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Objective Lung segmentation using volumetric quantitative computed tomography (CT) analysis may help predict outcomes of patients with coronavirus disease (COVID-19). The aim of this study was to investigate the relationship between CT volumetric quantitative analysis and prognosis in patients with COVID-19. Materials and Methods CT images from patients diagnosed with COVID-19 from February 18 to April 15, 2020 were retrospectively analyzed. CT with a negative finding, failure of quantitative analysis, or poor image quality was excluded. CT volumetric quantitative analysis was performed by automated volumetric methods. Patients were stratified into two risk groups according to CURB-65: mild (score of 0–1) and severe (2–5) pneumonia. Outcomes were evaluated according to the critical event-free survival (CEFS). The critical events were defined as mechanical ventilator care, ICU admission, or death. Multivariable Cox proportional hazards analyses were used to evaluate the relationship between the variables and prognosis. Results Eighty-two patients (mean age, 63.1 ± 14.5 years; 42 females) were included. In the total cohort, male sex (hazard ratio [HR], 9.264; 95% confidence interval [CI], 2.021–42.457; p = 0.004), C-reactive protein (CRP) (HR, 1.080 per mg/dL; 95% CI, 1.010–1.156; p = 0.025), and COVID-affected lung proportion (CALP) (HR, 1.067 per percentage; 95% CI, 1.033–1.101; p < 0.001) were significantly associated with CEFS. CRP (HR, 1.164 per mg/dL; 95% CI, 1.006–1.347; p = 0.041) was independently associated with CEFS in the mild pneumonia group (n = 54). Normally aerated lung proportion (NALP) (HR, 0.872 per percentage; 95% CI, 0.794–0.957; p = 0.004) and NALP volume (NALPV) (HR, 1.002 per mL; 95% CI, 1.000–1.004; p = 0.019) were associated with a lower risk of critical events in the severe pneumonia group (n = 28). Conclusion CRP in the mild pneumonia group; NALP and NALPV in the severe pneumonia group; and sex, CRP, and CALP in the total cohort were independently associated with CEFS in patients with COVID-19.

Compared to lung abscess, focal necrotizing pneumonia occurs more commonly in non-gravity-dependent segments with lower incidence of risk factors for aspiration. Similar to lung abscess, the rate of success for treatment of focal necrotizing pneumonia was high.

Objectives: Chemotherapy increases the risk of thromboembolism in patients with cancer. Although thrombocytopenia is a known side effect of chemotherapy, reactive thrombocytosis related to chemotherapy is uncommonly reported. The present study aimed to determine the incidence of gemcitabine-related thrombocytosis and the associated risk of thromboembolism. Methods: Medical records of 250 consecutive patients with a malignant disease who received gemcitabine-based therapy were reviewed. A multivariate analysis was done to determine factors associated with thromboembolism. Results: A total of 220 eligible patients with a median age of 63 years (range 26–83) were identified. Of these 220 patients, 95% had advanced malignancy and 59% had received prior chemotherapy. A total of 69% of patients received a platinum combination. In all, 46% patients experienced thrombocytosis following chemotherapy, with a median platelet count of 632 × 10⁹/l (range 457–1,385). Twenty-three of the 220 patients experienced a vascular event within 6 weeks of treatment. Eleven patients with thrombocytosis experienced a vascular event compared with 10 patients without thrombocytosis (not significant). On multivariate analysis, leukocytosis (odds ratio 5.8, 95% confidence interval 2.1–15.8) and comorbid illnesses (odds ratio 4.1, 95% confidence interval 1.4–12.6) were correlated with thromboembolism. Conclusions: Although gemcitabine-based therapy has been associated with an increased incidence of thrombocytosis, it does not increase the risk of thromboembolism in cancer patients. Leukocytosis and comorbid illnesses do increase the risk of thromboembolism.

Background Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.

Background: Data regarding pleural effusion due to pulmonary embolism (PE) are limited. Objectives: The aim of this study was to investigate the clinical characteristics of PE patients with pleural effusion caused by PE. Methods: Patients with PE were retrospectively analyzed and divided into 2 groups based on computed tomography: a group with pleural effusion due to PE (effusion group) and a group without pleural effusion (control group). Clinical characteristics were compared between the 2 groups. Results: The study population consisted of the effusion group (n = 127) and the control group (n = 651). Serum C-reactive protein (CRP) level was significantly higher in the effusion group than in the control group. The percentages of high-risk Simplified PE Severity Index (57 vs. 47%, p = 0.008), central PE (84 vs. 73%, p = 0.013), right ventricular dilation (45 vs. 36%, p = 0.053), and pulmonary infarction (40 vs. 8%, p < 0.001) were higher in the effusion group than in the control group. Multivariate analysis demonstrated that pulmonary infarction (odds ratio [OR] 6.20, 95% confidence interval [CI] 3.49-10.91, p < 0.001) and CRP level (OR 1.05, 95% CI 1.101-1.09, p = 0.023) were independent predictors of pleural effusion due to PE. The presence of pleural effusion was not a predictor of short-term outcomes or length of hospital stay. Conclusions: Patients with more severe PE are likely to have pleural effusion caused by PE. However, pleural effusion was not a proven predictor of short-term outcome or length of hospital stay. Pulmonary infarction and CRP levels were independent risk factors for the development of pleural effusion.

Conclusions: The addition of FDG PET/CT scanning to chest CT imaging provides better performance for predicting conversion to thoracotomy during VATS lobectomy in lung cancer patients. Therefore, in lung cancer patients undergoing surgical resection, FDG PET/CT can provide additional reliable information in selecting the appropriate surgical approach for a lobectomy.