European radiology
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To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). ⋯ • Radiomics analysis of pre-CRT multiparameter MR images could predict pCR in patients with LARC. • Proposed radiomics signature from joint T2-w, ADC and cT1-w images showed better predictive performance than individual signatures. • Most of the clinical characteristics were unable to predict pCR.
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The objective of this study was to explore the feasibility of using intracranial T1-weighted vessel wall imaging (VWI) to visualize the lenticulostriate arteries (LSAs) at 3T. ⋯ • T1-weighted intracranial VWI at 3T allows for black-blood MR angiography of lenticulostriate artery. • 3T intracranial VWI depicts the stems and proximal segments of the lenticulostriate arteries comparable to 7T TOF-MRA. • It is feasible to assess both large vessel wall lesions and lenticulostriate vasculopathy in one scan.
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The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classify meningiomas accurately using tree classifiers. ⋯ • A precise preoperative prediction of the WHO grade of a meningioma brings benefits to further treatment plans. • Machine learning models based on clinical, morphological features and ADC value could achieve equivalent diagnostic performance compared to experienced neuroradiologists. • The decision forest model built with 23 selected texture features and the ADC value achieved the best diagnostic performance (kappa = 0.64, accuracy = 79.51%).
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The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. ⋯ We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. KEY POINTS: • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.
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Multicenter Study Clinical Trial
Magnetic resonance enterography, small bowel ultrasound and colonoscopy to diagnose and stage Crohn's disease: patient acceptability and perceived burden.
To compare patient acceptability and burden of magnetic resonance enterography (MRE) and ultrasound (US) to each other, and to other enteric investigations, particularly colonoscopy. ⋯ • MRE and US are rated as acceptable by most patients and superior to colonoscopy. • MRE generates significantly greater burden and longer recovery times than US, particularly in younger patients and those with high levels of emotional distress. • Most patients prefer the experience of undergoing US than MRE; however, patients rate test accuracy as more importance than scan burden.