Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study

CT Differentiation and Prognostic Modeling in COVID-19 and Influenza A Pneumonia

RATIONALE AND OBJECTIVES

This study aimed to compare CT features of COVID-19 and Influenza A pneumonia, develop a diagnostic differential model, and explore a prognostic model for lesion resolution.

MATERIALS AND METHODS

A total of 446 patients diagnosed with COVID-19 and 80 with Influenza A pneumonitis underwent baseline chest CT evaluation. Logistic regression analysis was conducted after multivariate analysis and the results were presented as nomograms. Machine learning models were also evaluated for their diagnostic performance. Prognostic factors for lesion resolution were analyzed using Cox regression after excluding patients who were lost to follow-up, with a nomogram being created.

RESULTS

COVID-19 patients showed more features such as thickening of bronchovascular bundles, crazy paving sign and traction bronchiectasis. Influenza A patients exhibited more features such as consolidation, coarse banding and pleural effusion (P < 0.05). The logistic regression model achieved AUC values of 0.937 (training) and 0.931 (validation). Machine learning models exhibited area under the curve values ranging from 0.8486 to 0.9017. COVID-19 patients showed better lesion resolution. Independent prognostic factors for resolution at baseline included age, sex, lesion distribution, morphology, coarse banding, and widening of the main pulmonary artery.

CONCLUSION

Distinct imaging features can differentiate COVID-19 from Influenza A pneumonia. The logistic discriminative model and each machine - learning network model constructed in this study demonstrated efficacy. The nomogram for the logistic discriminative model exhibited high utility. Patients with COVID-19 may exhibit a better resolution of lesions. Certain baseline characteristics may act as independent prognostic factors for complete resolution of lesions.

Ultrasonic humidifier lung with a reversed halo sign: A case report

The reversed halo sign was initially reported as a representative computed tomography scan finding of cryptogenic organizing pneumonia. Since then, however, it has been reported in various diseases and is now considered a nonspecific finding. However, there are no cases of humidifier lung with the reversed halo sign. An 82-year-old Japanese male patient presented with moving difficulties 48 days after starting darolutamide treatment for prostate cancer. He was admitted to the hospital due to acute pneumonia, which presented as bilateral extensive nonsegmental ground-glass opacities in the peripheral regions and extensive areas of ground-glass opacity with a circumferential halo of consolidation, with the reversed halo sign on computed tomography scan. After darolutamide discontinuation with the concomitant administration of antibiotics, the patient's pneumonia improved, and he was discharged from the hospital. However, within a few days, he was again admitted to the hospital due to pneumonia. He was found to have been using an ultrasonic humidifier at home and was then diagnosed with humidifier lung based on the bronchoscopy and provocative testing findings. Hence, ultrasonic humidifier lung should be considered as a differential diagnosis in patients presenting with the reversed halo sign, and a detailed medical history must be taken.

Clinical importance of thoracal lymphadenopathy in COVID-19

BACKGROUND

Thoracal lymphadenopathy may predict prognosis in patients with coronavirus disease 2019 (COVID-19), albeit the reported data is inconclusive. The aim of the present analysis was to analyze the affected lymph node stations and the cumulative lymph node size derived from computed tomography (CT) for prediction of 30-day mortality in patients with COVID-19.

METHODS

The clinical database was retrospectively screened for patients with COVID-19 between 2020 and 2022. Overall, 177 patients (63 female, 35.6%) were included into the analysis. Thoracal lymphadenopathy was defined by short axis diameter above 10 mm. Cumulative lymph node size of the largest lymph nodes was calculated and the amount of affected lymph node stations was quantified.

RESULTS

Overall, 53 patients (29.9%) died within the 30-day observation period. 108 patients (61.0%) were admitted to the ICU and 91 patients needed to be intubated (51.4%). Overall, there were 130 patients with lymphadenopathy (73.4%). The mean number of affected lymph node levels were higher in non-survivors compared to survivors (mean, 4.0 vs 2.2, p < 0.001). The cumulative size was also higher in non-survivors compared to survivors (mean 55.9 mm versus 44.1 mm, p = 0.006). Presence of lymphadenopathy was associated with 30-day mortality in a multivariable analysis, OR = 2.99 (95% CI 1.20 - 7.43), p = 0.02.

CONCLUSIONS

Thoracal lymphadenopathy comprising cumulative size and affected levels derived from CT images is associated with 30-day mortality in patients with COVID-19. COVID-19 patients presenting with thoracic lymphadenopathy should be considered as a risk group.