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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.

Gliomas are brain tumors characterized by highly variable growth patterns. Magnetic resonance imaging (MRI) is the cornerstone of glioma diagnosis and management planning. However, glioma features on MRI do not directly correlate with tumor cell distribution. Additionally, there is evidence that glioma tumor characteristics and prognosis are sex-dependent. Magnetic resonance elastography (MRE) is an imaging technique that allows interrogation of tissue stiffness in-vivo and has found utility in the imaging of several cancers. We investigate the relationship between MRI features, MRE features, and growth parameters derived from an established mathematical model of glioma proliferation and invasion. Results suggest that both the relationship between tumor volume and tumor stiffness as well as the relationship between the parameters derived from the mathematical model and tumor stiffness are sex-dependent. These findings lend evidence to a growing body of knowledge about the clinical importance of sex in the context of cancer diagnosis, prognosis and treatment.

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

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

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p

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

BackgroundSex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.MethodsCombining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients. Pretreatment MR images of 494 glioblastoma patients (299 males and 195 females) were segmented to quantify tumor volumes. Cox proportional hazard (CPH) models and Student’s t-tests were used to assess which variables were associated with survival outcomes.ResultsAmong males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, pConclusionDespite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

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

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

The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.

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

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

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

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

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

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

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

Alcoholic fatty liver disease (AFLD) is characterized by an abnormal accumulation of lipid droplets (LDs) in the liver. Here, we explore the composition of hepatic LDs in a rat model of AFLD. Five to seven weeks of alcohol consumption led to significant increases in hepatic triglyceride mass, along with increases in LD number and size. Additionally, hepatic LDs from rats with early alcoholic liver injury show a decreased ratio of surface phosphatidylcholine (PC) to phosphatidylethanolamine (PE). This occurred in parallel with an increase in the LD association of perilipin 2, a prominent LD protein. To determine if changes to the LD phospholipid composition contributed to differences in protein association with LDs, we constructed liposomes that modeled the LD PC:PE ratios in AFLD and control rats. Reducing the ratio of PC to PE increased the binding of perilipin 2 to liposomes in an in vitro experiment. Moreover, we decreased the ratio of LD PC:PE in NIH 3T3 and AML12 cells by culturing these cells in choline-deficient media. We again detected increased association of specific LD proteins, including perilipin 2. Taken together, our experiments suggest an important link between LD phospholipids, protein composition, and lipid accumulation.

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

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

Excess lipid is stored in an intracellular organelle known as the lipid droplet. While the core of the lipid droplet is rich in neutral lipids, the surface is primarily comprised of two phospholipi...

Temozolomide concurrent with radiation (Stupp protocol) has been one of the most important advances in the treatment of Glioblastoma (GBM), significantly increasing survival times (Stupp 2005, 2009). We examined whether GBM response to Stupp could be associated with patterns of tumor cell invasion using our previously developed patient–specific mathematical models. Drawing from our clinical research database, we identified a cohort of 47 newly diagnosed GBM patients receiving Stupp protocol with MRI followup spanning pre-surgery through first progression. These MRIs allowed us to quantify the distribution of tumor cells throughout the brain with our previously established invasion metric D/ρ (Wang 2009). High D/ρ tumors are more invasive, having a greater proportion of tumor cells beyond the imageable regions of the tumor. We examined whether this index changed following treatment with Stupp protocol. Using linear regression, Cox proportional hazard, and survival analyses (Kaplan-Meier) we examined whether this index in either setting, and/or the change in this index was correlated with patient outcomes. Initial (pre-treatment) D/ρ values were positively correlated with overall survival (p=0.016). Further, patients for which D/ρ decreased between diagnosis and recurrence had favorable outcomes (p=0.0496). Patients who had more diffuse tumors (high D/ρ) initially were more likely to become less diffuse (low D/ρ) at recurrence whereas patients who have low D/ρ initially become more diffuse (p=0.004). This change suggests a shift in tumor phenotype from a more–diffuse pattern of growth to less–diffuse, which may reflect the exposure of those diffusely invaded tumor cells to widely distributed radiation dose and the blood-brain barrier permeable TMZ. Taken together, these data suggest that tumors with increased diffuse invasion are more likely to respond to Stupp and experience improved overall survival.

Excess lipid is stored in organelles known as lipid droplets. Each lipid droplet is composed of a neutral lipid core surrounded by a phospholipid monolayer. The surface of each lipid droplet is dec...