology and these differences have consequences for the etiology and pathophysiology of many diseases. In a disease like cancer, these consequences manifest in the sex biases in incidence and outcome of many cancer types. The ability to deliver precise, targeted therapies to complex cancer cases is limited by our current understanding of the underlying sex differences. Gaining a better understanding of the implications and interplay of sex differences in diseases like cancer will thus be informative for clinical practice and biological research. Here we review the evidence for a broad array of biological sex differences in humans and discuss how these differences may relate to observed sex differences in various diseases, including many cancers and specifically glioblastoma. We focus on areas of human biology that play vital roles in healthy and disease states, including metabolism, development, hormones, and the immune system, and emphasize that the intersection of sex differences in these areas should not go overlooked. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.

INTRODUCTION Dendritic cells (DCs) are potent antigen presenting cells that can be exploited to initiate an adaptive anti-tumoral immune response. DC vaccine clinical trials for primary glioblastoma (GBM) have reported prolonged progression-free survival without any impact on overall survival (OS). We report a radiomics approach that identifies a subpopulation of patients with prolonged OS in clinical trial MC1272. METHODS Twenty adults with primary GBM undergoing standard-of-care therapy were enrolled in MC1272. Autologous DCs were pulsed with allogenic GBM cell lines to generate vaccines that were administered for up to twelve cycles. Standard brain imaging was obtained at the initiation of treatment and two months afterwards. An independent cohort of image-localized biopsies underwent RNA sequencing followed by cellular deconvolution to estimate T cell abundance. A machine learning model was trained to predict intratumoral T cell abundance from imaging features, and the model was applied to MC1272 patient imaging. RESULTS Voxelwise predictions of T cell abundance were generated for each patient’s pre- and post-treatment images. The difference in total intratumoral T cell abundance between imaging timepoints classified patients into increasing or decreasing T cell groups. Patients whose T cells increased had longer OS (median, 21 months) than those whose T cells decreased (median, 10 months; p=0.0035). The significance remained in a Cox proportional hazards analysis that adjusted for patient age and sex (p=0.011). CONCLUSIONS A spatially-resolved radiomics model detected that an intratumoral T cell influx after DC vaccine therapy was associated with prolonged OS. The “post-treatment” imaging in this study was obtained a mere two months after DC vaccine initiation, suggesting that our radiomics model can provide an early indication of treatment responsiveness and prognosis in these patients.

INTRODUCTION Glioblastoma (GBM) is a diffusely invasive primary brain tumor with significant spread of tumor cells to the periphery of visible image abnormality. Enhancement of Gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) has historically been considered a confirmation of local breakdown of the blood brain barrier (BBB) and sufficient drug delivery to the bulk of tumors. In this work, we used GBM-derived xenograft (PDX) models to compare drug delivery in GBM brain for high and low BBB-permeable drugs. MATERIALS AND METHODS Five patient-derived orthotopic xenograft models from two GBM cell lines (GBM39 and GBM12) were co-dosed with erlotinib and osimertinib, two drugs with low and high BBB-permeability, respectively. T1Gd and T2-weighted MRIs were acquired from all animals prior to model sacrifice. Tumors were manually segmented on denoised and standardized MRIs and intensity patterns were captured using first and second order statistical features in the moving 3x3 kernel. We compared drug levels found in Matrix Assisted Laser Desorption Ionization (MALDI) in T1Gd enhancement, T2 enhancement, and normal brain. We also performed linear regression modeling to predict drug levels using MRI features. Model performance was measured using root mean squared error (RMSE). RESULTS Our analysis showed correlations between imaging features and MALDI drug levels. Osimertinib had a uniform distribution across the brain for all animals and all cell lines, consistent with our expectation for a high BBB-penetrant drug. Erlotinib showed the highest drug levels in T2 for GBM39 and in T1Gd for GBM12. Regression models showed promising results for predicting Erlotinib with a low RMSE of 0.037. CONCLUSION Our preliminary results suggest MRI can be predictive of drug levels for low-BBB penetrant drugs. Understanding the relationship between MRIs and drug distribution in diffuse tumors can be beneficial to developing effective treatment.

Purpose:Quantify in vivo biomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk.Methods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high). A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors.Results: 50 normal risk and 86 high risk patients were included. Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (N = 55) compared to normal risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.Conclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

Local response prediction for brain metastases (BM) after stereotactic radiosurgery (SRS) is challenging, particularly for smaller BM, as existing criteria are based solely on unidimensional measurements. This investigation sought to determine whether radiomic features provide additional value to routinely available clinical and dosimetric variables to predict local recurrence following SRS.Analyzed were 408 BM in 87 patients treated with SRS. A total of 440 radiomic features were extracted from the tumor core and the peritumoral regions, using the baseline pretreatment volumetric post-contrast T1 (T1c) and volumetric T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences. Local tumor progression was determined based on Response Assessment in Neuro-Oncology‒BM criteria, with a maximum axial diameter growth of20% on the follow-up T1c indicating local failure. The top radiomic features were determined based on resampled random forest (RF) feature importance. An RF classifier was trained using each set of features and evaluated using the area under the receiver operating characteristic curve (AUC).The addition of any one of the top 10 radiomic features to the set of clinical features resulted in a statistically significant (P0.001) increase in the AUC. An optimized combination of radiomic and clinical features resulted in a 19% higher resampled AUC (mean = 0.793; 95% CI = 0.792-0.795) than clinical features alone (0.669, 0.668-0.671).The increase in AUC of the RF classifier, after incorporating radiomic features, suggests that quantitative characterization of tumor appearance on pretreatment T1c and FLAIR adds value to known clinical and dosimetric variables for predicting local failure.

Intra-tumor genetic heterogeneity is an important cause of treatment failure of GBM. Using MRI and image-localized biopsies, it is possible to train machine learning (ML) models to predict regional genetic status. However, biopsy samples are limited, making it difficult to train a robust ML model. We proposed a data-inclusive model called Weakly Supervised Ordinal Support Vector Machines (WSO-SVM) which leverages the vast amount of MRI data outside the sparsely sampled biopsy regions to augment the biopsy samples to improve ML accuracy. Our study included a unique dataset of 104 image-localized biopsies with spatially matched multiparametric MRI from 30 untreated Glioblastoma (GBM) patients. Each biopsy sample went through genetic sequencing analysis and our study focused on two GBM hallmark genes, EGFR and PDGFRA. For each gene, a biopsy sample was labeled as “altered” if the copy number of this gene was amplified or a mutation was found, and “non-altered” otherwise. From the localized region of six MRI contrasts from T1gd, T2w, diffusion and perfusion imaging, over 300 texture features were extracted. To account for biopsy sample location uncertainty, six neighboring regions of the biopsy sample including four neighbors two pixels away from the biopsy location and two neighbors on adjacent slices were also included in model training. WSO-SVM achieved 0.83 accuracy, 0.77 sensitivity, and 0.86 specificity for classifying EGFR; 0.77 accuracy, 0.74 sensitivity, and 0.79 specificity for classifying PDGFRA, based on 10-fold cross validation. Furthermore, using the trained models, we generated regional EGFR and PDGFRA alteration maps for each patient within the enhancing and non-enhancing tumoral areas. On average we found a greater proportion of enhancing tumor with co-alteration than non-enhancing tumor, while this trend was reversed when considering only one altered gene. The ratio of EGFR vs PDGFRA alterations was higher in non-enhancing tumor than enhancing tumor.

Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

Choosing effective chemotherapies for intravenous delivery to brain tumors is challenging, especially given the protective nature of the blood brain barrier (BBB). Connecting drug distribution to non-invasive, pre-surgical magnetic resonance imaging (MRI) could allow for predictive insight into drug distribution. In a previous study, we found that T2Gd images were predictive of a low BBB penetrant drug (Cefazolin), and FLAIR images were predictive of a high BBB penetrant drug (Levetiracetam). While these results are promising, we further seek to explore how advanced MRI sequences might inform image-based models of drug distribution. Prior to surgery, we acquired advanced dynamic contrast enhanced (DCE) and diffusion weighted imaging (DWI) MRI sequences for eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) in addition to the anatomic MRIs. All resulting quantitative maps and acquired images were co-registered. Prior to incision, patients received injections of cefazolin and levetiracetam. Next, multiple blood samples and biopsies were collected during surgery. Biopsies and plasma samples were analyzed for drug concentration using liquid chromatography mass spectrometry (LCMS), and biopsy drug levels were reported as Brain-Plasma Ratio (BPR). Mean image intensity was extracted from a 15x15 voxel window surrounding the biopsy location. We performed linear regression analyses to determine which combination of images were predictive of BPR. We found that considering quantitative imaging improved our initial ability to predict BPR for both drugs. For cefazolin, the third diffusion tensor eigenvalue (L3) map was significantly correlated with BPR (p< 0.001, R2= 0.36). For levetiracetam, the best model consisted of a combination of images and maps with the L3 map and the isotropic diffusion map (P) being the most influential (p= 0.001, R2= 0.63). Advanced MRI-based modeling is a promising tool for forecasting drug distribution in brain tumors and could be of great importance for understanding efficacy and selecting therapeutic strategies.

OBJECTIVE Glioblastoma (GBM) is the most common malignant primary brain tumor in adults, with males more commonly affected than females(1.6:1). Despite advancements in treatments, prognosis is dismal with a median overall survival of 15 months. Our aim was to investigate sex as a variable in GBM patient survival after receiving incremental levels of standard-of-care treatment regimens – different extents of surgical resection and different numbers of cycles of adjuvant temozolomide chemotherapy. METHODS Drawing from our extensive multi-institutional brain tumor repository, we investigated GBM subjects with overall survival (OS), extent of resection (EOR), number of temozolomide (TMZ) cycles, and sex data (n=620, males: n=387, females: n=233). Cox proportional hazard ratios were computed to investigate the multivariable predictive value of the patient variables with OS. Patients were then divided into groups based on their sex, EOR (either biopsy, subtotal resection (STR) or gross total resection (GTR)), and TMZ cycles (I: < 6 cycles, II: 7-11 cycles and III: >12 cycles). RESULTS We observed that STR was beneficial for females (HR=0.52; CI=0.33-0.83; p-value=0.013), while for males the benefit was not detected (HR=0.73; CI=0.46-1.15; p-value=0.173) for STR but was detectable for GTR (HR=0.58, CI=0.37-0.90; p-value=0.014). Females receiving 7-11 cycles of TMZ showed a survival benefit (HR=0.52; CI=0.12-0.53; p-value=0.048) while males in the same group did not (HR=0.74; CI=0.46-1.19; p-value=0.21), in comparison to those in group I of TMZ cycles. No sex differences were identified in patients receiving < =6 cycles or >=12 cycles. CONCLUSION Together, our results contribute to the growing literature that sex differences exist in GBM patients, even in response to standard-of-care therapies. This should be accounted for when designing clinical trials for GBM so that we may advance our pursuit to deliver personalized medicine.

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.

Glioblastoma (GBM) is the most aggressive primary brain tumor and can have cystic components, identifiable through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7-23% of GBMs and report mixed results regarding their prognostic impact. Using our retrospective cohort of 493 patients with first-diagnosis GBM, we carried out an exploratory analysis on this potential link between cystic GBM and survival. Using pretreatment MRIs, we manually identified 88 patients with GBM that had a significant cystic component at presentation and 405 patients that did not. Patients with cystic GBM had significantly longer overall survival and were significantly younger at presentation. Within patients who received the current standard of care (SOC) (N=184, 40 cystic), we did not observe a survival benefit of cystic GBM. Unexpectedly, we did not observe a significant survival benefit between this SOC cystic cohort and patients with cystic GBM diagnosed before the standard was established (N=40 with SOC, N=19 without SOC); this significant SOC benefit was clearly observed in patients with noncystic GBM (N=144 with SOC, N=111 without SOC). When stratified by sex, this significant survival benefit was only preserved in male patients (N=303, 47 cystic). We report differences in the absolute and relative sizes of imaging abnormalities on MRI and the prognostic implication of cysts based on sex. We discuss hypotheses for these differences, including the possibility that the presence of a cyst could indicate a less aggressive tumor.

Many drugs investigated for the treatment of glioblastoma (GBM) have had disappointing clinical trial results. Efficacy of these agents is dependent on adequate delivery to sensitive tumor cell populations, which is limited by the blood-brain barrier (BBB). Additionally, tumor heterogeneity can lead to subpopulations of cells with different sensitivities to anti-cancer drugs, further impacting therapeutic efficacy. Thus, it may be important to evaluate the extent to which BBB limitations and heterogeneous sensitivity each contribute to a drug's failure. To address this challenge, we developed a minimal mathematical model to characterize these elements of overall drug response, informed by time-series bioluminescence imaging data from a treated patient-derived xenograft (PDX) experimental model. By fitting this mathematical model to a preliminary dataset in a series of nonlinear regression steps, we estimated parameter values for individual PDX subjects that correspond to the dynamics seen in experimental data. Using these estimates as a guide for parameter ranges, we ran model simulations and performed a parameter sensitivity analysis using Latin hypercube sampling and partial rank correlation coefficients. Results from this analysis combined with simulations suggest that BBB permeability may play a slightly greater role in therapeutic efficacy than relative drug sensitivity. Additionally, we discuss recommendations for future experiments based on insights gained from this model. Further research in this area will be vital for improving the development of effective new therapies for glioblastoma patients.

Background Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies. Methods DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan–Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM. Results In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan–Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients. Conclusion DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

Background. Neuroanatomic locations of gliomas may influence clinical presentations, molecular profiles, and patients’ prognoses. Methods. We investigated our institutional cancer registry to include patients with glioma over a 10-year period. Statistical tests were used to compare demographic, genetic, and clinical characteristics among patients with gliomas in different locations. Survival analysis methods were then used to assess associations between location and overall survival in the full cohort, as well as in relevant subgroups. Results. 182 gliomas were identified. Of the tumours confined to a single lobe, there were 51 frontal (28.0%), 50 temporal (27.5%), 22 parietal (12.1%), and seven occipital tumours (3.8%) identified. Tumours affecting the temporal lobe were associated with reduced overall survival when compared to all other tumours (11 months vs. 13 months, log-rank p = 0.0068). In subgroup analyses, this result was significant for males [HR (95%CI) 2.05 (1.30, 3.24), p = 0.002], but not for females [HR (95%CI) 1.12 (0.65, 1.93), p = 0.691]. Out of 82 cases tested for IDH-1, 10 were mutated (5.5%). IDH-1 mutation was present in six frontal, two temporal, one thalamic, and one multifocal tumour. Out of 21 cases tested for 1p19q deletions, 12 were co-deleted, nine of which were frontal lobe tumours. MGMT methylation was assessed in 45 cases; 7/14 frontal tumours and 6/13 temporal tumours were methylated. Conclusion. Our results support the hypothesis that the anatomical locations of gliomas influence patients’ clinical courses. Temporal lobe tumours were associated with poorer survival, though this association appeared to be driven by these patients’ more aggressive tumour profiles and higher risk baseline demographics. Independently, female patients who had temporal lobe tumours fared better than males. Molecular analysis was limited by the low prevalence of genetic testing in the study sample, highlighting the importance of capturing this information for all gliomas. Importance of this study. The specific neuroanatomic location of tumours in the brain is thought to be predictive of treatment options and overall prognosis. Despite evidence for the clinical significance of this information, there is relatively little information available regarding the incidence and prevalence of tumours in the different anatomical regions of the brain. This study has more fully characterised tumour prevalence in different regions of the brain. Additionally, we have analysed how this information may affect tumours’ molecular characteristics, treatment options offered to patients, and patients’ overall survival. This information will be informative both in the clinical setting and in directing future research.

BackgroundSex 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.MethodsCombining 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. Pretreatment MR images of 494 glioblastoma patients (299 males and 195 females) were segmented to quantify tumor volumes. Cox proportional hazard (CPH) models and Student’s t-tests were used to assess which variables were associated with survival outcomes.ResultsAmong 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, pConclusionDespite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

BackgroundTemozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.Methods and findingsUsing a cohort of 90 newly-diagnosed GBM patients treated according to the standard of care, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors.ConclusionsOur finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation.

The blood–brain barrier (BBB) excludes the vast majority of cancer therapeutics from normal brain. However, the importance of the BBB in limiting drug delivery and efficacy is controversial in high-grade brain tumors, such as glioblastoma (GBM). The accumulation of normally brain impenetrant radiographic contrast material in essentially all GBM has popularized a belief that the BBB is uniformly disrupted in all GBM patients so that consideration of drug distribution across the BBB is not relevant in designing therapies for GBM. However, contrary to this view, overwhelming clinical evidence demonstrates that there is also a clinically significant tumor burden with an intact BBB in all GBM, and there is little doubt that drugs with poor BBB permeability do not provide therapeutically effective drug exposures to this fraction of tumor cells. This review provides an overview of the clinical literature to support a central hypothesis: that all GBM patients have tumor regions with an intact BBB, and cure for GBM will only be possible if these regions of tumor are adequately treated.

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.

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.

PURPOSE Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma. METHODS We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation). RESULTS The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79). CONCLUSION We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

Sex differences in brain tumor related epilepsy (BTE) as an initial symptom of glioma are rarely investigated. Sex differences are being explored with increased interest more recently. Epidemiological studies have generally indicated overall incidence of epilepsy to be slightly higher in males than females, but a higher incidence of idiopathic generalized epilepsy has been seen in females in some studies. Clinicians regularly face the challenge of anti-epileptic drug prescription determination in patients with newly identified brain mass and would greatly benefit from knowing clinical characteristics in BTE. We analyzed clinical characteristics in patients with pathologically-determined glioma with known seizure status at clinical presentation using non-parametric and Fisher’s Exact tests with the entire cohort and male and female separately. From our clinical data repository, we identified 223 patients (females, n=86 (39%); males, n=137 (61%)) divided by those that presented with seizure (SP, n=96, 43%; F, n= 28; M, n= 68) and those that presented without seizure (nSP, n=127, 57%, F n=58, M n=69). In sex-specific analysis males had a significantly (x2=6.28, p=0.01) higher proportion of SP (49.6%) than females (32.6%). There was a dependence between seizure presentation status and tumor grade in males (x2=5.94, p=0.015). Tumor kinetics in the female cohort showed significantly (p< 0.0001) more diffuse tumors in SP compared to nSP. Right-sided tumors were predominantly nSP, while left-sided tumors were split more evenly between SP and nSP. The predominance of nSP among right-sided tumors seems to be driven by females (74%), male right-sided tumors were split more evenly between SP and nSP. Seizure status is dependent on tumor location for the combined cohort, however not for males (x2=5.23, p=0.16) or females (x2=4.34, p=0.23). Clearly there are sex differences and similarities in clinical characteristics at glioma presentation. Further characterization could lead to improved adherence of national standards for AED prescription.

INTRODUCTION Glioblastoma (GBM) is an aggressive primary brain tumor, known for its poor prognosis. Due to its diffuse invasiveness into normal-appearing brain, localized treatments such as surgical resection and radiotherapy are typically supplemented with chemotherapy. However, to reach invading tumor cells, such antineoplastic drugs must cross the blood-brain barrier (BBB). That is, while angiogenesis induces BBB breakdown in dense tumor regions, the BBB remains rather intact for invading GBM cells. As a result, it is unclear whether BBB-impermeable drugs are delivered at a sufficient level to be effective. METHODS In order to study heterogeneity in BBB breakdown, experiments were conducted using both flank and intracranial patient-derived xenografts (PDXs) treated with the EGFR-targeted monoclonal antibody drug conjugate, depatuxizumab mafodotin (ABT-414). Time-series bioluminescence imaging (BLI) data was used to develop a differential equation model of tumor growth for three PDX cell lines. Data from untreated PDXs, both in flank and intracranially, were used to parameterize tumor proliferation rates. Flank PDX data were used to parameterize individual sensitivity to ABT-414, whereas intracranial PDX data were used to determine the proportion of drug exposed to the tumor. RESULTS Each PDX line differed in response to the study drug ABT-414. As expected, such heterogeneous responses can primarily be attributed to differences in both drug sensitivity and the proportion of drug that reached the tumor. Notably, the estimated proportion of drug that reached the tumor was highest in the PDX line with the longest survival times, despite also having higher estimates of resistance. This suggests that PDXs with greater overall BBB breakdown may respond better to this agent. CONCLUSIONS Although more cell lines are needed to validate our approach, parameterizing this model for PDXs gives valuable insight into the extent of BBB breakdown in patient GBMs and may aid in determining optimal therapies for individual patients.

OBJECTIVES Dogma suggests that for brain tumors, regions of enhancement on T1-weighted gadolinium contrast enhanced (T1Gd) magnetic resonance imaging (MRI) correlate with intravenously delivered drug distribution as enhancement indicates a compromised blood-brain barrier (BBB). However, poor response to intravenous therapies highlights the importance of the diffuse disease beyond enhancing regions. This study investigated whether imaging features can provide an accurate prediction of drug distribution. METHODS Eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) were included in this Phase 0 trial. Presurgery T1-weighted, T1Gd, T2-weighted gadolinium contrast enhanced (T2Gd), and T2-weighted Fluid Attenuated Inversion Recovery (T2FLAIR) MRIs were acquired. All images underwent bias correction using the N4 algorithm, standardization of intensities, and registration. Prior to incision, patients received both an antibiotic cefazolin (6% BBB penetrance) and levetiracetam (80% BBB penetrance), an anti-seizure drug. Subsequently, multiple blood samples and image-guided biopsies were taken and analyzed for drug concentration using liquid chromatography mass spectrometry. Biopsy drug levels are reported as Brain-Plasma Ratio (BPR), the ratio of biopsy concentration relative to plasma concentration. Mean image intensity was extracted from an 8x8 mm window surrounding each biopsy location. Regression analysis was performed to determine which combination of image types were linearly predictive of BPR for both drugs. Correlations were also analyzed according to the biopsy location radiographic appearance. RESULTS Regression analysis revealed that T2Gd intensity was linearly predictive of cefazolin BPR and FLAIR intensity was linearly predictive of levetiracetam BPR (p=0.009 and 0.041, respectively). Grouping samples according the the radiographic appearance revealed that levetiracetam BPR had a similar pattern of values to that of FLAIR intensities and cefazolin BPR had a similar pattern to T2, further supporting the regression analysis results. CONCLUSIONS Local concentrations of drug may be related to T2-weighted signals (T2Gd and T2FLAIR) rather than the gadolinium distribution on T1Gd images.

Glioblastoma (GBM) is the most aggressive primary brain tumor with an average survival of 15 months with standard of care treatment. GBM patients typically present with necrosis surrounded by enhancement on T1-weighted post gadolinium magnetic resonance imaging (T1gd MRI), however some patients present with a significant cystic component. Cysts are caused by different underlying biological mechanisms to necrosis and are important to identify for future clinical investigations. These cystic components can be manually identified through MRI but this process can be time consuming for large patient cohorts. Over the last two decades, our lab has collected serial MRI data of brain tumor patients. With over 70,000 images now in the database and that number increasing daily, it is clear that we have a unique resource for clinical investigation and a need to automate this process. To this end, the aim of this work was to develop and assess the performance of a convolution neural network (CNN) model for automatic detection of cystic GBMs. In this retrospective IRB-approved work, we collected pretreatment MRIs of a patient cohort consisting of 85 patients with a significant cystic component at presentation along with 400 non-cystic GBM, both identified manually through MRI. Image slices with a view of the cystic component were used as positive samples for training. Data were randomly split into training, validation, and test sets using a 70:15:15 ratio. The proportion of positive to negative cases was comparable between sets. Prior to training, we used image augmentation techniques to compensate for the class imbalance in our data. Our results showed that deep learning networks can automatically detect cystic GBMs on MRIs with high accuracy and thus illustrates the potential use of this technique in clinically relevant settings.

Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of only 15 months with standard of care treatment. Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, another morphological measure of the complexity of pixel arrangement, of segmented necrotic regions on gadolinium-enhanced T1 weighted (T1gd) MRI have previously been shown to distinguish both overall survival (OS) and progression free survival (PFS) in GBM (n = 95). In our larger patient cohort (n = 389), we sought to validate or refute previously published results connecting morphological metrics and patient survival. We identified pretreatment necrotic regions of our retrospective first-diagnosis GBM patient cohort using segmented T1gd MRI enhancing regions. We calculated lacunarity and fractal dimension across all T1gd MRI slices with enhancing tumor, and used the median lacunarity and fractal dimension values for our analysis. We find that a lacunarity threshold can significantly distinguish OS (14 months vs 19 months median, log-rank p = 0.015, n = 389) and a fractal dimension threshold can significantly distinguish PFS (8 months vs 11 months median, log-rank p = 0.015, n = 123). We believe that morphological metrics such as lacunarity and fractal dimension could play a role in standard-of-care prognostic considerations at tumor presentation. This link between morphological and survival metrics could be driven by underlying biological phenomena or microenvironmental factors that should be further explored.

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology “model-informed drug development,” has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

Seizures are common presenting symptoms of primary brain tumors. Mechanisms of epileptogenesis are still unknown and are believed to be multifactorial. Previous studies have indicated correlation of seizure with tumor location. Recent investigations of our group have shown image-based parameters have sex-specific implications for patient outcome. In this retrospective study, we examined the association of tumor location with the probability and risk of seizure in male and female glioma patients.

Although glioblastoma is a fatal primary brain cancer with a short median survival of 15 months, a small number of patients survive more than 5 years after diagnosis; they are known as extreme survivors (ES). Due to their rarity, very little is known about what differentiates these outliers from other glioblastoma patients. For the purpose of identifying unknown drivers of extreme survivorship in glioblastoma, we developed the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of glioblastoma). This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histological parameters. Leveraging our combined resources, the goals of the ENDURES consortium are two-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with glioblastoma and (2) to leverage the ENDURES repository for new insights on tumor behavior and novel targets for prolonging survival for all glioblastoma patients. In this article, we review the available literature and discuss what is already known about ES. We then describe the creation of our consortium and some of our preliminary results.FundingThis review was financially supported by a grant from the James S. McDonnell FoundationConflicts of InterestThe authors have declared that no conflicts of interest exist.AuthorshipConceptualized consortium: LW, RG, KME, PC, and KRS. Built consortium: SKJ, PK, NR, JS, KME, PC, and KRS. Wrote the manuscript: SKJ, PW, SCM, PK, AP, and KME. Reviewed and edited the manuscript: LFGC, MMM, AHD, PRJ, and LSH. Contributed to writing, provided feedback, and approved of final manuscript: All authors.Link to website for ENDUREShttp://mathematicalneurooncology.org/?page_id=2125

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer., Graphical Abstract ‘Two Beasts’ (2017) Artist: Ben Day Todd. Acrylic, gouache on canvas ’Two Beasts’ is an exploration of form and colour. By adding and subtracting paint, pushing and pulling colour, new information is found and previously unknown dialogues are recorded.

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens., Author summary Glioblastoma, the most common primary brain tumor, is an aggressive and difficult to treat cancer. A key reason is that the tumors can be very heterogeneous, consisting of many different mutants driving distinct cell behaviors. We believe that treatments for this disease could be significantly improved by understanding and quantifying the functional impact of heterogeneity within the tumor. From a clinical standpoint, the larger tissue-scale dynamics, like growth rate, can be informed from serial MRI imaging, while the cell-scale heterogeneity, can be informed by analysis of biopsies. In this work, we combined information from both scales using a mathematical framework and multiscale data from an animal model of glioblastoma. We found that a wide range of potential tumor compositions matched imaging data alone, as a result the model predicts a wide variety of responses to treatment. Using both imaging and cell-scale data narrowed the range of possible tumor compositions and better predicted responses to treatment. The mathematical model also predicted that while targeting migration alone did not slow tumor growth (in fact it drove a more proliferative tumor), an anti-proliferative/anti-migratory treatment combination improved treatment response.

Poor clinical trial outcomes for glioblastoma (GBM) can be attributed to multiple possible causes. GBM is heterogeneous, such that there is a chance of treatment–resistant cells coming to predominate the tumor, and due to the blood brain barrier (BBB) it is also possible that therapy was inadequately delivered to the tumor. Mathematically modeling the dynamics of therapeutic response in patient–derived xenografts (PDX) and fitting the mathematical model to bioluminescence imaging flux data, we may be able to assess the degree to which both drug resistance and drug penetrance are driving varied responses to these therapies.

Purpose Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype—growth before treatment and during each treatment to recurrence —has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. Patients and Methods From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. Results There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). Conclusion Dichotomizing the heterogeneity of GBMs into two populations—one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival—suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.

Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors are not MGMT methylated. Using a cohort of 90 newly-diagnosed GBM patients treated according to the Stupp protocol, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, MGMT methylated tumors were more diffusely invasive than unmethylated tumors, suggesting that the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors. Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests that patients with these tumors may benefit from additional cycles of adjuvant TMZ, even for those without MGMT methylation.

Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Glioblastoma (GBM) is the one of the most aggressive and common primary brain malignancies, with a poor median overall survival of less than 15 months. While the immune system is activated and brain-resident microglia and blood-derived macrophages combat the tumor, the tumor can convert some microglia and macrophages to instead exhibit an immune-suppressive phenotype. These co-opted immune cells are thereby termed ‘glioma-associated microglia and macrophages’ (GAMMs), as they allow for continued tumor growth. However, limited clinical data has been collected to understand this phenomenon. As a result, we have collected spatially-distributed image-localized biopsies from a cohort of patients and performed RNA sequencing on each sample. Correlations between normalized RNA counts of genetic markers for macrophages (i.e., CD68, CD163), tumor populations (i.e., SOX2, OLIG2), and key cell functions (i.e., KI67, CASP3) were analyzed. To further investigate the temporal effects that GAMMs have on GBM growth, we proposed the Proliferation-Invasion-Macrophage (PIM) model. This system of partial differential equations incorporates the proliferative and invasive behavior of GBM, as well as populations for both ‘healthy’ and ‘glioma-associated’ macrophages. By exploring the parameter space, we classified the various dynamics of tumor progression and how they relate to the immune response. With further insights of the interactions between GBM and macrophage populations, we can begin to parameterize the model on a patient-specific basis and provide insights to personalized immunotherapies and other novel immune-targeted treatments.

INTRODUCTION: Brain tumors are difficult to segment in MRI scans. Consequently, we have developed a new system for completely automatic brain tumor segmentation by combining a state-of-the-art 3D deep convolutional neural network (CNN) with a large collection of curated segmentations of brain tumors. METHODS: Our brain tumor database holds 74,722 MRI series from 2,742 unique patients. Over the last 15 years our image analysis team has segmented brain tumors in 35,710 of these series. This preliminary experiment identified 741 pre-treatment studies that included a T1GD and FLAIR scan, and at least one adjudicated brain tumor segmentation. These studies were randomly assigned into 600 training, 41 validation, and 100 test cases. CNN training was performed in two stages: 1) 50 epochs on minimally modified MRI volumes; and, 2) 24 epochs to tune the CNN on skull-stripped volumes. Whole-tumor Dice coefficients (1=perfect overlap, 0=no overlap) were calculated by comparing CNN segmentations against adjudicated segmentations from trained measurers. Training was performed in-the-cloud using an Amazon Machine Instance equipped with an NVidia Tesla V100 GPU, 8 Intel Xeon processors, and 64 GB of RAM. RESULTS: Training required 74 hours. Afterwards, our network required 800 seconds to segment 100 studies in the test set (8 seconds/study). The mean whole-tumor Dice coefficient on the test studies was 0.885. DISCUSSION: The best result on the highly cited 2017 BraTS brain tumor segmentation challenge was a whole-tumor Dice of 0.886, achieved by an ensemble of 7 CNNs. BraTS included 274 studies, each with T1GD, FLAIR, T1 and T2 contrasts. The performance of our single CNN may be due to our comparatively large training set. Our goal is to train our CNN on all series in our database. This may provide useful tools to monitor each patients journey, from diagnosis through treatment.

Clinical neuro-oncology relies on the hyperintensity of gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) in tumor regions to confirm that the blood-brain barrier (BBB) is locally compromised. While the extent of Gd hyperintensity may indicate that systemically administered drug is being distributed to the tumor regions, little is known about how a drug is distributed and how it may relate to the Gd hyperintensity. Additionally, glioblastomas (GBMs) are diffusely invading neoplasms with a significant fraction of the overall tumor cells spread peripheral to the Gd abnormality, which raises uncertainty as to how or if the rest of the diffuse tumor is affected by drug. Given the gap in understanding drug delivery to the brain, we propose a quantitative approach to model drug delivery in GBM based on MRI and matrix-assisted laser desorption/ionization mass spectroscopy imaging (MALDI). T2-weighted (T2) and T1-weighted with Gd contrast (T1Gd) MRI images were acquired for an animal with the GBM12 orthotopic GBM patient derived xenograft (PDX) line dosed with 100mg/kg erlotinib. A T1Gd region of interest (ROI) captured the Gd-associated hyperintensity. MALDI was performed and aligned with MRI images. A Drug ROI was created to represent the increased intensity of erlotinib on MALDI images. Since the Drug ROI encompassed the T1Gd ROI, we subtracted the two ROIs to create a ‘Drug No T1Gd’ ROI, which represented the drug region beyond the edge of the T1Gd ROI. A ‘Brain ROI’ was created to represent the region of the brain outside of the drug’s spread. The MALDI intensities for the three ROIs were all significantly different (p

The hallmark of glioblastoma (GBM) is poor survivorship. However, a small subset (5%) of patients live greater than 5 years (extreme survivors (ES)). The pathological and tumor determinants of ES are unknown. Giant cell (GC) morphology occurs in < 1% of all GBM, is typically IDH1 wildtype, shows a high frequency of p53 mutations, and is reportedly associated with a somewhat better survival than other IDH1-wildtype GBM. However, the clear association between ES and GC GBM has not be established. We aimed to describe the characteristics of ES tumors that presented with GC GBM features and examine sex differences. In our retrospective multi-institutional database, we identified 90 ES patients with GBM. We reviewed neuropathological reports for the diagnosis of GC GBM or pathology description that included the descriptor(s) of GC: monstrocellular or giant cell or bizarre multinucleated and evaluated phenotypical features in order to describe ES with GC GBM. Values are presented as means. Sixteen (17.8%) ES patients were characterized as GC GBM (males (n=9, 56.25%), females (n=7, 43.75%)). Males were significantly younger than females (39.67 vs 56.29, p=0.018). Females presented with significantly smaller tumors than males (F: 9.49 mm, M: 21.14 mm, p=0.0008). Males less often presented with seizures (33.33%) compared to females (42.86%, p=0.7686). Calculations of invasion/proliferation ratios from our mathematical model revealed no statistically significant differences between females and males. Overall survival between males and females was not statistically significant. Across the board, GC GBM occurred most often in frontal lobes (All: 56% (n=9), 66.67% of males (n=6), and 42.86% of females (n=3)). Extreme survivors of GBM exhibit giant cell features at greater than five-fold incidence than the general GBM population. Sex differences warrant significant attention in future explorations of pathological and tumor characteristics in GC GBM.

PURPOSE: Seizures are frequent symptoms of gliomas. Predicting which patients are more likely to seize and in turn require anti-epileptic management has been a challenge. Correctly identifying these patients could help optimize care and minimize side effects. To provide guidance for clinicians, we used machine learning techniques to predict seizure presentation in this population. METHODS: We used volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log()), invasiveness (log(D)) and their relative ratio (log(/D)). We compared the performance of 5 machine learning models in predicting seizure status, using Artificial Neural Network, Naive Bayes (NB), Linear Discriminant Analysis (LDA), Random Forest, and Support Vector Machine. Correlations between probability of seizure presentation (p(SP)) and continuous variables were also analyzed. RESULTS: Our cohort consisted of 59 seizure-presenting and 77 non-seizure-presenting patients. All models consistently demonstrated significant correlations (p < 0.05) between (p(SP)) and the following variables: T1Gd radius (-0.781 to -0.674), T2-FLAIR (-0.674 to -0.611), and log(/D) (0.169 to 0.294). Age was significant (p < 0.05) in 4 of the 5 models (-0.211 to -0.175). Mean performance measures for the models (and the best performer) were: 0.726 for Area under the ROC curve (0.75 with NB), 0.6202 for sensitivity (0.661 with NB), 0.74 for specificity (0.766 with LDA). The 5 features ranked as most important were: T1Gd, T2-FLAIR, log(/D), age, log(). CONCLUSIONS: We found an association in seizure-presenting patients with smaller, more proliferative tumors and younger age. Machine learning predictive modeling can potentially be informative in the clinical arena. Further validation studies, to determine the degree of data overfitting, model versatility, as well as performance on test data, are warranted.

INTRODUCTION: While a growing body of research on the intrinsic biological differences between male and female tumors and their environments continues to evolve, few deep learning techniques have been trained with a sex-specific focus. To investigate the influence of sex differences on automated tumor segmentation, a set of Male and Female 3D deep convolutional neural networks were trained and evaluated (MaleDNN and FemaleDNN). METHODS: A balanced data set was obtained from our brain tumor database of 518 cases with known sex, pretreatment T1GD and FLAIR MRI, and at least one brain tumor segmentation. Cases were split by sex to create training cohorts of 200 male and female cases to train the MaleDNN and FemaleDNN. A set of 59 unseen test cases for each sex were reserved for evaluation. Both networks were used to segment tumor volumes from male and female test cases. Whole-tumor Dice coefficients (1=perfect overlap, 0=no overlap) were calculated by comparing network segmentations against segmentations from trained measurers. RESULTS: The MaleDNN had higher overall performance on all cases (Dice male=0.8416; female=0.7800) compared to the FemaleDNN (Dice male=0.8269, female=0.7639). The difference in performance between networks was significant for male tumors (p=0.0466), but not female tumors (p=0.1872). Both networks performed better on male tumors. Average dice scores were significantly lower for the MaleDNN (Dice decrease=0.0616, p=0.0273) and FemaleDNN (Dice decrease=0.629, p=0.0422) when evaluating female tumors. DISCUSSION: It was anticipated that the MaleDNN and FemaleDNN would have highest performance on test cases from the same sex. However, the FemaleDNN performed better on males than females and the MaleDNN had comparable performance for female tumors. The significant differences between these sex-specific deep neural networks indicate that male and female gliomas differ on imaging and that female tumors are more difficult to segment at initial presentation.

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.

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.

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.

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.

Sex differences in the incidence and outcome of human disease are broadly recognized but in most cases not adequately understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence, and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level, or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that temozolomide chemotherapy is more effective in female compared to male GBM patients. We then applied a novel computational algorithm to linked GBM transcriptome and outcome data, and identified novel sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling were identified as the critical determinants of survival for male and female patients, respectively. The clinical utility of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses, and that improved outcome for all patients might be accomplished via tailoring treatment to sex differences in molecular mechanisms.One Sentence SummaryMale and female glioblastoma are biologically distinct and maximal chances for cure may require sex-specific approaches to treatment.

Glioblastomas (GBMs) are notoriously heterogeneous tumors, both between and within tumors. Non-invasive magnetic resonance images (MRIs) provide some information about tumor characteristics, but understanding of the underlying tumor composition remains incomplete. We propose combining a machine-learning (ML) model of tumor cell density with a model of MRI physics that takes into account signal contributions from the intracellular (ICS) and extracellular (ECS) space to predict the volumetric proportions of tumor cells, non-tumor cells, and extracellular space/edema. T2-weighted and T1Gd MRIs of 18 GBM patients were acquired. In total, 82 image-localized biopsies were collected. A neuropathologist scored the biopsies for the percentage of tumor cells. Biopsies were analyzed for TP53 amplification using array CGH. Machine learning utilizing MRIs was performed to predict the percentage of tumor cells in each voxel. A multi-exponential model of the MRI signal equation was utilized to estimate the ECS and ICS in each voxel. The predicted ICS was then modulated by the ML model to estimate the proportion of tumor and non-tumor cells. Further, indices representing relative edema within the tumor (RAI) and relative amount of tumor compared to all cells (RTI) were calculated. Pathology scores were negatively correlated with the predicted proportion of non-tumor cells (p

In glioblastoma (GBM), contrast enhanced (CE)-MRI delineates bulk tumor with contrast-enhancement but poorly characterizes invasive tumor in the nonenhancing T2W abnormality. There is extensive literature in both machine-learning (ML) and mechanistic mathematical oncology seeking to accurately predict diffuse tumor invasion from multi-parametric MRI. ML offers strengths of a data-driven iterative approach, while mechanistic (proliferation-invasion, PI) modeling incorporates spatial relationships with expected drop-offs of tumor cell density from central regions of MRI enhancement. In this study, we build and cross-validate a first-of-its-kind hybrid (ML-PI) model. We collected 82 image-guided biopsies from 18 primary GBM patients throughout CE T1W and nonenhancing T2W regions. For each biopsy, we obtained neuropathologist estimates of tumor cell density and spatially matched MRI (CE T1W, T2W) from which we extracted texture features. PI maps of tumor invasion were generated from MRI-based patient-specific estimates of the net rates of invasion and proliferation. Then, the PI maps were incorporated with a ML model that uses texture features to predict cell density to minimize the prediction error on biopsy samples (ML strength) while making sure tumor-wide prediction (biopsied and unbiopsied regions) conformed with glioblastoma biology (PI strength). We optimized this hybrid ML-PI model using leave-one-out-cross-validation, and compared its performance with PI and ML alone. We used Pearson correlation (r) between cross-validated predicted tumor cell density and true tumor cell density. We focused on prediction within the nonenhancing zone (n=32) because accurate estimation of invasive tumor cell density in this zone has important clinical value for radiation and surgical planning. PI-ML showed significantly stronger correlation (r=0.76) compared to PI (r=0.44) and ML (r=0.04) models alone (p value