The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

A radiomics model based on preoperative contrast-enhanced CT was used to predict the aggressive macrotrabecular-massive subtype of hepatocellular carcinoma and was associated with defective humoral... Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expec...

For patients with hepatocellular carcinoma, intratumor necrosis at CT was predictive of both macrotrabecular-massive subtype and vessels encapsulating tumor clusters pattern, and a distinct CT imag... Background Macrotrabecular-massive (MTM) subtype and vessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. Purpose To esti...

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

Aims/hypotheses: It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to investigate the neural basis of cognitive dysfunction in type 2 diabetes (T2D) patients. Alterations in brain functional connectivity may underlie diabetes-related cognitive dysfunction and brain damage. The aim of this study was to investigate the changes in default mode network (DMN) connectivity in different glucose metabolism status and diabetes duration. Methods: We used a seed-based fMRI analysis to investigate positive and negative DMN connectivity in four groups (39 subjects with normal glucose metabolism [NGM], 23 subjects with impaired glucose metabolism [IGM; i.e., prediabetes], 59 T2D patients with a diabetes duration of < 10 years, and 24 T2D patients with a diabetes duration of >= 10 years). Results: Negative DMN connectivity increased and then regressed with deteriorating glucose metabolism status and extending diabetes duration. DMN connectivity showed a significant correlation with diabetes duration. Conclusion/interpretation: This study suggests that DMN connectivity may exhibit distinct patterns in different glucose metabolism status and diabetes duration, providing some potential neuroimaging evidence for early diagnosis and further understanding of the pathophysiological mechanisms of diabetic brain damage.

Objective: To determine the predictive value of CT and clinical characteristics for short-term disease progression in patients with 2019 novel coronavirus pneumonia (NCP). Materials and Methods: 224 patients with confirmed 2019 novel coronavirus (COVID-19) infection outside Wuhan who had chest CT examinations were retrospectively screened. Clinical data were obtained from electronic medical records. CT images were reviewed and scored for lesion distribution, lobe and segment involvement, ground-glass opacities, consolidation, and interstitial thickening. All included patients with moderate NCP were observed for at least 14 days from admission to determine whether they exacerbated to severe NCP (progressive group) or not (stable group). CT and clinical characteristics between the two groups were compared, and multivariate logistic regression and sensitivity analyses were performed to identify the risk factors for developing severe NCP. Results: A total of 141 patients with moderate NCP were included, of which 15 (10.6%) patients developed severe NCP during hospitalization and assigned to the progressive group. Multivariate logistic regression analysis showed that higher neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR] and 95% confidence interval [CI], 1.26 [1.04-1.53]; P = 0.018) and CT severity score (OR and 95% CI, 1.25 [1.08-1.46]; P = 0.004) on admission were independent predictors for progression to severe NCP, and sensitivity analysis confirmed the consistent results in nonimported patients but not in imported patients. However, no significant difference in lung involvement was found on CT between imported and nonimported patients (all P > 0.05). Patients who were admitted more than 4 days from symptom onset tended to have more severe lung involvement. Spearman correlation analysis showed the close association between CT severity score and inflammatory indexes ( r = 0.17∼0.47, all P < 0.05). Conclusion: CT severity score was associated with inflammatory levels and higher NLR and CT severity score on admission were independent risk factors for short-term progression in patients with NCP outside Wuhan. Furthermore, early admission and surveillance by CT should be recommended to improve clinical outcomes.