627 research outputs found

    A risk-based approach to identifying oligometastatic disease on imaging.

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    Recognition of <3 metastases in <2 organs, particularly in cancers with a known predisposition to oligometastatic disease (OMD) (colorectal, prostate, renal, sarcoma and lung), offers the opportunity to focally treat the lesions identified and confers a survival advantage. The reliability with which OMD is identified depends on the sensitivity of the imaging technique used for detection and may be predicted from phenotypic and genetic factors of the primary tumour, which determine metastatic risk. Whole-body or organ-specific imaging to identify oligometastases requires optimization to achieve maximal sensitivity. Metastatic lesions at multiple locations may require a variety of imaging modalities for best visualisation because the optimal image contrast is determined by tumour biology. Newer imaging techniques used for this purpose require validation. Additionally, rationalisation of imaging strategies is needed, particularly with regard to timing of imaging and follow-up studies. This article reviews the current evidence for the use of imaging for recognising OMD and proposes a risk-based roadmap for identifying patients with true OMD, or at risk of metastatic disease likely to be OM

    Tuned Out. Traditional Music and Identity in Northern Ireland

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    Tuned Out offers a lively and informative history of traditional music in Ireland in which the author attempts to account for the increasing absence of Protestant musicians from the contemporary traditional music scene. By re-visiting the significance of the revival period for traditional music and demonstrating an acute awareness of how the political context shaped both opinion and practice, the author presents an original and multi-faceted piece of work which will make a worthy contribution..

    Successful MRI-Guided Focused Ultrasound Uterine Fibroid Treatment Despite an Ostomy and Significant Abdominal Wall Scarring

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    We present a case of successful magnetic resonance imaging-guided focused ultrasound surgery (MRgFUS) of a uterine fibroid in a patient with extensive anterior abdominal wall surgical scars from two longitudinal laparotomies, a total colectomy and ileostomy. This case demonstrates that MRgFUS can be safely used in patients with an ostomy and significant abdominal wall scarring, but careful pretreatment planning and positioning during treatment is needed

    Is perfect the enemy of good? Weighing the evidence for biparametric MRI in prostate cancer

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    The role of multiparametric MRI in diagnosis, staging and treatment planning for prostate cancer is well established. However there remain several challenges to widespread adoption. One such challenge is the duration and cost the examination. Abbreviated exams omitting contrast enhanced sequences may help address this challenge. In this review, we will discuss the rationale for biparametric MRI (bpMRI) for detection and characterization of clinically significant prostate cancer prior to biopsy and synthesize the published literature. We will weigh up the advantages and disadvantages to this approach and lay out a conceptual cost/benefit analysis regarding adoption of bpMRI

    Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

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    Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure
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