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      Effects of Androgen Deprivation on Cerebral Morphometry in Prostate Cancer Patients – An Exploratory Study

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          Abstract

          Background

          Androgen deprivation therapy (ADT) is a common treatment for non-metastatic, low-risk prostate cancer, but a potential side effect of ADT is impaired brain functioning. Previous work with functional magnetic resonance imaging (MRI) demonstrated altered prefrontal cortical activations in cognitive control, with undetectable changes in behavioral performance. Given the utility of brain imaging in identifying the potentially deleterious effects of ADT on brain functions, the current study examined the effects of ADT on cerebral structures using high resolution MRI and voxel-based morphometry (VBM).

          Methods

          High resolution T1 weighted image of the whole brain were acquired at baseline and six months after ADT for 12 prostate cancer patients and 12 demographically matched non-exposed control participants imaged at the same time points. Brain images were segmented into gray matter, white matter and cerebral ventricles using the VBM toolbox as implemented in Statistical Parametric Mapping 8.

          Results

          Compared to baseline scan, prostate cancer patients undergoing ADT showed decreased gray matter volume in frontopolar cortex, dorsolateral prefrontal cortex and primary motor cortex, whereas the non-exposed control participants did not show such changes. In addition, the decrease in gray matter volume of the primary motor cortex showed a significant correlation with longer reaction time to target detection in a working memory task.

          Conclusions

          ADT can affect cerebral gray matter volumes in prostate cancer patients. If replicated, these results may facilitate future studies of cognitive function and quality of life in men receiving ADT, and can also help clinicians weigh the benefits and risks of hormonal therapy in the treatment of prostate cancer.

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          Most cited references 27

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          Fast and robust parameter estimation for statistical partial volume models in brain MRI.

          Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.
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            Adaptive non-local means denoising of MR images with spatially varying noise levels.

            To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. (c) 2009 Wiley-Liss, Inc.
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              Statistical approach to segmentation of single-channel cerebral MR images.

              A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                19 August 2013
                : 8
                : 8
                Affiliations
                [1 ]Department of Internal Medicine & Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [2 ]Medical Service, VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
                [3 ]Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [4 ]Department of Science and Technology, Federal University of Sao Paolo, Salo Paolo, Brazil
                [5 ]Tower Research, Los Angeles, California, United States of America
                [6 ]Clinical Epidemiology Research Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
                [7 ]Deparment of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [8 ]Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, United States of America
                University of North Carolina at Chapel Hill, United States of America
                Author notes

                Competing Interests: Dr. Edward Uchio who participated in this study as an employee of the Department of Veterans Affairs/US government is currently employed by a private company, Tower Research, Los Angeles, CA. The authors confirm that there are no competing interests as a result of the current employment and that this does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: HHC EU MR JC C-SRL. Performed the experiments: HHC SH EU. Analyzed the data: JI SZ C-SRL. Wrote the paper: HHC MR JC C-SRL. Read and approved the final manuscript: HHC SH JI EU SZ MR JC C-SRL.

                Article
                PONE-D-13-12660
                10.1371/journal.pone.0072032
                3747074
                23977199

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 6
                Funding
                This study was supported by NIH grant R03CA138121 to H.H.C., a Yale Cancer Center/Connecticut Challenge Survivorship pilot award, and VA Urology Research Sectional Funding. The funding agencies had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
                Categories
                Research Article
                Biology
                Neuroscience
                Cognitive Neuroscience
                Cognition
                Decision Making
                Neuroimaging
                Fmri
                Behavioral Neuroscience
                Medicine
                Drugs and Devices
                Adverse Reactions
                Oncology
                Cancers and Neoplasms
                Genitourinary Tract Tumors
                Prostate Cancer
                Cancer Treatment
                Urology
                Prostate Diseases
                Prostate Cancer
                Social and Behavioral Sciences
                Psychology
                Cognitive Psychology
                Problem Solving
                Neuropsychology

                Uncategorized

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