47 research outputs found

    Imaging Based Prediction of Pathology in Adult Diffuse Glioma with Applications to Therapy and Prognosis

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    The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known. In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the underlying biological heterogeneity via imaging. This provides an approach to answer questions about how tissue heterogeneity can affect prognosis. We estimated the local pathology within tumors using imaging data and stereotactically precise biopsy samples from an ongoing clinical imaging trial. From this data, we trained a random forest model to reliably predict tumor grade, proliferation, cellularity, and vascularity, representing tumor aggressiveness. We then made voxel-wise predictions to map the tumor heterogeneity and identify high-grade malignancy disease. Next, we used the previously trained models on a cohort of 1,850 glioma patients who previously underwent surgical resection. High contrast enhancement, proliferation, vascularity, and cellularity were associated with worse prognosis even after controlling for clinical factors. Patients that had substantial reduction in cellularity between preoperative and postoperative imaging (i.e. due to resection) also showed improved survival. We developed a clinically implementable model for predicting pathology and prognosis after surgery based on imaging. Results from imaging pathology correlations enhance our understanding of disease extent within glioma patients and the relationship between residual estimated pathology and outcome helps refine our knowledge of the interaction of tumor heterogeneity and prognosis

    Towards A Direct Detection of the Spin of Dark Matter

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    We investigate the contribution of higher spin particles in the signal of direct detection experiments searching for dark matter. We consider a bosonic or fermionic higher spin dark matter (HSDM) candidate which interacts with the Standard Model via a dark U(1) mediator. For a particular subclass of interactions, spin-polarized targets may be used for spin determination: The angular dependence of scatterings can distinguish integer (spin-ss) vs. half-integer (spin-s+1/2s + 1/2), while the recoil energy dependence of the signal determines ss. We consider also the signal of a supersymmetric higher spin dark sector, which suggests a characteristic signal (''SUSY Rilles'') for directional direct detection.Comment: Matches published version in PL

    PocketNet: A Smaller Neural Network for Medical Image Analysis

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    Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings

    The New Economy Business Model and Sustainable Prosperity

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    Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

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    BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)
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