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Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing literature has been developed demonstrating the clinical relevance of a biomathematical model, the Proliferation-Invasion (PI) model, of glioblastoma growth. Of interest here is the development of a treatment response metric, Days Gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium enhanced (T1Gd) and T2-weighted magnetic resonance images (MRIs). This metric was shown to be prognostic of time to progression. Further, it was shown to be more prognostic of outcome than standard response metrics. While promising, the original paper did not account for uncertainty in the calculation of the DG metric leaving the robustness of this cutoff in question. We harness the Bayesian framework to consider the impact of two sources of uncertainty: 1) image acquisition and 2) interobserver error in image segmentation. We first utilize synthetic data to characterize what non-error variants are influencing the final uncertainty in the DG metric. We then consider the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival. Our results indicate that the key clinical variants are the time between pre-treatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression and was over 80% reliable. While further validation must be done, this work represents a key step in ascertaining the clinical utility of this metric.

Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT has been associated with sensitivity to TMZ, while increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors. However, the evolutionary processes driving this shift, and more generally the emergence and growth of TMZ-resistant tumor subpopulations, are still poorly understood. Here we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM (surgery, chemotherapy and radiation). We first find that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for GBM patients with high and low levels of MGMT methylation at diagnosis.

We show the application of a minimally based, patient-specific mathematical model in the evaluation of glioblastoma response to therapy. Days Gained uses computational models of glioblastoma growth dynamics derived from clinically acquired magnetic resonance imaging (MRI) to compare the post-treatment tumor lesion to the expected untreated tumor lesion at the same time point. It accounts for the inter-patient variability in growth dynamics and response to therapy. This allows for the accurate assessment of therapeutic response and provides insight into overall survival as it relates to treatment response.

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.

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor associated with a poor median survival of 15–18 months, yet there is wide heterogeneity across and within patients. This heterogeneity has been the source of significant clinical challenges facing patients with GBM and has hampered the drive toward more precision or personalized medicine approaches to treating these challenging tumors. Over the last two decades, the field of Mathematical Neuro-oncology has grown out of desire to use (often patient-specific) mathematical modeling to better treat GBMs. Here, we will focus on a series of clinically relevant results using patient-specific mathematical modeling. The core model at the center of these results incorporates two hallmark features of GBM, proliferation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\rho )$$\end{document}(ρ) and invasion (D), as key parameters. Based on routinely obtained magnetic resonance images, each patient’s tumor can be characterized using these two parameters. The Proliferation-Invasion (PI) model uses \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho $$\end{document}ρ and D to create patient-specific growth predictions. The PI model, its predictions, and parameters have been used in a number of ways to derive biological insight. Beyond predicting growth, the PI model has been utilized to identify patients who benefit from different surgery strategies, to prognosticate response to radiation therapy, to develop a treatment response metric, and to connect clinical imaging features and genetic information. Demonstration of the PI model’s clinical relevance supports the growing role for it and other mathematical models in routine clinical practice.

Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements. Methods: The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated. Results: Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data. Conclusion: Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

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

The current guideline to assess radiological changes in response to immunotherapy, iRANO, faces critical challenges in the determination of progressive disease during the first 6 months from the start of therapy. In patients with recurrent glioblastoma (GBM), immunotherapy induces an inflammatory response that mimics progressive disease, which can lead to premature discontinuation of potentially effective therapy. We investigated the dynamics of tumor growth within the first month of starting immunotherapy with the goal of identifying early indicators that could suggest clinical response. We performed a retrospective review of 8 patients from a Phase 1 Trial of CAR T-Cell therapy that had one MRI scan prior to treatment, during treatment and following treatment. Spherically equivalent radii were extracted from tumor volumes segmented on T1Gd and FLAIR MRIs to quantify the dynamics of tumor growth. Kaplan Meier and Cox Proportional Hazard analysis were performed to the role of tumor dynamics in predicting patient survival. Initial tumor growth velocity during CAR T-cell therapy of >50 mm/year (P=0.0067) was prognostic for overall survival from the time of treatment. An increase in radial T1Gd tumor size of >25% (P=0.046) after receiving two CAR T-Cell cycles also discriminated patient survival from the start of treatment. In addition to the above noted optimal cutoffs, initial T1Gd tumor growth velocity was significant as a continuous variable in cox proportional hazard (HR=0.976, P=0.0404). This significance was retained when controlling for age and sex (HR=0.961, P=0.0429). In this case, more aggressive imageable growth was beneficial to the patient. This increased tumor growth rate may be suggestive of a robust immunological response early in therapy that may be important to anticipate in the clinical management of GBM patients. Increased initial tumor growth velocity during treatments shows promise in identifying patients experiencing clinically relevant response that is predictive of patient survival.

Perineural spread (PNS) of pelvic cancer along the lumbosacral plexus is an emerging explanation for neoplastic lumbosacral plexopathy (nLSP) and an underestimated source of patient morbidity and mortality. Despite the increased incidence of PNS, these patients are often times a clinical conundrum—to diagnose and to treat. Building on previous results in modeling glioblastoma multiforme (GBM), we present a mathematical model for predicting the course and extent of the PNS of recurrent tumors. We created three-dimensional models of perineurally spreading tumor along the lumbosacral plexus from consecutive magnetic resonance imaging scans of two patients (one each with prostate cancer and cervical cancer). We adapted and applied a previously reported mathematical model of GBM to progression of tumor growth along the nerves on an anatomical model obtained from a healthy subject. We were able to successfully model and visualize perineurally spreading pelvic cancer in two patients; average growth rates were 60.7 mm/year for subject 1 and 129 mm/year for subject 2. The model correlated well with extent of PNS on MRI scans at given time points. This is the first attempt to model perineural tumor spread and we believe that it provides a glimpse into the future of disease progression monitoring. Every tumor and every patient are different, and the possibility to report treatment response using a unified scale—as “days gained”—will be a necessity in the era of individualized medicine. We hope our work will serve as a springboard for future connections between mathematics and medicine.

Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

Microglia are a major cellular component of gliomas, and abundant in the centre of the tumour and at the infiltrative margins. While glioma is a notoriously infiltrative disease, the dynamics of microglia and glioma migratory patterns have not been well characterized. To investigate the migratory behaviour of microglia and glioma cells at the infiltrative edge, we performed two-colour time-lapse fluorescence microscopy of brain slices generated from a platelet-derived growth factor-B (PDGFB)-driven rat model of glioma, in which glioma cells and microglia were each labelled with one of two different fluorescent markers. We used mathematical techniques to analyse glioma cells and microglia motility with both single cell tracking and particle image velocimetry (PIV). Our results show microglia motility is strongly correlated with the presence of glioma, while the correlation of the speeds of glioma cells and microglia was variable and weak. Additionally, we showed that microglia and glioma cells exhibit different types of diffusive migratory behaviour. Microglia movement fit a simple random walk, while glioma cell movement fits a super diffusion pattern. These results show that glioma cells stimulate microglia motility at the infiltrative margins, creating a correlation between the spatial distribution of glioma cells and the pattern of microglia motility.

Objective Although a significant amount of experience has accumulated for awake procedures for brain tumor, epilepsy, and carotid surgery, its utility for intracranial neurovascular indications remains largely undefined. Awake surgery for select neurovascular cases offers the advantage of precise brain mapping and robust neurologic monitoring during surgery for lesions in eloquent areas, avoidance of potential hemodynamic instability, and possible faster recovery. It also opens the window for perilesional epileptogenic tissue resection with potentially less risk for iatrogenic injury. Methods Institutional review board approval was obtained for a retrospective review of awake surgeries for intracranial neurovascular indications over the past 36 months from a prospectively maintained quality database. We reviewed patients' clinical indications, clinical and imaging parameters, and postoperative outcomes. Results Eight consecutive patients underwent 9 intracranial neurovascular awake procedures conducted by the senior author. A standardized “sedated–awake–sedated” protocol was used in all 8 patients. For the 2 patients with arteriovenous malformations and the 3 patients with cavernoma, awake brain surface and white matter mapping was performed before and during microsurgical resection. A neurological examination was obtained periodically throughout all 5 procedures. There were no intraoperative or perioperative complications. Hypotension was avoided during the 2 Moyamoya revascularization procedures in the patient with a history of labile blood pressure. Postoperative imaging confirmed complete arteriovenous malformation and cavernoma resections. No new neurologic deficits or new-onset seizures were noted on 3-month follow-up. Conclusions Awake surgery appears to be safe for select patients with intracranial neurovascular pathologies. Potential advantages include greater safety, shorter length of stay, and reduced cost.

Mathematical and computational modeling approaches have been applied to every aspect of tumor growth from mutation acquisition and tumorigenesis to metastasis and treatment response. In thi...

Object Patients with systemic cancer and a single brain metastasis who undergo treatment with resection plus radiotherapy live longer and have a better quality of life than those treated with radiotherapy alone. Historically, whole-brain radiotherapy (WBRT) has been the mainstay of radiation therapy; however, it is associated with significant delayed neurocognitive sequelae. In this study, the authors looked at survival in patients with single and multiple intracranial metastases who had undergone surgery and adjuvant stereotactic radiosurgery (SRS) to the tumor bed and synchronous lesions. Methods The authors retrospectively reviewed the records from an 8-year period at a single institution for consecutive patients with brain metastases treated via complete resection of dominant lesions and adjuvant radiosurgery. The cohort was analyzed for time to local progression, synchronous lesion progression, new intracranial lesion development, systemic progression, and overall survival. The Kaplan-Meier method (stratified by age, sex, tumor histology, and number of intracranial lesions prior to surgery) was used to calculate both progression-free and overall survival. A Cox proportional-hazards regression model was also fitted with the number of intracranial lesions as the predictor and survival as the outcome controlling for disease severity, age, sex, and primary histology. Results The median overall follow-up among the 150-person cohort eligible for analysis was 17 months. Patients had an average age of 46.2 years (range 16–82 years), and 62.7% were female. The mean (± standard deviation) number of intracranial lesions per patient was 2.5 ± 2.3. The mean time between surgery and stereotactic radiosurgery (SRS) was 3.2 ± 4.1 weeks. Primary cancers included lung cancer (43.3%), breast cancer (21.3%), melanoma (10.0%), renal cell carcinoma (6.7%), and colon cancer (6.7%). The average number of isocenters per treated lesion was 7.6 ± 6.6, and the average treatment dose was 17.8 ± 2.8 Gy. One-year survival for patients in this cohort was 52%, and the 1-year local control rate was 77%. The median (±standard error) overall survival was 13.2 ± 1.9 months. There was no difference in survival between patients with a single lesion and those with multiple lesions (p = 0.319) after controlling for age, sex, and histology of primary tumor. Patients with primary breast histology had the greatest overall median survival (22.9 ± 6.2 months); patients with colorectal cancer had the shortest overall median survival (5.3 ± 1.8 months). The most common cause of death in this series was systemic progression (79%). Conclusions These results confirm that 1-year survival for patients with multiple intracranial metastases treated with resection followed by SRS to both the tumor bed and synchronous lesions is similar to established outcomes for patients with a single intracranial metastasis.

Temozolomide (TMZ) is one of the most potent chemotherapy agents for the treatment of glioblastoma. Unfortunately, almost half of glioblastoma tumors are TMZ resistant due to overexpression of methylguanine methyltransferase (MGMT(hi)). Coadministration of O6-benzylguanine (O6BG) can restore TMZ sensitivity, but causes off-target myelosuppression. Here, we conducted a prospective clinical trial to test whether gene therapy to confer O6BG resistance in hematopoietic stem cells (HSCs) improves chemotherapy tolerance and outcome.We enrolled 7 newly diagnosed glioblastoma patients with MGMT(hi) tumors. Patients received autologous gene-modified HSCs following single-agent carmustine administration. After hematopoietic recovery, patients underwent O6BG/TMZ chemotherapy in 28-day cycles. Serial blood samples and tumor images were collected throughout the study. Chemotherapy tolerance was determined by the observed myelosuppression and recovery following each cycle. Patient-specific biomathematical modeling of tumor growth was performed. Progression-free survival (PFS) and overall survival (OS) were also evaluated.Gene therapy permitted a significant increase in the mean number of tolerated O6BG/TMZ cycles (4.4 cycles per patient, P0.05) compared with historical controls without gene therapy (n = 7 patients, 1.7 cycles per patient). One patient tolerated an unprecedented 9 cycles and demonstrated long-term PFS without additional therapy. Overall, we observed a median PFS of 9 (range 3.5-57+) months and OS of 20 (range 13-57+) months. Furthermore, biomathematical modeling revealed markedly delayed tumor growth at lower cumulative TMZ doses in study patients compared with patients that received standard TMZ regimens without O6BG.These data support further development of chemoprotective gene therapy in combination with O6BG and TMZ for the treatment of glioblastoma and potentially other tumors with overexpression of MGMT.Clinicaltrials.gov NCT00669669.R01CA114218, R01AI080326, R01HL098489, P30DK056465, K01DK076973, R01HL074162, R01CA164371, R01NS060752, U54CA143970.

See the editorial by Lathia, on pages 763–764. Since the 2008 discovery of a mutation in the isocitrate dehydrogenase 1 (IDH1) gene in a subset of glioma patients, more than 2 dozen manuscripts have been published about the role of this IDH1 mutation in the natural history of glioma.1–6 This unique mutation in IDH1, which changes arginine at position 132 to histidine, is disproportionately represented in lower-grade gliomas; it is present in >75% of grade II and III gliomas but only ∼10% of grade IV glioblastomas (GBMs).1,7 Furthermore, this mutation in IDH1 is more prevalent in younger patients. Glioma patients with the mutation show significantly longer survival times than those with the wild-type copy of the gene, having a median survival of 3.8 years versus 1.1 years.1,8 Additionally, secondary GBMs are predominantly mutant in IDH1 (83%), while very few primary GBMs (5%) harbor the mutation.7 Since IDH1mut GBMs (and perhaps more contrast-enhancing gliomas) have a significantly better prognosis than IDH1wt GBMs, it is clinically important to have pretreatment (presurgical) predictors of IDH1mut status. Our laboratory has developed a patient-specific mathematical model of glioma growth that is based on diagnostic and pretreatment MRI scans obtained in the course of normal clinical treatment.9–15 By combining our model formalism with tumor volume measures extracted from these routinely obtained pretreatment MRIs, we are able to estimate patient-specific parameters that quantify the net proliferation rate (ρ) of the glioma cells and their net dispersal or diffusion rate (D). These parameters can be used to characterize the differential role of proliferation versus diffusion in driving the overall tumor growth pattern seen in each patient. The variation in the parameters across patients is consistent with the wide heterogeneity in imaging results and invasive capacity typical of the disease.12 These 2 kinetic parameters can be combined to produce a biological aggressiveness ratio (ρ/D) that quantifies the relative proliferative to invasive nature of each tumor. This measure of biological aggressiveness is predictive of worse prognosis14 and increasing degrees of hypoxia,15 a known feature of tumor aggressiveness. Also, we have previously shown that a high ρ/D (characteristic of more nodular, less diffuse tumors) is more likely to represent a rapidly developing primary GBM that is relatively less invasive, whereas a low ρ/D (characteristic of a more diffuse, less nodular tumor) is associated with a slower developing but more invasive secondary GBM.16 This novel discovery suggests the possibility of predicting primary versus secondary GBM natural histories and, by extension, likely IDH1mutation status based on a single measure quantifiable from routinely obtained MR images alone. Further, a recent game theory-based consideration of the evolutionary role of IDH1 mutation in cellular populations suggested that such a mutation would select for a more invasive overall tumor phenotype.17 Combining these 2 insights suggests the following question: can we predict IDH1 mutation status from image-based analysis of tumor invasion using patient-specific mathematical models of glioma proliferation and invasion kinetics? One of our main hypotheses was that the most diffuse contrast-enhancing gliomas (low ρ/D) would be mutant in IDH1. To test this hypothesis, we examined the mutational status of IDH1 by mutation-specific immunohistochemistry in a cohort of 172 newly diagnosed, contrast-enhancing glioma patients, 91% of whom were ultimately found to have grade IV tumors. The concept was to distinguish IDH1 mutant tumors (with their associated favorable prognosis) within a cohort of contrast-enhancing gliomas by mapping a favorable molecular feature to patient-specific disease kinetics (ie, net proliferation and invasion rates).

BACKGROUND: Failed trials involving targeted therapies face a daunting task of understanding whether the root cause was inadequate targeting, resistance, or insufficient delivery to the tumor. Previous work with a bio-mathematical model has shown the prognostic value of an imaging based invasion metric, D/ρ (mm(2)/yr), which is linearly correlated with the extent of tumor burden beyond the imaging abnormality. As this extent likely impacts the definition of sufficient drug delivery, we investigated whether this metric is able to predict response to ABT-414, an antibody drug conjugate targeting EGFR. METHODS: Preclinical experiments: After initial screening in vitro and in flank, the efficacy of ABT-414 was evaluated for three patient derived cell lines (PDXs), GBM6, GBM12, and GBM39, implanted orthotopically. All three showed strong response to ABT-414 in the previous experiments. In the orthotopic setting, GBM39 was very sensitive to therapy (> 155 days benefit), GBM12 was moderately sensitive (15–30 days benefit), and GBM6 was resistant to therapy (no benefit). Invasion Index: D/ρ was calculated for each cell line based on the original patients’ pre-treatment T1-weighted with Gd contrast and FLAIR MRIs. We then analyzed whether D/ρ was correlated with the observed response to ABT-414. RESULTS: GBM6 had the highest D/ρ = 4.7 (greatest proportion of tumor cells invaded beyond the imaging abnormality), GBM12 was in the middle with D/ρ = 1.7, and GBM39 had the lowest D/ρ = 0.9. This perfectly (inversely) correlates with the response patterns. GBM6 showed no response, GBM12 had a moderate response, and GBM39 showed the greatest response to ABT-414. CONCLUSION: Our results suggest that the imaging based tumor invasion metric, D/ρ, is inversely correlated with tumor response to ABT-414. This supports our hypothesis that drugs with low BBB permeability will be more beneficial for tumors with little tumor burden beyond the imaging abnormality.

PURPOSE: Despite a growing body of research on the etiology of seizures in the brain tumor population, it is not yet clear why approximately one third of this population presents with seizure. In this retrospective study we seek to assess the impact of tumor location on the risk of seizure among male and female glioma patients, respectively. METHODS: From our multi-institutional database, we selected adult patients with contrast-enhancing gliomas (any grade) and known seizure-presentation status at initial diagnosis. Tumors were segmented on pretreatment T2-FLAIR, T2, and post-gadolinium T1 (T1Gd) MR sequences. We warped cortical and subcortical probabilistic atlases to patient images and estimated tumor burden (%) on structures within each lobe and the deep brain. We calculated risk of seizure for various levels of tumor burden and used the result to create sex-specific risk-maps RESULTS: Our cohort included 128 patients (47 females, 81 males) among whom tumors presented with seizure in 34% of females (n=18) and 58% of males (n=47). Patients with tumors in deep brain structures were identified as at risk for seizure in both males and females. In female patients T2 hyperintensity on 44% of right frontal lobe or 28% of left parietal lobe seem to have an estimated 50%+ risk of seizure. Data indicate that in male patients with left-sided tumors, T2 hyperintensity on 30% or more of frontal, temporal, or parietal lobes results in 50%+ risk of seizure. Male patients with right side tumors, showed a similar level of risk for T2 presence in lateral ventricles and parietal lobe. CONCLUSION: This study reveals that the risk of seizure is specific to the location of glioma, the percentage of tumor burden, and patient sex. These differences could be considered in patient management decisions to more selectively prescribe anticonvulsants and optimize patient care.

INTRODUCTION: Evaluating treatment response in glioblastoma is a difficult task for clinicians, particularly in recurrent disease. Current response metrics focus on changes in imageable tumor burden which can be ambiguously affected by anti-angiogenic therapy. In previous work, we reported the ability of the Days Gained (DG) response metric to discriminate patients receiving bevacizumab into long and short-term survival groups. In this work, we investigate how the DG metric performs for patients who received bevacizumab with a concurrent cytotoxic agent (e.g. CCNU) compared to patients who received bevacizumab alone. METHODS: We identified a set of 38 patients with recurrent glioblastoma who received bevacizumab therapy (21 patients) or bevacizumab with concurrent CCNU (17 patients). Each case had tumor volumes segmented on two dates prior and one day post therapy on T1GD and FLAIR MR imaging. DG scores were calculated using tumor growth characteristics and were used to evaluate discrimination of patient survival and time to progression (TTP) using Kaplan-Meier curves and logistic regression. RESULTS: An optimal threshold of 162 DG (p= 0.0393, log rank) on T1GD imaging was discriminative for TTP in bevacizumab cases. Combination therapy with CCNU showed a similar trend for the correlation between DG and TTP outcomes as for bevacizumab alone. Patients receiving bevacizumab alone had significant DG thresholds for survival following treatment (T1GD: 162 DG, p=0.01857; FLAIR: 216 DG, p=0.0387). Although predictive of TTP, combination therapy was not significantly associated with increased overall survival from time of treatment. DISCUSSION: Days gained was able to discriminate survival and TTP for patients receiving bevacizumab therapy alone. Discriminative power for combination therapy with CCNU demonstrated the same trend for TTP outcomes. CONCLUSION: Growing evidence supports DG as a clinically meaningful metric of treatment response even in the context of anti-angiogenic therapies that are known to ambiguously modulate imaging features on MRI.

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

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

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.

INTRODUCTION Immunotherapy with engineered CAR T cells is a promising new therapy for glioblastoma, for which predictive and prognostic biomarkers are needed to inform effective intervention. Recently, our group analyzed standard-of-care (SOC) MRI images of long-term and short-term glioblastoma survivors and identified six intratumoral “habitats” of which “Habitat 6” was correlated with survival at diagnosis in high-grade glioma. Based on the MRI characteristics of “Habitat 6”, viz. high enhancement and high edema, we hypothesized that it could be a marker of tumor immune infiltrates. We are studying longitudinal changes in tumor “habitat” composition on MRIs of subjects with recurrent high-grade glioma treated with CAR T cells engineered to target IL13Ra2. METHODS MRI scans of the brain were acquired in 6 subjects at 3.0 T at baseline and various times before and after initiation of CAR T cell therapy. FLAIR, T1W and T1W-CE MRI images were registered to T2W images and six intratumoral “habitats” were computed as per our recently published methodology. The six habitats generated at the end of the tumor segmentation process were: “Habitat 1” (low FLAIR, low enhancement), “Habitat 2” (high FLAIR, low enhancement), “Habitat 3” (low FLAIR, medium enhancement), “Habitat 4” (high FLAIR, medium enhancement), “Habitat 5” (low FLAIR, high enhancement), and “Habitat 6” (high FLAIR, high enhancement). RESULTS Analysis of temporal changes in the six “habitats” shows an initial increase in both “Habitat 4” and “Habitat 6” following CAR T cell therapy initiation. Subjects with higher absolute volumes of “Habitat 6” at the baseline (pre-treatment) showed longer overall survival. Overall survival is a function of absolute “Habitat 6” volume at baseline, its direction of change immediately post-therapy, the duration of any increase in “Habitat 6” post-treatment, and the “Habitat 6” to “Habitat 4” ratio. Additional subjects are being evaluated to further understand these preliminary observations.

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

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.

Platelet-derived growth factor (PDGF) drives the formation of gliomas in an experimental animal model, which notably involves the recruitment of large numbers of glial progenitor cells (Assanah 2006). In order to understand the underlying mechanism, particularly what factors influence the degree of recruitment, and how varied amounts of PDGF would affect the gross characteristics and overall appearance of tumors in the brain, we adapted a reaction diffusion model of glioma, which has been used for analyzing clinical data, to model the interactions at play in these experimental models.

In this article, we present an example of how to parameterize a partial differential equation model of glioblastoma growth for individual patients. The parameters allow for a deeper understanding of individual patients which have all been labeled with the same disease based on the tumor growth kinetics.

ETOC: Brain invasion by gliomas makes these tumors particularly malignant. In this paper, we demonstrate that these tumors need myosin II to drive this process and that the need for myosin II cannot be replaced by stimulating the upstream signal transduction cascades that are pathogenic in this disease., Anaplastic gliomas, the most common and malignant of primary brain tumors, frequently contain activating mutations and amplifications in promigratory signal transduction pathways. However, targeting these pathways with individual signal transduction inhibitors does not appreciably reduce tumor invasion, because these pathways are redundant; blockade of any one pathway can be overcome by stimulation of another. This implies that a more effective approach would be to target a component at which these pathways converge. In this study, we have investigated whether the molecular motor myosin II represents such a target by examining glioma invasion in a series of increasingly complex models that are sensitive to platelet-derived growth factor, epidermal growth factor, or both. Our results lead to two conclusions. First, malignant glioma cells are stimulated to invade brain through the activation of multiple signaling cascades not accounted for in simple in vitro assays. Second, even though there is a high degree of redundancy in promigratory signaling cascades in gliomas, blocking tumor invasion by directly targeting myosin II remains effective. Our results thus support our hypothesis that myosin II represents a point of convergence for signal transduction pathways that drive glioma invasion and that its inhibition cannot be overcome by other motility mechanisms.

Although new neurons are produced in the subventricular zone (SVZ) of the adult mammalian brain, fewer functional neurons are produced with increasing age. The age-related decline in neurogenesis has been attributed to a decreased pool of neural progenitor cells (NPCs), an increased rate of cell death, and an inability to undergo neuronal differentiation and develop functional synapses. The time between mitotic events has also been hypothesized to increase with age, but this has not been directly investigated. Studying primary-cultured NPCs from the young adult and aged mouse forebrain, we observe that fewer aged cells are dividing at a given time; however, the mitotic cells in aged cultures divide more frequently than mitotic cells in young cultures during a 48-hour period of live-cell time-lapse imaging. Double-thymidine-analog labeling also demonstrates that fewer aged cells are dividing at a given time, but those that do divide are significantly more likely to re-enter the cell cycle within a day, both in vitro and in vivo. Meanwhile, we observed that cellular survival is impaired in aged cultures. Using our live-cell imaging data, we developed a mathematical model describing cell cycle kinetics to predict the growth curves of cells over time in vitro and the labeling index over time in vivo. Together, these data surprisingly suggest that progenitor cells remaining in the aged SVZ are highly proliferative.

Glioblastoma multiforme (GBM) is a class of primary brain tumours characterized by their ability to rapidly proliferate and diffusely infiltrate surrounding brain tissue. The aggressive growth of GBM leads to the development of regions of low oxygenation (hypoxia), which can be clinically assessed through [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) imaging. Building upon the success of our previous mathematical modelling efforts, we have expanded our model to include the tumour microenvironment, specifically incorporating hypoxia, necrosis and angiogenesis. A pharmacokinetic model for the FMISO-PET tracer is applied at each spatial location throughout the brain and an analytical simulator for the image acquisition and reconstruction methods is applied to the resultant tracer activity map. The combination of our anatomical model with one for FMISO tracer dynamics and PET image reconstruction is able to produce a patient-specific virtual PET image that reproduces the image characteristics of the clinical PET scan as well as shows no statistical difference in the distribution of hypoxia within the tumour. This work establishes proof of principle for a link between anatomical (magnetic resonance image [MRI]) and molecular (PET) imaging on a patient-specific basis as well as address otherwise untenable questions in molecular imaging, such as determining the effect on tracer activity from cellular density. Although further investigation is necessary to establish the predicitve value of this technique, this unique tool provides a better dynamic understanding of the biological connection between anatomical changes seen on MRI and biochemical activity seen on PET of GBM in vivo.

Glioblastoma (GBM) is an aggressive primary brain cancer that has a median overall survival (OS) of 15 months from diagnosis. Determining which pretreatment tumor characteristics are predictive of extreme survival (EXS) (OS > 1825 days) and short-term survival (STS) (OS < 210 days) has significant clinical value. Several biological markers have been discovered that are predictive of EXS or STS, but the impact of patient sex on the predictive value of these markers has been minimally explored. In this investigation, machine learning algorithms and statistical tests were used to assess which pretreatment clinical and MR image-based volumetric and kinetic parameters were significant predictors of overall survival duration for male (n=299, including 30 EXS and 46 STS) and female (n=195, including 17 EXS and 42 STS) GBM patients. When compared to the middle survivor group (210 days < OS < 1825 days), female EXS had smaller tumor volumes and both male and female EXS had lower tumor cell proliferation rates at time of diagnosis. Independent predictors of overall survival included tumor cell diffuse invasion rate among females (hazard ratio [HR] =1.011, p < 0.001), tumor size among males (HR=1.027, p=0.044) and age among both males and females (Females: HR=1.021, p=0.006; Males: HR=1.030, p < 0.001). Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

BACKGROUND: MRI-based modeling can help characterize the intratumoral genetic heterogeneity of Glioblastoma (GBM). Yet, published models to date have neglected the potential impact of sex-differences on the accuracy of MRI-genetic correlations. Specifically, there is growing awareness that female GBM patients can display different genetic/molecular aberrations and phenotypic expression compared to male counterparts. In this exploratory study, we compare MRI signal and key GBM driver alterations across a cohort of male and female GBM patients, using image-guided biopsies and spatially matched multi-parametric MRI. METHODS: We collected 61 image-guided biopsies from 18 primary GBM patients (9/9 male/female). For each biopsy, we analyzed DNA copy number variants (CNV) for 6 core GBM driver genes reported by TCGA: amplifications (++) for EGFR and PDGFRA and deletions (--) for PTEN, CDKN2A, RB1, TP53. We compared regional CNV status with spatially matched MRI texture measurements from co-registered biopsy locations. Advanced MRI features included relative cerebral blood volume (rCBV) on DSC-perfusion, mean diffusivity (MD) and fractional anisotropy (FA) on diffusion tensor imaging. We identified univariate correlations for combined and sex-specific (male, female) subgroups. We also built multivariate predictive decision-tree models for each GBM driver gene and used leave-one-out-cross-validation (LOOCV) to determine area-under-curve (AUC) on ROC analysis to compare accuracies across combined and sex-specific models. RESULTS: We identified multiple univariate correlations between regional CNV status and spatially matched MRI texture features that were specific to either male or female GBM tumors. For instance, EGFR++ specifically correlated with T2W image textures in male biopsies but rCBV textures in female biopsies. In general, sex-specific analyses on decision-tree modeling improved predictive accuracies (AUC) compared to combined (male+female) modeling, particularly for EGFR++ (p

INTRODUCTION: Glioblastoma (GBM) is the most common malignant primary brain tumor in adults with a median survival of 14–16 months. Patient sex plays an important role in GBM as there is a difference in incidence rates and outcome between males and females, which may be attributable to differences in genetic makeup and physiology. OBJECTIVE: Investigate the impact of tumor location and sex differences on survival outcomes based on tumor location, laterality, age, handedness, and extent of resection. METHODS: Patients (129 males and 87 females) who received standard-of-care were included. Analyses were performed using Cox proportional hazard modeling and Kaplan-Meier analysis (log-rank test) to determine which variables impacted patient survival. RESULTS: Overall survival was significantly longer in females in comparison to males (197 days, p = 0.0391). Investigating specific tumor locations, females with a tumor in the left frontal lobe (n = 12) showed a survival advantage compared to females with a right frontal (n = 15) GBM (2853 days, p = 0.0160). Significant differences in median OS were also associated with age. Female patients below the age of fifty showed significantly longer survival (2602 days, n = 84, p

Glioblastoma multiforme (World Health Organization grade IV) is a highly anaplastic, rapidly proliferating, primary brain neoplasm characterized by diffuse invasion of the normal-appearing tissue peripheral to the contrast-enhanced abnormality seen on clinical imaging (1). These tumors respond poorly to the most aggressive treatments. The current standard of care includes surgical resection, followed by radiation therapy and chemotherapy, with a prognosis of survival generally falling in the range of 6–14 mo (2). MRI and PET play important roles in the assessment of patients with gliomas. 18F-fluoromisonidazole PET (18F-FMISO) allows for imaging of hypoxia in vivo (3,4). On the basis of the biologic links between hypoxia and the aggressiveness of disease, including tumor-induced neoangiogenesis, it is instructive to compare 18F-FMISO images of hypoxia with MRI scans to reveal aspects of new vasculature and invasion. Gadolinium-enhanced T1-weighted MRI (T1Gd) provides morphologic imaging of the blood–brain barrier breakdown in regions of angiogenesis usually surrounding central necrosis (5). Central necrosis appears hypointense when imaged by T1Gd because of the lack of viable vessels. In addition, edema associated with infiltrated glioma cells is hyperintense on T2-weighted MRI (T2). Exposure to ionizing radiation increases the permeability of the vasculature and may affect both T1Gd and T2 (6). PET with the radiotracer 18F-FMISO detects regions of hypoxia independent of the tumor anatomy and perfusion (7). 18F-FMISO is a 2-nitroimidazole derivative that undergoes intracellular reduction in cells that are alive, generating a radical anion. Oxygen acts as an electron acceptor for the 18F-FMISO radical anion. In the absence of oxygen, the unstable 18F-FMISO radical anion is further reduced and covalently bound to intracellular macromolecules and cannot exit the hypoxic cells (8). Binding of 18F-FMISO is proportional to the concentration of oxygen, with significant retention occurring at oxygen levels below 3 mm Hg (9). Thus, 18F-FMISO binds to hypoxic tumor tissue but not to the oxygenated brain or oxygenated tumor (10). The Angiogenic Cascade As tumor cells proliferate, the local tissue becomes relatively oxygen-depleted (hypoxic). Tumor cell growth outstrips vascular development so that oxygen delivery is decreased beyond its diffusion distance. Hypoxia promotes neovascularization through a variety of molecular signals that drive tumor survival (11,12). On the molecular level, hypoxia-inducible factor-1—a transcription factor that is constantly produced but survives only in hypoxic cells—binds to a hypoxic response element that activates the transcription of more than 100 genes, promoting cell survival, invasiveness, and aggressiveness, including vascular endothelial growth factor (VEGF) (13,14). VEGF is a dominant cytokine regulating the expression of growth factor receptor genes at the onset of the angiogenic cascade (15). In its role as an angiogenic factor, VEGF serves to recruit and induce proliferation of vascular endothelial cells. In addition to the role of hypoxia in neoangiogenesis, tumor cells show adaptation to hypoxia by decreasing the proliferation rate, remaining longer in the resting phase G0, and escaping to some degree the impact of conventional treatments that principally target cells in the S phase of their cycle. A greater dose of photon irradiation (2–3 times) is required to kill hypoxic tissue relative to normoxic tissue (16), a factor that further implicates hypoxia in primary tumor recurrence (2). This study considers the assessment of disease burden by 18F-FMISO relative to T1Gd and T2. Motivated by the biologic link between hypoxia and tumor-induced neovasculature and tumor aggressiveness, we analyzed detailed spatial and volumetric data of 18F-FMISO uptake relative to MRI-defined regions. This initial analysis provides the basis for exploring the benefit of combining MRI and PET data into a more complete picture of the disease burden for better disease assessment and prognosis.

Purpose: Hypoxia is associated with resistance to radiotherapy and chemotherapy and activates transcription factors that support cell survival and migration. We measured the volume of hypoxic tumor and the maximum level of hypoxia in glioblastoma multiforme before radiotherapy with [18F]fluoromisonidazole positron emission tomography to assess their impact on time to progression (TTP) or survival. Experimental Design: Twenty-two patients were studied before biopsy or between resection and starting radiotherapy. Each had a 20-minute emission scan 2 hours after i.v. injection of 7 mCi of [18F]fluoromisonidazole. Venous blood samples taken during imaging were used to create tissue to blood concentration (T/B) ratios. The volume of tumor with T/B values above 1.2 defined the hypoxic volume (HV). Maximum T/B values (T/Bmax) were determined from the pixel with the highest uptake. Results: Kaplan-Meier plots showed shorter TTP and survival in patients whose tumors contained HVs or tumor T/Bmax ratios greater than the median (P ≤ 0.001). In univariate analyses, greater HV or tumor T/Bmax were associated with shorter TTP or survival (P < 0.002). Multivariate analyses for survival and TTP against the covariates HV (or T/Bmax), magnetic resonance imaging (MRI) T1Gd volume, age, and Karnovsky performance score reached significance only for HV (or T/Bmax; P < 0.03). Conclusions: The volume and intensity of hypoxia in glioblastoma multiforme before radiotherapy are strongly associated with poorer TTP and survival. This type of imaging could be integrated into new treatment strategies to target hypoxia more aggressively in glioblastoma multiforme and could be applied to assess the treatment outcomes.

As with all mathematical modeling, the scope of the question to be explored determines the scope of the most appropriate model. The case is no different for the modeling of primary brain tumors (gliomas), ranging from too simple, not accounting for the major feature of gliomas (extensive invasion), to too complicated, with too many variables and no easy way to translate from culture media in vitro to brain tissue in vivo. We settle on a "just right" approach which utilizes currently available magnetic resonance imaging (MRI) to estimate two defining characteristics, net rates of proliferation (ρ) and diffusion (D). Most importantly, these parameters are predictive of clinical behavior, and can be tailored to individual patients in vivo and in real time. These two rates combine to generate a linear radial growth pattern of the MRI visible portion of each glioma. Further, we introduce a novel method for the calculation of glioma invasion through grey and white matter.

A mathematical model has been developed to describe and quantify the growth and invasion of gliomas (the most common type of primary brain tumors). The model has already been shown to agree well with in vivo imaging studies of gliomas. Here we demonstrate the model's agreement with in vitro experimental data. The analysis provided in this article demonstrates that the model agrees well with published observations of growth and invasion of gliomas cells in culture. These in vitro results support the model's utility in describing the behavior of glioma cells in experimental settings but also lends credence to the applications of the model to the in vivo setting.