The British journal of radiology
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Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume. ⋯ We believe all three approaches have their strengths and weaknesses, but they should all strive to create Findable, Accessible, Interoperable, Reusable (FAIR) data. To learn from these data, we need distributed learning techniques, sending machine learning algorithms to FAIR data stores around the world, learning from trial data, registries and routine clinical data rather than trying to centralize all data. To improve and personalize medicine, rapid learning platforms must be able to process FAIR "Big Data" to evaluate current clinical practice and to guide further innovation.
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The objectives of this article were: (1) to review common and rare manifestations of systemic and pulmonary Langerhans cell histiocytosis, Rosai-Dorfman disease, Erdheim-Chester disease and juvenile xanthogranuloma; (2) to provide the reader with important pathologic, epidemiologic and clinical features of these diseases. The histiocytoses are a diverse group of diseases which typically manifest with multiorgan involvement. Understanding the pathologic, epidemiologic and clinical features of these entities can help the radiologist suggest an accurate diagnosis of histiocytosis when typical imaging features are encountered.
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Review
Clinical interpretation of high-resolution vessel wall MRI of intracranial arterial diseases.
Intracranial arterial pathology has traditionally been evaluated with luminal imaging. Recently, high-resolution vessel wall imaging (HR-VWI) with MRI has facilitated submillimetre evaluation of the arterial walls. ⋯ Interpretation of HR-VWI examinations requires a solid understanding of the pathophysiology, clinical features, serum and cerebrospinal fluid laboratory findings, treatment administered and fundamental patterns of VWI abnormalities that may be encountered with the intracranial vasculopathies. This pictorial essay aimed to illustrate the essential findings of common conditions encountered with HR-VWI including intracranial atherosclerosis, moyamoya disease, intracranial vasculitis, varicella zoster vasculopathy, reversible cerebral vasoconstriction syndrome and aneurysms.
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To investigate the efficacy of the radial acquisition regime (RADAR) for acquiring head and neck MR images. ⋯ RADAR-T2WI could replace FSE-T2WI as a conventional T2WI protocol for the head and neck. For the RADAR-DWI sequence, validation studies are needed. Advances in knowledge: RADAR-T2WI was superior to FSE-T2WI with regard to artefacts and detectability, and RADAR-DWI was superior in terms of artefacts compared with SS-EPI-DWI.
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To assess the clinical feasibility of whole-body diffusion-weighted MRI (WB-DWI/MRI) for diagnosis and prediction of complete tumour resection in patients with suspected recurrent ovarian cancer. ⋯ WB-DWI/MRI allowed better detection of ovarian cancer recurrence and better prediction of complete resection than CT. Advances in knowledge: WB-DWI/MRI could assist in optimizing treatment planning for recurrent ovarian cancer, particularly by improving patient selection for salvage surgery, thus giving eligible patients the highest chance on prolonged survival and refraining patients who would not benefit from extensive surgery reducing related morbidity and mortality.