Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia

Background The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients. Results All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001). Conclusion Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.

The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia

Objective: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test. Methods: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort. Results: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or “COVID-19 without virus detection”, as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%. Conclusion: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making. Advances in knowledge: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.

Chest CT findings and experience in 100 COVID19 patients in Mosul city, Iraq

Introduction: Due to lack of PCR kits in our area, as well as the extensive dissemination and peaking of COVID-19 since March 2020, our knowledge as radiologists has become increasingly relevant for recognizing CT patterns in order to diagnose and isolate COVID-19-infected patients. In 100 instances, the investigation began with the most prevalent CT chest abnormalities and the CT severity score index in relation to sex. The goal of this study is to better diagnose COVID-19-related lung injuries, enhance the diagnostic accuracy of chest CT scans, and track disease development in Mosul City. Materials and Methods: From June 2020 to January 2021, one hundred patients were enrolled in this cross-sectional study in Mosul, with 71 males (71%) and 29 females (29%) ranging in age from 15 to 85 years, mean SD (53.2317.80). Non contrast chest CT were done as part of investigation tool on patients were suspected COVID-19 infection. Results: A radiologist gathered data between 4 and 10 days after the onset of symptoms and evaluated it for lesion pattern, location, and severity. The commonest CT changes (ground glass opacity 55.23%, consolidation 17.44%, broncho vascular thickening 9.88%, crazy paving 5.81% and tree in bud 5.23%) were seen, along with less common pattern (bronchiectasis 1.74%, nodules 2.33%, reversed halo sign and pleural effusion 1.17%), and no lymphadenopathy were seen. Multilobe involvement was detected in 52/100 instances (68.92%), while peripheral affection was seen in 52/100 cases (65%). The higher CT severity score 4 and 5 with male gender were found to have a significant link (P value 0.002). Conclusion: CT pulmonary are useful as a physician's helper for management and as an excellent predictor of disease severity and patient outcome. In patients with COVID-19 positive infection, the CT scan severity score is highly linked to laboratory findings, hospital stay, and oxygen demands.