European radiology
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Smoking is a major risk factor for both cardiovascular disease (CVD) and lung cancer. Aortic valve calcification (AVC) and coronary artery calcification (CAC) are both due to atherosclerotic disease. We aim to investigate whether AVC on low-dose CT (LDCT) predicts death from CVD in smokers beyond that provided by CAC. ⋯ • Aortic valve calcification (AVC) and coronary artery calcification (CAC) are both due to atherosclerotic disease. The prevalence of AVC in lung cancer screening cohort significantly increased with the increasing severity of CAC. • CAC and AVC were significant predictors of cardiovascular disease (CVD) death when considered alone. Participants who underwent lung cancer screening with AVC > 0 and CAC ≥ 4 had more than a 2-fold increased risk of CVD death than the group with AVC = 0 and CAC < 4, when adjusted for other risk factors.
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To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. ⋯ • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.
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To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting. ⋯ • Clinically significant prostate cancer identification and segmentation on multi-parametric MRI is feasible in low-risk patients using a deep neural network. • The deep neural network for significant prostate cancer localization performs better for lesions with larger volumes sizes (> 0.5 cc) as compared to small lesions (> 0.03 cc). • For the evaluation of automatic prostate cancer segmentation methods in the active surveillance cohort, the large discordance group (MRI positive, targeted biopsy negative) should be included.
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To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. ⋯ • A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).
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To systematically review microwave ablation (MWA) protocols, safety, and clinical efficacy for treating bone tumors. ⋯ • Large heterogeneity exists across literature about ablation protocols used with microwave ablation applied for the treatment of benign and malignant bone tumors. • Although microwave ablation of bone tumors appears safe, further studies are needed to assess this aspect, as current literature does not allow definitive conclusions. • Nevertheless, microwave ablation is effective in achieving pain relief at short- (1 month) and mid-term (4-6 months) for painful osteoid osteomas and malignant bone tumors, respectively.