The blood of patients with anemia demonstrates distinctly lower attenuation in unenhanced CT images. However, the frequent usage of intravenous contrast hampers evaluation of anemia. Spectral detector computed tomography (SDCT) allows for reconstruction of virtual non-contrast images (VNC) from contrast-enhanced data (CE). The purpose of this study was to evaluate whether VNC allow for prediction of anemia. Five hundred twenty-two patients with CE-SDCT of the chest and accessible serum hemoglobin (HbS) were retrospectively included. Patients were assigned to three groups (severe anemia, moderate/mild anemia, and healthy) based on recent lab tests (≤ 7 days) for HbS following gender and the WHO definition of anemia. click here CT attenuation was determined using two ROI in the left ventricular lumen and one ROI in the descending thoracic aorta. ROI were placed on CE and copied to VNC. ANOVA, linear regression, and receiver operating characteristics were used for statistic evaluation. Average HbS was 11.6 ± 2.4 g/dlf anemia be estimated in virtual non-contrast images using proposed cutoffs of 39.2 HU and 37.6 HU for men and women, respectively, to differentiate between healthy and anemic patients.• While the attenuation of blood is a previously described biomarker for anemia in non-contrast images, virtual non-contrast images from spectral detector CT circumvent this limitation and allow for diagnosis of anemia in contrast-enhanced scans. • Attenuation of blood in virtual non-contrast images derived from spectral detector CT shows a moderate correlation to serum hemoglobin levels. • Presence of anemia be estimated in virtual non-contrast images using proposed cutoffs of 39.2 HU and 37.6 HU for men and women, respectively, to differentiate between healthy and anemic patients. We aimed to investigate the use of a myelin-sensitive MRI contrast, the standardized T1-weighted/T2-weighted (sT1w/T2w) ratio, for detecting early changes in the middle cerebellar peduncle (MCP) in cerebellar subtype multiple system atrophy (MSA-C) patients. We included 28 MSA-C patients, including a subset of 17 MSA-C patients within 2 years of disease onset (early MSA-C), and 28 matched healthy controls. T1w and T2w scans were acquired using a 3-T MR system. The sT1w/T2w ratio in MCP was analyzed using SPM12 by utilizing a region-of-interest approach in normalized space. The diagnostic performance of the MCP sT1w/T2w ratio in discriminating MSA-C and the subgroup of early MSA-C from the matched controls was assessed. Correlation analyses were performed to evaluate the relationship between the MCP sT1w/T2w ratio and other clinical parameters including the International Cooperative Ataxia Scale (ICARS) score for quantifying cerebellar ataxia. Compared to controls, the sT1w/T2w ratio in the MCP was marke in early MSA-C patients. The high variability of hypertrophic cardiomyopathy (HCM) genetic phenotypes has prompted the establishment of risk-stratification systems that predict the risk of a positive genetic mutation based on clinical and echocardiographic profiles. This study aims to improve mutation-risk prediction by extracting cardiovascular magnetic resonance (CMR) morphological features using a deep learning algorithm. We recruited 198 HCM patients (48% men, aged 47 ± 13years) and divided them into training (147 cases) and test (51 cases) sets based on different genetic testing institutions and CMR scan dates (2012, 2013, respectively). All patients underwent CMR examinations, HCM genetic testing, and an assessment of established genotype scores (Mayo Clinic score I, Mayo Clinic score II, and Toronto score). A deep learning (DL) model was developed to classify the HCM genotypes, based on a nonenhanced four-chamber view of cine images. The areas under the curve (AUCs) for the test set were Mayo Clinic score I (AUC 0.64, setification of HCM patients with positive genotypes.• Deep learning method could enable the extraction of image features from cine images. • Deep learning method based on cine images performed better than established scores in identifying HCM patients with positive genotypes. • The combination of the deep learning method based on cine images and the Toronto score could further improve the performance of the identification of HCM patients with positive genotypes. To validate and compare the performance of the Brock model and Lung CT Screening Reporting and Data System (Lung-RADS) on nodules detected by baseline CT screening. We performed a secondary analysis of the Korean Lung Cancer Screening Project (K-LUCAS; ClinicalTrials.gov , NCT03394703), a nationwide, multicenter, prospective cohort study. From April 2017 to December 2018, low-dose CT screening was performed on high-risk subjects. Discrimination and calibration of Brock models 2a and 2b (i.e., full model without and with spiculation, respectively) were assessed, and discrimination was compared with that of Lung-RADS, which utilized subjective assessment categories 2b (b stands for benign) and 4X. Of the 13,150 subjects, 4578 were eligible (median age 62 years; 4458 men; 9929 nodules including 40 lung cancers). Areas under the receiver operating characteristic curve were 0.96 (IQR 0.92-0.99) for Brock model 2a, 0.96 (IQR 0.92-0.99) for Brock model 2b, and 0.95 (IQR 0.91-0.99) for Lung-RADS (p = 0.32 and p model 2b (p = 0.001 and p = 0.02, respectively). • The Brock model showed poor calibration (p < 0.001).• Brock model 2b and Lung CT Screening Reporting and Data System (Lung-RADS) demonstrated a similar discrimination performance for lung cancer in the baseline CT screening (areas under the receiver operating characteristic curve 0.96 vs. 0.95; p = 0.34). • When visual assessment-based categories were removed from Lung-RADS, specificity and positive predictive value were lower than those of Brock model 2b (p = 0.001 and p = 0.02, respectively). • The Brock model showed poor calibration (p less then 0.001). To evaluate the reliability and validity of measuring subchondral trabecular biomarkers in "conventional" intermediate-weighted (IW) MRI sequences and to assess the predictive value of biomarker changes for predicting near-term symptomatic and structural progressions in knee osteoarthritis (OA). For this study, a framework for measuring trabecular biomarkers in the proximal medial tibia in the "conventional" IW MRI sequence was developed. The reliability of measuring these biomarkers (trabecular thickness [cTbTh], spacing [cTbSp], connectivity density [cConnD], and bone-to-total volume ratio [cBV/TV]) was evaluated in the Bone Ancillary Study (within the Osteoarthritis Initiative [OAI]). The validity of these measurements was assessed by comparing to "apparent" biomarkers (from high-resolution steady-state MRI sequence) and peri-articular bone marrow density (BMD, from dual-energy X-ray absorptiometry). The association of these biomarker changes from baseline to 24 months (using the Reliable Change Index) with knee OA progression was studied in the FNIH OA Biomarkers Consortium (within the OAI).