The Diagnostic Accuracy of the Hounsfield Unit Value in Pulmonary Embolism.

Objective: Pulmonary embolism (PE) a vascular disease. Computed tomography pulmonary angiography (CTPA) is the radiological imaging technique used to diagnose PE. In this study, we aimed to demonstrate the diagnostic accuracy of Hounsfield Unit (HU) value for PE based on the hypothesis that acute thrombosis causes an increase in HU value on computed tomography (CT). Methods: This research was as a single-center, retrospective study. Patients presenting to the emergency department (ED) diagnosed with PE on CTPA were enrolled as the study group. In addition, patients admitted to the same emergency department who were not diagnosed with PE and had non-contrast CT scans were included as the control group. A receiver operating curve (ROC) was produced to the diagnostic accuracy of HU values in predicting PE. Results: The study population (N=74) consisted of a study group (N=46) and a control group (N=28). The sensitivity and specificity of HU value for predicting PE on thoracic CT were found 61.5% and 96.4% at a value of 54.8 (Area Under the Curve (AUC):0.690) for right main pulmonary artery; 65.0% and 96.4% at a value of 55.9 (AUC:0.736) for left main pulmonary artery; 44.4% and 96.4% at a value of 62.7 (AUC:0.615) for right interlobar artery; and 60.0% and 92.9% at a value of 56.7 (AUC:0.736) for left interlobar artery. Conclusion: HU values may exhibit high diagnostic specificity on CT, for thrombi up to the interlobar level. An HU value exceeding 54.8 up to the interlobar level may raise suspicion of the presence of PE.

Could the Cerebral Involvement of COVID-19 Disease be Related to Microstructural Changes that are not Reflected in Conventional MRI Images?

In patients with COVID-19, neurodegeneration may develop before clinical symptoms appear. Diffusion-weighted (DW) MRI is an important technique for analyzing microstructural changes such as gliosis. In this study, a quantitative evaluation of microstructural changes in the brain with apparent diffusion coefficient (ADC) values in patients presenting with a headache after the COVID-19 disease was analyzed and compared. DW MR images of patients of 20 COVID-19 patients (13 females, 7 males) who required imaging due to headache; 20 controls (16 females, 4 males) were retrospectively reevaluated. ADC measurements were taken from 16 regions of the brain, including right and left symmetrical in patients with COVID-19 infections and control groups. All regions of interest (ROIs) were taken from the hypothalamus, parahippocampus, thalamus, corpus striatum, cingulate gyrus, occipital gyrus, dentate nucleus, and medulla oblongata posterior. ADC values in the dentate nucleus right (784.6 ± 75.7 vs. 717.25 ± 50.75), dentate nucleus left (768.05 ± 69.76 vs. 711.40 ± 52.99), right thalamus (731.15 ± 38.14 vs. 701.60 ± 43.65), left thalamus (744.05 ± 39.00 vs. 702.85 ± 28.88), right parahippocampus (789.10 ± 56.35 vs. 754.75 ± 33.78), right corpus striatum (710.00 ± 39.81 vs. 681.55 ± 39.84) were significantly higher than those in the control group. No significant changes were observed in other areas. A significant increase in ADC values at many levels in the brain in patients with COVID-19 disease and headache was observed. Thus, this study indicates that cerebral involvement in COVID-19 disease may be related to microstructural changes that are not reflected in conventional MRI images.

Lumbrical muscle enhancement on MRI and its association with rheumatoid arthritis.

OBJECTIVE: To investigate the significance of lumbrical muscle enhancement (LME) on magnetic resonance imaging (MRI) in rheumatoid arthritis (RA). MATERIALS AND METHODS: Blinded to the diagnoses, contrast-enhanced bilateral hand MRIs of patients with suspected early RA between 2014 and 2019 were reviewed by two observers for the presence and degree (weak/strong) of LME. The presence of other inflammatory findings was also noted. The patients were then stratified into RA (n = 41), control (n = 31), and other arthritides groups(n = 28) based on their final diagnoses in the hospital records within the following 12 months. Categorical variables were compared by chi-square test or Fisher's exact test. Differences among the groups were evaluated by one-way ANOVA or Kruskal-Wallis tests. When the p-value from the Kruskal-Wallis test was statistically significant, multiple comparison test was used to identify group differences. Correlations between LME and flexor tenosynovitis were evaluated by Spearman rank correlation test. The agreement between two observers was assessed by Cohen's Kappa (κ) statistic. P-value < 0.05 was considered as statistically significant. RESULTS: There were 100 patients (88 females) with mean age of 47.2 ± 11.2. There were no significant differences for age or sex between groups (p = 0.17, p = 0.84, respectively). RA patients showed significantly more frequent (p < 0.001) and stronger LME (p = 0.001). There were no correlations between LME and flexor tenosynovitis (p > 0.05). Interrater agreement for the degree of LME on right and left sides was substantial (κ = 0.74, κ = 0.67, respectively). CONCLUSION: RA patients demonstrated significantly more frequent and stronger LME with substantial interrater agreement. LME could constitute a subtle radiological clue for early RA.

The Effect of Sarcopenia and Metabolic PET-CT Parameters on Survival in Locally Advanced Non-Small Cell Lung Carcinoma.

The treatment of stage III non-small cell lung cancer (NSCLC) is complex. Here, we aimed to examine the prognostic utility of sarcopenia and metabolic muscle volumes and evaluate their relationship with oncological treatments in patients with locally advanced NSCLC. Patients with unresectable stage III NSCLC were evaluated retrospectively. Muscle fields were measured, and metabolic parameters of the psoas were obtained. The skeletal muscle index (SMI), sarcopenia, sarcopenic obesity, and body mass index (BMI)-associated sarcopenia were evaluated. Fifty-three (94.6%) patients were men, and three (5.4%) were women. Sarcopenia was identified in 36 (64.3%) patients. Pretreatment sarcopenia and BMI-associated sarcopenia negatively affected overall survival (p = 0.040 and 0.023, respectively). A high psoas SUVmean (Standardized Uptake Value mean) and low mean psoas HU (Hounsfield unit) were poor prognostic factors (p = 0.009 and 0.014, respectively). SMI and muscle mass decreased after oncological treatment. Advanced age, inability to complete treatment, administration of chemoradiotherapy after chemotherapy, presence of sarcopenia, and a low mean psoas HU decreased survival. In conclusion, sarcopenia and BMI-associated sarcopenia are poor prognostic factors in patients with lung cancer. Oncological treatments can adversely affect muscle mass. The metabolic parameters of the psoas muscle can predict patient prognosis.

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets.

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

ERS International Virtual Congress 2021: Highlights from the Turkish Thoracic Society Early Career Members.

This review aimed to highlight some important points derived from the presentations of the European Respiratory Society 2021 Virtual International Congress by a committee formed by the Early Career Task Group of the Turkish Thoracic Society. We summarized a wide range of topics including current developments of respiratory diseases and provided an overview of important and striking topics of the congress. Our primary motivation was to give some up-to-date information and new developments discussed during congress especially for the pulmonologists who did not have a chance to follow the congress. This review also committed an opportunity to get an overview of the newest data in the diverse fields of respiratory medicine such as post-coronavirus disease 2019, some new interventional and technologic developments related to respiratory health, and new treatment strategies.

Evaluation of spinal-paraspinal parameters to determine segmentation of the vertebrae.

PURPOSE: We aimed to evaluate whether lumbar vertebrae can be correctly numbered using auxiliary parameters. MATERIAL AND METHODS: Vertebra corpus shape, O'Driscoll classification, lumbosacral axis angle, last two square vertebra dimensions, orifice of right renal artery (RRA), orifice of celiac truncus (CT), orifice of superior mesenteric artery (SMA), vena cava inferior confluence (CVC), abdominal aorta bifurcation (AB), and iliolumbar ligament were evaluated in this study. RESULTS: Lumbosacral transitional vertebrae (LSTV) were observed in 13 (9%) patients. The most common locations of the paraspinal parameters were: RRA: L1 vertebrae (45%), SMA: L1 vertebrae (66%), CT: T12 vertebrae (46%), AB: L4 vertebrae (63%), and CVC: L4 vertebrae (52%). CONCLUSIONS: According to the results of our study, no single parameter in the magnetic resonance imaging can accurately indicate the number of vertebrae without counting the levels. As a result, we believe that these parameters may be suspicious in terms of the presence of LSTV rather than the correct level.

Butterfly adrenal gland with maldevelopment of the mesonephric duct: A rare association in an adult patient.

Adrenal gland disorders can be asymptomatic and detected incidentally via imaging techniques such as ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. Fusion anomaly is a condition that can be attributed to errors in the developmental process and may be detected via these imaging modalities. We present a case of butterfly adrenal gland in a 61-year-old man with CT and magnetic resonance images. In our patient, this anomaly is also accompanied by unilateral renal agenesis and a diaphragmatic defect. Positron emission tomography-CT, contrast-enhanced CT, and magnetic resonance images are presented. To the best of our knowledge, this is the second case in which coexistence of unilateral renal agenesis and butterfly adrenal gland anomaly in an adult patient has been documented.