Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Mathematical modelling applied to preclinical, clinical, and public health research is critical for our understanding of a multitude of biological principles. Biology is fundamentally heterogeneous, and mathematical modelling must meet the challenge of variability head on to ensure the principles of diversity, equity, and inclusion (DEI) are integrated into quantitative analyses. Here we provide a follow-up perspective on the DEI plenary session held at the 2023 Society for Mathematical Biology Annual Meeting to discuss key issues for the increased integration of DEI in mathematical modelling in biology.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

BACKGROUND AND OBJECTIVE

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.

METHODS

We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.

RESULTS

WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.

CONCLUSIONS

This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Androgen loss weakens anti-tumor immunity and accelerates brain tumor growth

Many cancers, including glioblastoma (GBM), have a male-biased sex difference in incidence and outcome. The underlying reasons for this sex bias are unclear but likely involve differences in tumor cell state and immune response. This effect is further amplified by sex hormones, including androgens, which have been shown to inhibit anti-tumor T cell immunity. Here, we show that androgens drive anti-tumor immunity in brain tumors, in contrast to its effect in other tumor types. Upon castration, tumor growth was accelerated with attenuated T cell function in GBM and brain tumor models, but the opposite was observed when tumors were located outside the brain. Activity of the hypothalamus-pituitary-adrenal gland (HPA) axis was increased in castrated mice, particularly in those with brain tumors. Blockade of glucocorticoid receptors reversed the accelerated tumor growth in castrated mice, indicating that the effect of castration was mediated by elevated glucocorticoid signaling. Furthermore, this mechanism was not GBM specific, but brain specific, as hyperactivation of the HPA axis was observed with intracranial implantation of non-GBM tumors in the brain. Together, our findings establish that brain tumors drive distinct endocrine-mediated mechanisms in the androgen-deprived setting and highlight the importance of organ-specific effects on anti-tumor immunity.

Fractal-Based Morphometrics of Glioblastoma

Morphometrics have been able to distinguish important features of glioblastoma from magnetic resonance imaging (MRI). Using morphometrics computed on segmentations of various imaging abnormalities, we show that the average and range of lacunarity and fractal dimension values across MRI slices can be prognostic for survival. We look at the repeatability of these metrics to multiple segmentations and how they are impacted by image resolution. We speak to the challenges to overcome before these metrics are included in clinical care, and the insight that they may provide.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

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 (clinical) 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 anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor 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.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma

BACKGROUND

Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a "one size fits all" approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity.

METHODS

Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed.

RESULTS

A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region.

CONCLUSIONS

In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.

Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net

Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treatment effects and surgical cavities are not present. Thus, existing automatic segmentation models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmentation on glioma images with different phenotypic and treatment appearance. We assessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained technicians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

NIMG-19. IMAGE-BASED MODELING MAP OF EDEMA IS CORRELATED WITH MULTIPLE BLOOD-BRAIN-BARRIER PERMEABILITY RELEVANT TRANSCRIPTOMIC MARKERS IN BRAIN TUMOR PATIENTS

Brain tumor associated edema is a significant cause of patient morbidity and mortality often requiring continual treatment with differing levels of success across patients. Edema is a broad term that can indicate increased local water as well as diffusely infiltrating tumor cells. Edema is visualized as hyperintense regions on T2-weighted (T2W) magnetic resonance imaging (MRI) and is typically associated with blood-brain barrier (BBB) breakdown and tumor aggressiveness. Understanding the molecular mechanisms driving imaging patterns of “edema” could provide insights into clinical imaging interpretation. We have an ongoing image-guided biopsy study that allows us to link biopsy molecular markers with locoregional MRI patterns of edema. Further, we previously developed a physics-based method to estimate edema abundance (i.e., edema map) from T2W MRIs. Our goal was to identify connections between BBB-associated molecular factors and edema abundance in brain tumors. Our cohort included 38 patients (female: 15, male: 23) with 129 image-guided biopsies (female: 62, male: 67). We correlated image-localized edema map values with the mean transcriptional frequency for 57 genes related to BBB function. Additionally, we examined correlations separately according to patient reported sex (i.e., male and female) and imaging phenotype (i.e., ENH: enhancing and NE: non-enhancing). We utilized multiple comparisons corrections with a 5% false discovery rate to determine significance. For the overall cohort, we observed significant positive correlations for the HIF1A (p< 0.001) and SOX2 markers with edema. For NE samples, significant correlations included APOE (p=0.001), HIF1A (p< 0.001), PIK3CA (p< 0.001), PTCH1 (p< 0.001), and SOX2 (p< 0.001). Amongst female samples, a significant correlation with PTCH1 (p=0.002) was observed. There were no significant correlations noted for male and enhancing sub-cohorts. Significant correlations between molecular markers of BBB and edema map values could lead to clinical biomarkers for edema or tumor aggressiveness.

TMIC-58. PATTERNS OF CELLULAR SUBPOPULATION COHABITATION DEFINE GLIOBLASTOMA STATES

Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients

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.

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis

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.

A mechanism for improved talc pleurodesis via foam delivery

Talcum powder is recognized as the leading drug for pleurodesis, a treatment of choice for malignant pleural effusions. Recently, it was shown that hydrogel foam delivery systems significantly enhanced the number of adhesions between the chest wall and the lung in a New Zealand rabbit model due to the sol-gel transition. However, many questions still remain regarding the cause of improved efficacy, such as: (1) Would only hydrogel foams improve the efficacy of talc pleurodesis? (2) Is it possible to achieve the same efficacy of hydrogels using non-hydrogel foams? 3) What are the physicochemical properties that can be correlated to the efficacy of talc pleurodesis? In this study, we use non-hydrogel foam formulations to determine the efficacy of pleurodesis. Foam stability and rheology of the formulations were correlated to adhesion formation. The results clearly suggest a correlation of pleurodesis efficacy to the viscosity and modulus of the foam delivery system.

Assessment of Prognostic Value of Cystic Features in Glioblastoma Relative to Sex and Treatment With Standard-of-Care

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

Learning Equations from Biological Data with Limited Time Samples

Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients

Background

Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.

Methods

Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females).

Results

Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test).

Conclusion

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.

TMOD-15. IDENTIFYING THE SPATIAL AND TEMPORAL DYNAMICS OF GLIOBLASTOMA SUBPOPULATIONS WITHIN INDIVIDUAL PATIENTS

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.

NIMG-71. DETECTION OF CYSTIC GLIOBLASTOMA FROM MAGNETIC RESONANCE IMAGING USING DEEP LEARNING TECHNIQUES

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

NIMG-07. LACUNARITY AND FRACTAL DIMENSION ACT AS PRETREATMENT IMAGING BIOMARKERS FOR SURVIVAL IN GLIOBLASTOMA PATIENTS

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

RARE-09. CYSTIC GLIOBLASTOMA PRESENTATION AS A BENEFICIAL PROGNOSTIC INDICATOR FOR OVERALL SURVIVAL

Glioblastoma (GBM) is the most aggressive primary brain tumor with a median overall survival of 15 months with standard-of-care treatment. GBM patients sometimes present with a cystic component, which can be identified through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7–22% of GBM patients and have reported mixed results regarding whether cystic GBM have a survival benefit compared to noncystic GBM. Using our large retrospective cohort of 493 first-diagnosis GBM patients, we aim to elucidate this link between cystic GBM and survival. Within this cohort, 88 patients had a significant cystic component at presentation as identified on MRI. Compared to noncystic GBM (n=405), cystic GBM patients had significantly better overall survival (15 vs 22 months median, log-rank, p=0.001) and were significantly younger at the time of presentation (t-test, p=0.002). However, within patients that received current standard-of-care treatment (n=184), cystic GBM (n=40) was not as beneficial for outcome (22 vs 25 months, log-rank, p=0.3). We also did not observe a significant survival benefit when comparing this standard-of-care cystic cohort to cystic GBM patients diagnosed before the standard was established (n=19, 25 vs 23 months, log-rank, p=0.3), but the analogous result for noncystic GBM patients gives a sizeable benefit, as expected (n=144, n=111, respectively, 22 vs 12 months, log-rank p < 0.0001). Together, these results on current standard-of-care may explain later studies that note no significant survival benefit for cystic GBM patients receiving current standard-of-care. We also report differences in the absolute and relative sizes of imaging abnormalities on MRI and in prognostic impact of cysts based on sex. We discuss current hypotheses for these observed differences, including the possibility that the presence of a cyst could be indicative of a less aggressive tumor.

Psychological, nutritional, and energy expenditure differences in college females with anorexia nervosa vs. comparable-mass controls

This is the first study to examine psychological and behavioral variables in nonhospitalized college females with subclinical anorexia nervosa (AN) as compared to healthy college females of comparable body mass (i.e., body mass index (BMI)<19). Participants who met all DSM-IV [Diagnostic and statistical manual of mental disorders, 4th ed. (1994). Washington, DC: APA.] criteria for AN-restrictive type (except for BMI<17.5; n=11) and control participants (n=15) with comparable body mass completed psychological, nutritional, and exercise assessments. Results suggested that those with AN evidenced more general psychopathology, more eating disorder symptoms, more dieting, more compulsive exercise, and less consumption of calories compared to participants in the control group. There was no difference in macronutrient consumption. There was no significant difference in expenditure of energy, despite differences in reports of compulsive exercise. Given similar body mass, this suggests that the women with AN were experiencing an energy deficit consistent with the disorder's defining features of "fear of gaining weight or becoming fat" and provides us with more understanding of individuals with AN in their natural environment.

Evaluation of a cognitive-behavioral therapy intervention to improve body image and decrease dieting in college women

This two-group experimental study evaluated the effectiveness of a cognitive-behavioral body image intervention, adapted from an effective clinical intervention, with normal college females. Participants included nonclinical, freshman college women who were assigned randomly to either the experimental intervention or the control group (brief educational session). Participants were assessed prior to the intervention and again 1 month later on dieting behavior, body image, fear of fat, and anxiety concerning physical appearance. Although it was hypothesized that each of these variables would be lower in the experimental group, none of these results, except for a trend for decreased dieting, were found. Overall these results of slightly reduced dieting behavior are consistent with other research targeting primary and secondary prevention. This intervention's failure to impact body image and eating behaviors of college students illustrates the continuing challenge of eating disorders prevention.

Comparison of extended versus brief treatments for marijuana use

Adult marijuana users (N = 291) seeking treatment were randomly assigned to an extended 14-session cognitive-behavioral group treatment (relapse prevention support group; RPSG), a brief 2-session individual treatment using motivational interviewing (individualized assessment and intervention; IAI), or a 4-month delayed treatment control (DTC) condition. Results indicated that marijuana use, dependence symptoms, and negative consequences were reduced significantly in relation to pretreatment levels at 1-, 4-, 7-, 13-, and 16-month follow-ups. Participants in the RPSG and IAI treatments showed significantly and substantially greater improvement than DTC participants at the 4-month follow-up. There were no significant differences between RPSG and IAI outcomes at any follow-up. The relative efficacy of brief versus extended interventions for chronic marijuana-using adults is discussed.

Determinants of attrition from cessation treatment in smokers with a history of major depressive disorder

Attrition from smoking cessation treatment by individuals with a history of major depression was investigated. An investigation of preinclusion attrition examined differences between eligible smokers who did (n = 258) and did not (n = 100) attend an initial assessment session. Postinclusion attrition was investigated by comparing early dropouts (n = 33), late dropouts (n = 27), and treatment completers (n = 117). Those who failed to attend the assessment session were more likely to be female, to smoke cigarettes with higher nicotine content, and to have a history of psychotropic medication use. Early-treatment dropouts reported a higher smoking rate than late-treatment dropouts and endorsed more symptoms of depression than late dropouts and treatment completers. Results are compared with previous investigations of smoking cessation attrition, and implications for treatment and attrition prevention are discussed.

Determinants of attrition from cessation treatment in smokers with a history of major depressive disorder

Attrition from smoking cessation treatment by individuals with a history of major depression was investigated. An investigation of preinclusion attrition examined differences between eligible smokers who did (n = 258) and did not (n = 100) attend an initial assessment session. Postinclusion attrition was investigated by comparing early dropouts (n = 33), late dropouts (n = 27), and treatment completers (n = 117). Those who failed to attend the assessment session were more likely to be female, to smoke cigarettes with higher nicotine content, and to have a history of psychotropic medication use. Early-treatment dropouts reported a higher smoking rate than late-treatment dropouts and endorsed more symptoms of depression than late dropouts and treatment completers. Results are compared with previous investigations of smoking cessation attrition, and implications for treatment and attrition prevention are discussed.

On Being a Person of Integrity...Or Ethics and Other Liabilities

The emergence of managed care and the health industry's response to it has precipitated a moral crisis for many health care professionals. Caught between the patients' best interests and the employers' and payers' restrictions, professionals find their good intentions tested. In this article, the author distinguishes moral from ethical problems and suggests that self-examination and self-diagnosis of the mindsets and conditions that contribute to moral malfunctioning in the workplace--self-management at the individual level--is essential for maintaining the ethical stance of the profession and the integrity of the professional.