Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases.

Objectives: If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer. Methods: Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models. Results: A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05). Conclusion: Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.

Comparison of Clinical Features and CT Temporal Changes Between Familial Clusters and Non-familial Patients With COVID-19 Pneumonia.

Purpose: This study aimed to compare the clinical characteristics, laboratory findings, and chest computed tomography (CT) findings of familial cluster (FC) and non-familial (NF) patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective study included 178 symptomatic adult patients with laboratory-confirmed COVID-19. The 178 patients were divided into FC (n = 108) and NF (n = 70) groups. Patients with at least two confirmed COVID-19 cases in their household were classified into the FC group. The clinical and laboratory features between the two groups were compared and so were the chest CT findings on-admission and end-hospitalization. Results: Compared with the NF group, the FC group had a longer period of exposure (13.1 vs. 8.9 days, p < 0.001), viral shedding (21.5 vs. 15.9 days, p < 0.001), and hospital stay (39.2 vs. 22.2 days, p < 0.001). The FC group showed a higher number of involved lung lobes on admission (3.0 vs. 2.3, p = 0.017) and at end-hospitalization (3.6 vs. 1.7, p < 0.001) as well as higher sum severity CT scores at end-hospitalization (4.6 vs. 2.7, p = 0.005) than did the NF group. Conversely, the FC group had a lower lymphocyte count level (p < 0.001) and a significantly lower difference in the number of involved lung lobes (Δnumber) between admission and discharge (p < 0.001). Notably, more cases of severe or critical illness were observed in the FC group than in the NF group (p = 0.036). Conclusions: Patients in the FC group had a worse clinical course and outcome than those in the NF group; thus, close monitoring during treatment and follow-ups after discharge would be beneficial for patients with familial infections.

Effect of heart rate on the diagnostic accuracy of 256-slice computed tomography angiography in the detection of coronary artery stenosis: ROC curve analysis.

The aim of the present study was to investigate the effect of heart rate (HR) on the diagnostic accuracy of 256-slice computed tomography angiography (CTA) in the detection of coronary artery stenosis. Coronary imaging was performed using a Philips 256-slice spiral CT, and receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic value of 256-slice CTA in coronary artery stenosis. The HR of the research subjects in the study was within a certain range (39-107 bpm). One hundred patients suspected of coronary heart disease underwent 256-slice CTA examination. The cases were divided into three groups: Low HR (HR <75 bpm), moderate HR (75≤ HR <90 bpm) and high HR (HR ≥90 bpm). For the three groups, two observers independently assessed the image quality for all coronary segments on a four-point ordinal scale. An image quality of grades 1-3 was considered diagnostic, while grade 4 was non-diagnostic. A total of 97.76% of the images were diagnostic in the low-HR group, 96.86% in the moderate-HR group and 95.80% in the high-HR group. According to the ROC curve analysis, the specificity of CTA in diagnosing coronary artery stenosis was 98.40, 96.00 and 97.60% in the low-, moderate- and high-HR groups, respectively. In conclusion, 256-slice coronary CTA can be used to clearly show the main segments of the coronary artery and to effectively diagnose coronary artery stenosis. Within the range of HRs investigated, HR was found to have no significant effect on the diagnostic accuracy of 256-slice coronary CTA for coronary artery stenosis.