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Glioblastoma multiforme (GBM) are aggressive and uniformly fatal primary brain tumors characterized by their diffuse invasion of the normal-appearing parenchyma peripheral to the clinical imaging abnormality. Hypoxia, a hallmark of aggressive tumor behavior often noted in GBMs, has been associated with resistance to therapy, poorer survival, and more malignant tumor phenotypes. Based on the existence of a set of novel imaging techniques and modeling tools, our objective was to assess a hypothesized quantitative link between tumor growth kinetics [assessed via mathematical models and routine magnetic resonance imaging (MRI)] and the hypoxic burden of the tumor [assessed via positron emission tomography (PET) imaging]. Our biomathematical model for glioma kinetics describes the spatial and temporal evolution of a glioma in terms of concentration of malignant tumor cells. This model has already been proven useful as a novel tool to dynamically quantify the net rates of proliferation (ρ) and invasion (D) of the glioma cells in individual patients. Estimates of these kinetic rates can be calculated from routinely available pretreatment MRI in vivo. Eleven adults with GBM were imaged preoperatively with 18F-fluoromisonidazole (FMISO)–PET and serial gadolinium-enhanced T1- and T2-weighted MRIs to allow the estimation of patient-specific net rates of proliferation (ρ) and invasion (D). Hypoxic volumes were quantified from each FMISO-PET scan following standard techniques. To control for tumor size variability, two measures of hypoxic burden were considered: relative hypoxia (RH), defined as the ratio of the hypoxic volume to the T2-defined tumor volume, and the mean intensity on FMISO-PET scaled to the blood activity of the tracer (mean T/B). Pearson correlations between RH and the net rate of cell proliferation (ρ) reached significance (P < 0.04). Moreover, highly significant positive correlations were found between biological aggressiveness ratio (ρ/D) and both RH (P < 0.00003) and the mean T/B (P < 0.0007). [Cancer Res 2009;69(10):4502–9]Major FindingsOverall, biological aggressiveness assessed by serial MRI is linked with hypoxic burden assessed on FMISO-PET using a novel biomathematical model for glioma growth and invasion. This study suggests that patient-specific modeling of growth kinetics can provide novel and valuable insight into the quantitative connections between disparate information provided by multimodality imaging.

Gliomas are uniformly fatal forms of primary brain neoplasms that vary from low- to high-grade (glioblastoma). Whereas low-grade gliomas are weakly angiogenic, glioblastomas are among the most angiogenic tumors. Thus, interactions between glioma cells and their tissue microenvironment may play an important role in aggressive tumor formation and progression. To quantitatively explore how tumor cells interact with their tissue microenvironment, we incorporated the interactions of normoxic glioma cells, hypoxic glioma cells, vascular endothelial cells, diffusible angiogenic factors, and necrosis formation into a first-generation, biologically based mathematical model for glioma growth and invasion. Model simulations quantitatively described the spectrum of in vivo dynamics of gliomas visualized with medical imaging. Furthermore, we investigated how proliferation and dispersal of glioma cells combine to induce increasing degrees of cellularity, mitoses, hypoxia-induced neoangiogenesis and necrosis, features that characterize increasing degrees of “malignancy,” and we found that changes in the net rates of proliferation (ρ) and invasion (D) are not always necessary for malignant progression. Thus, although other factors, including the accumulation of genetic mutations, can change cellular phenotype (e.g., proliferation and invasion rates), this study suggests that these are not required for malignant progression. Simulated results are placed in the context of the current clinical World Health Organization grading scheme for studying specific patient examples. This study suggests that through the application of the proposed model for tumor–microenvironment interactions, predictable patterns of dynamic changes in glioma histology distinct from changes in cellular phenotype (e.g., proliferation and invasion rates) may be identified, thus providing a powerful clinical tool. Cancer Res; 71(24); 7366–75. ©2011 AACR.

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Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness. [Cancer Res 2009;69(23):9133–40]

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Supplementary Table 1 from Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model

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Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here, we show in 63 newly diagnosed patients with glioblastoma that Days Gained scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Because Days Gained scores can be easily computed with routinely available clinical imaging devices, this model offers immediate potential to be used in ongoing prospective studies. Cancer Res; 73(10); 2976–86. ©2013 AACR.

Intratumoral heterogeneity presents a major challenge to diagnosis and treatment of glioblastoma (GBM). Such heterogeneity is further exacerbated upon the recurrence of GBM, where treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse MRI with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging unique image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically informed multi-task framework combining Bayesian neural networks and semi-supervised adversarial autoencoders, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet provides insight into how to integrate implicit and hierarchical domain knowledge, which is difficult to incorporate into ML models through existing methods. The proposed architecture further addresses challenges in exploiting latent feature structures from limited labeled image-localized biopsy samples, which lead to improvements in prediction accuracy. BioNet performs significantly better than existing methods on cross-validation and blind test datasets, shows generalizability that surpasses other models, and is adaptable to different types of data or tasks. Prediction maps of gene modules from BioNet provide accurate predictions of intratumoral heterogeneity, which can improve surgical planning and localization of diagnostic biopsies, as well as inform neuro-oncological treatment assessment for each patient. These results also highlight the emerging role of ML in precision medicine.Significance StatementQuantitative assessments of intratumoral heterogeneity are limited by sparse biopsy sampling but is crucial for diagnosis, clinical management and treatment of (recurrent) glioblastoma. We propose leveraging a unique cohort of image-localized biopsies and their associated locoregional imaging features to develop a deep learning model, BioNet, that takes as input patient MRIs to predict output maps of the regional distributions of tissue-states. BioNet is able to (1) amplify the signal to noise ratio of the intratumoral genetic and cellular heterogeneity and (2) augment the learning capability of deep learning (DL) models through integrating implicit, hierarchical, but hard to be mathematically formulated domain knowledge. Our method performs significantly better than existing methods and is able to be adapted to related diseases.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our ‘Image-Based Mapping of Brain Tumors’ study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample locations are tracked using neuronavigation and genetic aberrations are later quantified through whole-exome and RNA sequencing. The collected specimens from this NCI-funded research study will be primarily used to generate regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status across tumors, within clinically feasible time frames, to identify biopsy and/or treatment targets based on insight from the entire tumor makeup regional histologic and genetic makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma. From October 1, 2017 to October 31, 2022, this study has enrolled 186 patients with 197 surgeries, of which 163 resulted in the successful collection of image-guided biopsy samples. A total of 995 biopsies have been collected of which 962 are image localized, with a mean of 5.90 image-localized samples per surgery.

The most commonly-used omics databases are a compilation of results from primarily male-only and sex-agnostic studies. The pervasive use of these databases critically hinders progress towards fully accounting for the biology of sex differences.

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.

Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board–approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.

Glioblastomas (GBMs) are biologically heterogeneous within and between patients. Many previous attempts to characterize this heterogeneity have classified tumors according to their omics similarities. These discrete classifications have predominantly focused on characterizing malignant cells, neglecting the immune and other cell populations that are known to be present. We leverage a manifold learning algorithm to define a low-dimensional transcriptional continuum along which heterogeneous GBM samples organize. This reveals three polarized states: invasive, immune/inflammatory, and proliferative. The location of each sample along this continuum correlates with the abundance of eighteen malignant, immune, and other cell populations. We connect these cell abundances with magnetic resonance imaging and find that the relationship between contrast enhancement and tumor composition varies with patient sex and treatment status. These findings suggest that GBM transcriptional biology is a predictably constrained continuum that contains a limited spectrum of viable cell cohabitation ecologies. Since the relationships between this ecological continuum and imaging vary with patient sex and tumor treatment status, studies that integrate imaging features with tumor biology should incorporate these variables in their design.

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.

BACKGROUND AND PURPOSE: Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects. MATERIALS AND METHODS: We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%–100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor). RESULTS: Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%–100%) for normalized (r = 0.63, P

Purpose Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom. Methods The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance. Results We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement. Conclusions This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.

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 (O6-methylguanine–DNA methyltransferase) has been associated with sensitivity to TMZ, whereas 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 that drive 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 found 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 patients with GBM with high and low levels of MGMT methylation at diagnosis.

Glioblastoma is the most malignant primary brain tumor with significant heterogeneity and a limited number of effective therapeutic options. Many investigational targeted therapies have failed in clinical trials, but it remains unclear if this results from insensitivity to therapy or poor drug delivery across the blood-brain barrier. Using well-established EGFR-amplified patient-derived xenograft (PDX) cell lines, we investigated this question using an EGFR-directed therapy. With only bioluminescence imaging, we used a mathematical model to quantify the heterogeneous treatment response across the three PDX lines (GBM6, GBM12, GBM39). Our model estimated the primary cause of intracranial treatment response for each of the lines, and these findings were validated with parallel experimental efforts. This mathematical modeling approach can be used as a useful complementary tool that can be widely applied to many more PDX lines. This has the potential to further inform experimental efforts and reduce the cost and time necessary to make experimental conclusions.Author summaryGlioblastoma is a deadly brain cancer that is difficult to treat. New therapies often fail to surpass the current standard of care during clinical trials. This can be attributed to both the vast heterogeneity of the disease and the blood-brain barrier, which may or may not be disrupted in various regions of tumors. Thus, while some cancer cells may develop insensitivity in the presence of a drug due to heterogeneity, other tumor areas are simply not exposed to the drug. Being able to understand to what extent each of these is driving clinical trial results in individuals may be key to advancing novel therapies. To address this challenge, we used mathematical modeling to study the differences between three patient-derived tumors in mice. With our unique approach, we identified the reason for treatment failure in each patient tumor. These results were validated through rigorous and time-consuming experiments, but our mathematical modeling approach allows for a cheaper, quicker, and widely applicable way to come to similar conclusions.

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.

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.

Digital, OBJECTIVE Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH–wild-type glioblastoma (GBM) to further improve patients’ overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH–wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile). METHODS Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 − VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse. RESULTS A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98–0.99; p = 0.02; and HR 0.98, 95% CI 0.96–0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively. CONCLUSIONS The impact of SMR on OS for patients with IDH–wild-type GBM is influenced by the degree of tumor invasiveness. The authors’ results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH–wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors., Ciencias Médicas y de la Salud

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.

Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological “habitats” at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived &gt, 36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct “habitats” based on low- to medium- to high-contrast enhancement and low–high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%, validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, p &lt, 0.03, validation cohort, 16 ± 4.0%, p &lt, 0.007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.

Paracrine PDGF signaling is involved in many processes in the body, both normal and pathological, including embryonic development, angiogenesis, and wound healing as well as liver fibrosis, atherosclerosis, and cancers. We explored this seemingly dual (normal and pathological) role of PDGF mathematically by modeling the release of PDGF in brain tissue and then varying the dynamics of this release. Resulting simulations show that by varying the dynamics of a PDGF source, our model predicts three possible outcomes for PDGF-driven cellular recruitment and lesion growth: (1) localized, short duration of growth, (2) localized, chronic growth, and (3) widespread chronic growth. Further, our model predicts that the type of response is much more sensitive to the duration of PDGF exposure than the maximum level of that exposure. This suggests that extended duration of paracrine PDGF signal during otherwise normal processes could potentially lead to lesions having a phenotype consistent with pathologic conditions.

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.