Pulmonary embolism (PE) and deep vein thrombosis (DVT) occurred in 16.5% and 14.8% of patients with coronavirus disease 2019, respectively, and more than half of patients with PE lacked DVT. Background The association of pulmonary embolism (PE) with deep vein thrombosis (DVT) in patients with coronavirus disease 2019 (COVID-19) remains unclear, and the diagnostic accuracy of D-dimer te...

Kim PK, et al. Korean J Radiol. 2017 Jan-Feb;18(1):113-131. https://doi.org/10.3348/kjr.2017.18.1.113

Myocardial extracellular volume fraction determined with dual-energy equilibrium contrast-enhanced cardiac CT showed good agreement, with a small bias and acceptable 95% limits of agreement when compared with cardiac MR imaging, as well as excellent interobserver agreement. PurposeTo evaluate the feasibility of equilibrium contrast material–enhanced dual-energy cardiac computed tomography (CT) to determine extracellular volume fraction (ECV) in nonischemic cardiomyopathy (CMP) compared with magnetic resonance (MR) imaging.Materials and MethodsThis study was approved by the institutional review board; informed consent was obtained. Seven healthy subjects and 23 patients (six with hypertrophic CMP, nine with dilated CMP, four with amyloidosis, and four with sarcoidosis) (mean age ± standard deviation, 57.33 years ± 14.82; 19 male participants [63.3%]) were prospectively enrolled. Twelve minutes after contrast material injection (1.8 mL/kg at 3 mL/sec), dual-energy cardiac CT was performed. ECV was measured by two observers independently. Hematocrit levels were compared between healthy subjects and patients with the Mann-Whitney U test. In per-subject analysis, interobserver agreement for CT was assessed with the intraclass correlation coefficient (ICC), and intertest agreement...

The aim of this study was to investigate whether diffusion-weighted imaging (DWI) could be useful in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Sixty-seven surgically proven HCCs from 65 patients (54 men and 11 women with an age range of 35-75 years and a mean age of 56.0 years) were retrospectively analyzed. The signal intensities (SIs) of the lesions on preoperative diffusion-weighted (DW) images were visually categorized as isointense or hyperintense in comparison with the adjacent liver. We also quantitatively measured SIs and apparent diffusion coefficients (ADCs) by drawing regions of interest in HCCs and livers on DW images with b values of 50, 400, and 800 second/mm² and on ADC map images. Logistic regression analyses were performed so that we could identify independent predictors of MVI among laboratory and DWI findings. A univariate analysis showed that a histological grade of 3, a larger tumor size, a higher SI ratio on DW images, and a lower ADC value were significantly associated with MVI. A multiple logistic regression analysis showed that a histological grade of 3 and the ADC value were independent predictors of MVI. With a cutoff of 1.11 × 10⁻³ mm²/second, the ADC value provided a sensitivity of 93.5% and a specificity of 72.2% for the prediction of MVI with an odds ratio of 24.5 (95% confidence interval = 4.14-144.8, P < 0.001). In conclusion, lower ADC values (1.11 × 10⁻³ mm²/second or less) can be a useful predictor of MVI during the preoperative evaluation of HCC. Liver Transpl 18:1171–1178, 2012. © 2012 AASLD.

Background: We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD‐L1) expression in advanced stage lung adenocarcinoma.Methods: This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD‐L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c‐statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability.Results: Among 153 patients, 53 patients were classified as PD‐L1 positive and 100 patients as PD‐L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad‐score by radiomic analysis was higher in the PD‐L1 positive group than in the PD‐L1 negative group with a statistical significance (−0.378 ± 1.537 vs. −1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c‐statistic = 0.646 vs. 0.550, P = 0.0299).Conclusions: Quantitative CT radiomic features can predict PD‐L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.Key points: Significant findings of the studyQuantitative CT radiomic features can help predict PD‐L1 expression, whereas none of the qualitative imaging findings is associated with PD‐L1 positivity.What this study addsA prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.

Yoon SH, et al. Korean J Radiol. 2020 Jul;21:e174. https://doi.org/10.3348/kjr.2020.0137