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

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

Malignant large artery stroke is associated with high mortality of 70% to 80% with best medical management. Decompressive craniectomy (DC) is a highly effective tool in reducing mortality. Convincing evidence has accumulated from several randomized trials, in addition to multiple retrospective studies, that demonstrate not only survival benefit but also improved functional outcome with DC in appropriately selected patients. This article explores in detail the evidence for DC, nuances regarding patient selection, and applicability of DC for supratentorial intracerebral hemorrhage and posterior fossa ischemic and hemorrhagic stroke.

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

This article addresses two related issues: (a) When a new imaging agent is proposed, how does the imager integrate it with other biomarkers, either sampled or imaged? (b) When we have multiple imaging agents, is the information additive or duplicative and how is this objectively determined? Molecular biology is leading to new treatment options with reduced normal tissue toxicity, and imaging should have a role in objectively evaluating new treatments. There are two roles for molecular characterization of disease. Molecular imaging measurements before therapy help predict the aggressiveness of disease and identify therapeutic targets and, therefore, help choose the optimal therapy for an individual. Measurements of specific biochemical processes made during or after therapy should be sensitive measures of tumor response. The rules of evidence are not fully developed for the prognostic role of imaging biomarkers, but the potential of molecular imaging provides compelling motivation to push forward with convincing validation studies. New imaging procedures need to be characterized for their effectiveness under realistic clinical conditions to improve the management of patients and achieve a better outcome. The purpose of this article is to promote a critical discussion within the molecular imaging community because our future value to the overall biomedical community will be in supporting better treatment outcomes rather than in detection.

Over the last 10 years increasingly complex mathematical models of cancerous growths have been developed, especially on solid tumors, in which growth primarily comes from cellular proliferation. The invasiveness of gliomas, however, requires a change in the concept to include cellular motility in addition to proliferative growth. In this article we review some of the recent developments in mathematical modeling of gliomas. We begin with a model of untreated gliomas and continue with models of polyclonal gliomas following chemotherapy or surgical resection. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have recently been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter on a geometrically complex brain domain, including sulcal boundaries, with a resolution of 1 mm 3 voxels. We conclude that the velocity of expansion is linear with time and varies about 10-fold, from about 4 mm/year for low-grade gliomas to about 3 mm/month for high-grade ones. D 2003 Elsevier B.V. All rights reserved.

Gliomas are diffuse and invasive neoplasms of the brain with the ability to recur despite extensive treatment. We have developed a mathematical model to describe the growth and invasion of glioma cells throughout an anatomically accurate virtual human brain as well as the effects of operation on these lesions. The model predicts behavior similar to that observed clinically and presents itself as a possible tool for future clinical application.

Prostate-specific antigen (PSA) is an enzyme produced by both normal and cancerous prostate epithelial cells. Although PSA is the most widely used serum marker to detect and follow patients with prostatic adenocarcinoma, there are certain anomalies in the values of serum levels of PSA that are not understood. We developed a mathematical model for the dynamics of serum levels of PSA as a function of the tumor volume. Our model results show good agreement with experimental observations and provide an explanation for the existence of significant prostatic tumor mass despite a low-serum PSA. This result can be very useful in enhancing the use of serum PSA levels as a marker for cancer growth.

initial presentation. The counterparts at recurrence were 35 and 20, respectively, and 19 patients have no evidence of recurrence at the time of this analysis. For patients treated with 60 Gy, In NGR-contacting initial tumor group, NGR-contacting recurrence was more frequently observed compared with NGR-non-contacting initial tumor group. However, similar difference was not observed among 80 Gy patients (Table). Time to progression was not significantly different between 60 Gy and 80 Gy groups. Conclusions: Malignant glioma recurrences are significantly associated with NGR-contact and radiation therapy dose. NGR-contacting initial tumors are prone to NGR-contacting tumors, and high dose radiation therapy is associated with decreased probability of NGR-contacting recurrence of NGR-contacting initial tumors. Author Disclosure:H. Igaki: None. T. Magome: None.M. Sakuramachi: None. A. Nomoto: None. A. Sakumi: None. M. Kitaguchi: None. A. Haga: None. J. Itami: None. K. Nakagawa: None.

Purpose/Objective(s): A complete resolution of enhancing tumor on T1 weighted contrast MRI of patients with glioblastoma following resection and chemoradiation has been demonstrated to predict for longer survival. We have developed a novel method for discriminating time to progression following therapy for glioblastoma using computational models of the proliferative and diffusive tumor burden that account for the unique geometry and biology of each patient’s tumor. Using kinetic models informed by patient-specific measurements, we simulate the growth of an untreated tumor and estimate the number of “Days Gained” by the therapy. The goal of this study was to determine whether this metric of patient response would assist in evaluating patients who have residual T1-enhancing lesions following treatment with concurrent chemoradiation for glioblastoma. Materials/Methods: Twenty-eight patients with glioblastoma were prospectively enrolled prior to chemoradiation in our IRB approved image database study from 2003 to 2009. All patients underwent chemoradiation therapy at the University of Washington and had evidence of residual enhancing tumor on T1 weighted images in the first postradiation MRI study. Upon examination of subsequent images and clinical interventions we found evidence of pseudo-progression in 6 of these patients. We created individualized models of tumor growth for these 28 patients and computed their “Days Gained” score a measure of deflection of actual tumor growth from the model-predicted untreated tumor burden. Results: Based on a reporter-operator curve, we found that 104 Days Gained was an optimal discriminating cutoff (area under curve [AUC] Z 76.1%). We found that all patients with pseudo-progression (n Z 6) had Days Gained scores of 104 or above, with an odds ratio of 5.5 (p Z 0.03). None of the patients with Days Gained scores less than 104 (n Z 11) had pseudo-progression. There was no significant difference between the patients with greater than 104 Days Gained and no pseudo-progression (n Z 11) and those who had pseudo-progression. Additionally, patients with a higher Days Gained score had a longer predicted survival (median 610 vs. 376 days, p Z 0.04). Conclusions: Our novel method for measuring the predicted impact of treatment on both diffuse and proliferative tumor burden for glioblastoma discriminates those patients who are likely to have true progression on their first post-treatment scan. While we anticipated that patients with pseudo-progression would have the lowest estimate of Days Gained due to nonspecific imaging changes, we find instead that these patients are indistinguishable from the long-term responders. This difference in Days Gained may indicate that there is a robust biological response underlying the patients with an increased survival probability that is the subject of further investigation. Author Disclosure: A.D. Trister: None. M.L. Neal: None. T. Cloke: None. A.L. Baldock: None. S. Ahn: None. M.M. Mrugala: None. J.K. Rockhill: F. Honoraria; Elekta. R. Rockne: None. K.R. Swanson: None.