Background Coronavirus related respiratory illness usually manifests clinically as pneumonia with predominant imaging findings of an atypical or organizing pneumonia. Plain radiography is very helpful for COVID-19 disease assessment and follow-up. It gives an accurate insight into the disease course. We aimed to determine the COVID-19 disease course and severity using chest X-ray (CXR) scoring system and correlate these with patients’ age, sex, and outcome. Results In our study, there were 350 patients proven with positive COVID-19 disease; 220 patients (62.9%) had abnormal baseline CXR and 130 patients (37.1%) had normal baseline CXR. During follow-up chest X-ray studies, 48 patients (13.7%) of the normal baseline CXR showed CXR abnormalities. In abnormal chest X-ray, consolidation opacities were the most common finding seen in 218 patients (81.3%), followed by reticular interstitial thickening seen in 107 patients (39.9%) and GGO seen in 87 patients (32.5%). Pulmonary nodules were found 25 patients (9.3%) and pleural effusion was seen in 20 patients (7.5%). Most of the patients showed bilateral lung affection (181 patients, 67.5%) with peripheral distribution (156 patients, 58.2%) and lower zone affection (196 patients, 73.1%). The total severity score was estimated in the baseline and follow-up CXR and it was ranged from 0 to 8. The outcome of COVID-19 disease was significantly related to the age, sex, and TSS of the patients. Male patients showed significantly higher mortality rate as compared to the female patients (P value 0.025). Also, the mortality rate was higher in patients older than 40 years especially with higher TSS. Conclusion Radiographic findings are very good predictors for assessing the course of COVID-19 disease and it could be used as long-term consequences monitoring.
Background Coronavirus disease has spread widely all over the world since the beginning of 2020, and this required rapid adequate management. High-resolution computed tomography (HRCT) has become an initial valuable tool for screening, diagnosis, and assessment of disease severity. This study aimed to assess the clinical, radiographic, and laboratory findings of COVID-19 with HRCT follow-up in discharged patients to predict lung fibrosis after COVID-19 infection in survived patients. Results This study included two-hundred and ten patients who were tested positive for the novel coronavirus by nasopharyngeal swap, admitted to the hospital, and discharged after recovery. Patients with at least a one-time chest CT scan after discharge were enrolled. According to the presence of fibrosis on follow-up CT after discharge, patients were classified into two groups and assigned as the “non-fibrotic group” (without evident fibrosis) and “fibrotic group” (with evident fibrosis). We compared between these two groups based on the recorded clinical data, patient demographic information (i.e., sex and age), length of stay (LOS) in the hospital, admission to the ICU, laboratory results (peak C-reactive protein [CRP] level, lowest lymphocyte level, serum ferritin, high-sensitivity troponin, d-dimer, administration of steroid), and CT features (CT severity score and CT consolidation/crazy-paving score). CT score includes the CT during the hospital stay with peak opacification and follow-up CT after discharge. The average CT follow-up time after discharge is 41.5 days (range, 20 to 65 days). There was a statistically significant difference between both groups (p ˂0.001). Further, a multivariate analysis was performed and found that the age of the patients, initial CT severity score, consolidation/crazy-paving score, and ICU admission were independent risk factors associated with the presence of post-COVID-19 fibrosis (p<0.05). Chest CT severity score shows a sensitivity of 86.1%, a specificity of 78%, and an accuracy of 81.9% at a cutoff point of 10.5. Conclusion The residual pulmonary fibrosis in COVID-19 survivors after discharge depends on many factors with the patient’s age, CT severity, consolidation/crazy-paving scores, and ICU admission as independent risk factors associated with the presence of post-COVID-19 fibrosis.
Background: Coronavirus (COVID-19) pneumonia emerged in Wuhan, China, in December 2019. It was highly contagious spreading all over the world, with a rapid increase in the number of deaths. COVID-19 is characterized by fever, fatigue, dry cough, and dyspnea with variable chest imaging features which have been detected. In our study, we shared our experience of CT findings in proven cases of COVID-19 to recognize the different CT patterns to help in proper and accurate diagnosis. Results: The most common CT features detected in COVID-19 cases were ground glass patches (93.3%) followed by subpleural linear abnormality (53.3%), air bronchogram (23.3%), and consolidation patches (23.3%), as well as bronchial wall thickening (16.7%), crazy paving pattern (13.3%), and discrete nodules surrounded by ground glass appearance (10%). Only one case had pleural effusion (3.3%). No cavitary lesions or specific lymph nodes were detected in any of the examined patients. The lung lesions showed typical diffuse, basal, and subpleural involvement with less affection of the upper lobes. Conclusion: CT imaging findings of COVID-19 can help in early and accurate diagnosis of COVID-19 and proper assessment of the severity of the disease.
Background Since the beginning of 2020, coronavirus disease has spread widely all over the world and this required rapid adequate management; therefore, continuous searching for rapid and sensitive CT chest techniques was needed to give a hand for the clinician. We aimed to assess the validity of computed tomography (CT) quantitative and qualitative analysis in COVID-19 pneumonia and how it can predict the disease severity on admission. Results One hundred and twenty patients were enrolled in our study, 98 (81.7%) of them were males, and 22 (18.3%) of them were females with a mean age of 52.63 ± 12.79 years old, ranging from 28 to 83 years. Groups B and C showed significantly increased number of involved lung segments and lobes, frequencies of consolidation, crazy-paving pattern, and air bronchogram. The total lung severity score and the total score for crazy-paving and consolidation are used as severity indicators in the qualitative method and could differentiate between groups B and C and group A (90.9% sensitivity, 87.5% specificity, and 93.2% sensitivity, 87.5% specificity, respectively), while the quantitative indicators could differentiate these three groups. Using the quantitative CT indicators, the validity to differentiate different groups showed 84.1% sensitivity and 81.2% specificity for the opacity score, and 90.9% sensitivity and 81.2% specificity for the percentage of high opacity. Conclusion Advances in CT COVID-19 pneumonia assessment provide an accurate and rapid tool for severity assessment, helping for decision-making notably for the critical cases.
Background: Breast cancer is the most common cancer in women worldwide. It is responsible for about 23% of cancer in females in both developed and developing countries [1]. We aimed to assess the accuracy of contrastenhanced spectral mammography (CESM) versus contrast-enhanced breast MRI in the evaluation of BIRADS 4 breast lesions. Results: Fifty patients were included in this study; there were 28 malignant cases and 22 benign cases; all cases were proved by histopathological result either by core biopsy or excision biopsy. CESM was found to have less sensitivity (94.1%) than MRI (100%) but CESM has higher specificity (100%) than MRI (95.5%). The accuracy of CESM was 96.4%, while the accuracy of MRI was 98.2% with no statistical significance (P value 0.827). Conclusion: CESM can be used as a sensitive diagnostic tool in the detection and staging of breast cancer with higher specificity and less sensitivity as compared to contrast enhanced breast MRI.
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