European radiology
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We evaluated the detection rate and degree of motion artifact of the modified CAIPIRINHA-VIBE (mC-VIBE) without view-sharing and compare them with the CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) with view-sharing on multi-arterial gadoxetic acid-enhanced liver MRI in the assessment of hepatocellular carcinoma (HCC). ⋯ • Multi-arterial phase using the mC-VIBE without view-sharing can overcome motion artifacts, resulting in providing optimal arterial phase imaging. • The HCC-detection rate is slightly higher with the mC-VIBE vs. CAIPIRINHA-Dixon-TWIST-VIBE with view-sharing (CDT-VIBE). • View-sharing of CDT-VIBE in the multi-arterial phase is associated with increased frequency of TSM.
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To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. ⋯ • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.
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This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value. ⋯ • The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup. • Prognosis of high-risk patients remains dismal after curative-intent resection. • The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.
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To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier. ⋯ • An interpretable deep learning system prototype can explain aspects of its decision-making by identifying relevant imaging features and showing where these features are found on an image, facilitating clinical translation. • By providing feedback on the importance of various radiological features in performing differential diagnosis, interpretable deep learning systems have the potential to interface with standardized reporting systems such as LI-RADS, validating ancillary features and improving clinical practicality. • An interpretable deep learning system could potentially add quantitative data to radiologic reports and serve radiologists with evidence-based decision support.
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To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. ⋯ • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.