Role of diffusion-weighted MRI in differentiation between benign and malignant anterior mediastinal masses.

Diffusion-weighted imaging (DWI) is considered to be a useful biomarker to characterize the cellularity of lesions, yet its application in the thorax to evaluate anterior mediastinal lesions has not been well investigated. The aims of our study were to describe the magnetic resonance (MR) characteristics of anterior mediastinal masses and to assess the role of apparent diffusion coefficient (ADC) value in distinguishing benign from malignant lesions of the anterior mediastinum. We conducted a retrospective cross-sectional study including 55 patients with anterior mediastinal masses who underwent preinterventional MR scanning with the following sequences: T1 VIBE DIXON pre and post-contrast, T2 HASTE, T2 TIRM, DWI-ADC map (b values of 0 and 2000 sec/mm2). The ADC measurements were obtained by two approaches: hot-spot ROI and whole-tumor histogram analysis. The lesions were grouped by three distinct ways: benign versus malignant, group A (benign lesions and type A, AB, B1 thymoma) versus group B (type B2, B3 thymoma and other malignant lesions), lymphoma versus other malignancies. The study was composed of 55 patients, with 5 benign lesions and 50 malignant lesions. The ADCmean, ADCmedian, ADC10, ADC90 in the histogram-based approach and the hot-spot-ROI-based mean ADC of the malignant lesions were significantly lower than those of benign lesions (P values< 0.05). The hot-spot-ROI-based mean ADC had the highest value in differentiation between benign and malignant mediastinal lesions, as well as between group A and group B; the ADC cutoffs (with sensitivity, specificity) to differentiate malignant from benign lesions and group A from group B were 1.17 x 10-3 mm2/sec (80%, 80%) and 0.99 x 10-3 mm2/sec (78.4%, 88.9%), respectively. The ADC values obtained by using the hot-spot-ROI-based and the histogram-based approaches are helpful in differentiating benign and malignant anterior mediastinal masses.

Correlation of Chest X-Ray Scores in SARS-CoV-2 Patients With the Clinical Severity Classification and the Quick COVID-19 Severity Index.

Objectives This study aimed to assess the role of chest X-ray (CXR) scoring methods and their correlations with the clinical severity categories and the Quick COVID-19 Severity Index (qCSI). Methods We conducted a retrospective study of 159 COVID-19 patients who were diagnosed and treated at the University Medical Center between July and September 2021. Chest X-ray findings were evaluated, and severity scores were calculated using the modified CXR (mCXR), Radiographic Assessment of Lung Edema (RALE), and Brixia scoring systems. The three scores were then compared to the clinical severity categories and the qCSI using Spearman's correlation coefficient. Results Overall, 159 patients (63 males and 96 females) (mean age: 58.3 ± 15.7 years) were included. The correlation coefficients between the mCXR score and the Brixia and RALE scores were 0.9438 and 0.9450, respectively. The correlation coefficient between the RALE and Brixia scores was marginally higher, at 0.9625. The correlation coefficients between the qCSI and the Brixia, RALE, and mCXR scores were 0.7298, 0.7408, and 0.7156, respectively. The significant difference in the mean values of the three CXR scores between asymptomatic, mild, moderate, severe, and critical groups was also noted. Conclusions There were strong correlations between the three CXR scores and the clinical severity classification and the qCSI.

Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study.

Introduction Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Early detection and accurate diagnosis of HCC play an important role in patient management. This study aimed to develop a convolutional neural network-based model to identify and segment HCC lesions utilizing dynamic contrast agent-enhanced computed tomography (CT). Methods This retrospective study used CT image sets of histopathology-confirmed hepatocellular carcinoma over three phases (arterial, venous, and delayed). The proposed convolutional neural network (CNN) segmentation method was based on the U-Net architecture and trained using the domain adaptation technique. The proposed method was evaluated using 115 liver masses of 110 patients (87 men and 23 women; mean age, 56.9 years ± 11.9 (SD); mean mass size, 6.0 cm ± 3.6). The sensitivity for identifying HCC of the model and Dice score for segmentation of liver masses between radiologists and the CNN model were calculated for the test set. Results The sensitivity for HCC identification of the model was 100%. The median Dice score for HCC segmenting between radiologists and the CNN model was 0.81 for the test set. Conclusion Deep learning with CNN had high performance in the identification and segmentation of HCC on dynamic CT.

Role of Dual-Energy Computed Tomography in the Identification of Monosodium Urate Deposition in Gout Patients: A Comprehensive Analysis of 828 Joints According to Structural Joint Damage.

Background Dual-energy computed tomography (DECT) has become a promising, non-invasive procedure for the visualization, characterization, and quantification of monosodium urate (MSU) crystals, which aids clinicians in the diagnosis of gout. In this study, we aimed to examine the diagnostic accuracy of DECT in the evaluation of gout. Methodology This cross-sectional retrospective study included patients who were clinically diagnosed with gout and underwent a DECT scan. Results A majority (80.4%) of the MSU deposits were found in the ankle joints. The presence of MSU deposits on DECT scan was highly correlated with bone erosion in the upper limb (odds ratio [OR] = 132; 95% confidence interval [CI] = 17.3-1004.3), bone sclerosis in the lower limb (OR = 36.4; 95% CI = 15.4-86.1), bone erosion in metacarpophalangeal joints (OR = 160; 95% CI = 42.7-600.2), and bone sclerosis in metatarsophalangeal joints (OR = 35.6; 95% CI = 15.5-81.9). Using linear regression analysis on patient-level data, correlations were found between DECT MSU crystal deposition and damage on all categories of structural joint damage showing significant association with erosion (r = 0.91, p < 0.001) and space narrowing (r = 0.75, p < 0.001) but not with joints having periarticular calcification (r = 0.52, p < 0.041). Conclusions Our study established DECT as a valid method for detecting MSU deposits and their association with structural joint deterioration in a Vietnamese population.

Clinical and laboratory factors associated with coronavirus disease 2019 (Covid-19): A systematic review and meta-analysis.

SARS Coronavirus-2 is one of the most widespread viruses globally during the 21st century, whose severity and ability to cause severe pneumonia and death vary. We performed a comprehensive systematic review of all studies that met our standardised criteria and then extracted data on the age, symptoms, and different treatments of Covid-19 patients and the prognosis of this disease during follow-up. Cases in this study were divided according to severity and death status and meta-analysed separately using raw mean and single proportion methods. We included 171 complete studies including 62,909 confirmed cases of Covid-19, of which 148 studies were meta-analysed. Symptoms clearly emerged in an escalating manner from mild-moderate symptoms, pneumonia, severe-critical to the group of non-survivors. Hypertension (Pooled proportion (PP): 0.48 [95% Confident interval (CI): 0.35-0.61]), diabetes (PP: 0.23 [95% CI: 0.16-0.33]) and smoking (PP: 0.12 [95% CI: 0.03-0.38]) were highest regarding pre-infection comorbidities in the non-survivor group. While acute respiratory distress syndrome (PP: 0.49 [95% CI: 0.29-0.78]), (PP: 0.63 [95% CI: 0.34-0.97]) remained one of the most common complications in the severe and death group respectively. Bilateral ground-glass opacification (PP: 0.68 [95% CI: 0.59-0.75]) was the most visible radiological image. The mortality rates estimated (PP: 0.11 [95% CI: 0.06-0.19]), (PP: 0.03 [95% CI: 0.01-0.05]), and (PP: 0.01 [95% CI: 0-0.3]) in severe-critical, pneumonia and mild-moderate groups respectively. This study can serve as a high evidence guideline for different clinical presentations of Covid-19, graded from mild to severe, and for special forms like pneumonia and death groups.