European radiology
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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.
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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.