European radiology
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To assess the discriminative power of a 5-min quantitative double-echo steady-state (qDESS) sequence for simultaneous T2 measurements of cartilage and meniscus, and structural knee osteoarthritis (OA) assessment, in a clinical OA population, using radiographic knee OA as reference standard. ⋯ • Quantitative T2values of cartilage and meniscus as well as structural assessment of the knee with a single 5-min quantitative double-echo steady-state (qDESS) scan can distinguish between different grades of knee osteoarthritis (OA). • Quantitative and structural qDESS-based measurements correlate significantly with the reference standard, radiographic degree of OA, for all cartilage and meniscus regions. • By providing quantitative measurements and diagnostic image quality in one rapid MRI scan, qDESS has great potential for application in large-scale clinical trials in knee OA.
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To determine whether diffusion- and perfusion-weighted MRI-based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) METHODS: Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning-based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28). ⋯ • The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.
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To compare imaging and clinical features of fungal and Staphylococcus aureus discitis-osteomyelitis (DO) for patients presenting for CT-guided biopsies. ⋯ • MRI features of discitis-osteomyelitis (focal partial soft tissue abnormality and partial involvement of the disc/endplate) in combination with clinical features may help to predict fungal species as a causative organism for DO.
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To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction. ⋯ • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUVmaxof small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.
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To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer. ⋯ • This study investigated the value of deep learning dual-energy CT-based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT-based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.