European radiology
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To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). ⋯ • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.
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To evaluate gender differences in the authorship of articles published in two major European radiology journals, European Radiology (EurRad) and CardioVascular and Interventional Radiology (CVIR). ⋯ • There was a significant increase in female authorship in original diagnostic but not interventional imaging research articles between 2002 and 2016. • There is a strong influence of the radiological subspecialty on the percentage of female authors. • Women are significantly more frequently first authors when the last author is a woman.
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Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images. ⋯ • A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.
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To present a deep learning-based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network (CNN). ⋯ • Prostate cancer classification using a deep learning model is feasible and it allows direct processing of MR sequences without prior lesion segmentation. • Prostate cancer classification performance as measured by AUC is comparable to that of an experienced radiologist. • Perfusion MR images (K-trans), followed by DWI and ADC, have the highest effect on the overall performance; whereas T2w images show hardly any improvement.
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A combination of T2/FLAIR mismatch sign and advanced imaging parameters may improve the determination of molecular subtypes of diffuse lower-grade glioma. We assessed the diagnostic value of adding the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) to the T2/FLAIR mismatch sign for differentiation of the IDH mutation or 1p/19q codeletion. ⋯ • The combination of the T2/FLAIR mismatch sign with the ADC or CBV histogram parameters can improve the identification of IDHmut-Noncodel diffuse lower-grade gliomas. • The multivariable model showed a significantly better performance for distinguishing the IDHmut-Noncodel group from other diffuse lower-grade gliomas than the T2/FLAIR mismatch sign alone or any single parameter. • The IDHmut-Noncodel type was associated with intermediate treatment outcomes; therefore, the identification of IDHmut-Noncodel diffuse lower-grade gliomas could be helpful for determining the clinical approach.