Purpose Although CT imaging features of Coronavirus Disease 2019 (COVID-19) pneumonia have already been published in the literature, there was little attention to distinctive imaging features encou...

To evaluate the computed tomography features of peripheral small cell lung cancer and non-small cell lung cancer and to establish a predictive model to conveniently distinguish between them. We retrospectively reviewed the computed tomography features of 51 patients with peripheral small cell lung cancer and 207 patients with peripheral non-small cell lung cancer after pathological diagnosis. Thirteen computed tomography morphologic findings were included and analyzed statistically. Meaningful features were analyzed by logistic regression for multivariate analysis. We then used β-coefficients as the basis to establish an image scoring prediction model. The meaningful morphologic features for distinguishing between peripheral small cell lung cancer and other tumor types are multinodular shape and lymphadenectasis, with scores of 12 and 11, respectively. The scores ranged from −51 to 23, and the most reasonable cut-off was −24. The available area under the curve was 0.834 (95% confidence interval [CI] 0.783–0.877). Sensitivity and specificity were 86.3% (95% CI 0.737–0.943) and 69.6% (95% CI 0.628–0.758), respectively. The image scoring predictive model that we constructed provides a simple and economical noninvasive method for distinguishing between peripheral small cell lung cancer and peripheral non-small cell lung cancer.

A pandemic named coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbroke in China since December 2019. This disease has caused more than 70000 deaths worldwide. We intend to analyze the risk factors of death and establish a prognosis nomogram for critical patients with COVID-19. We analyzed the clinical data of COVID-19 patients in Zhongnan Hospital of Wuhan University who were critical cases with COVID-19 before March 20, 2020. Data were collected at admission and compared between survivors and non-survivors and analyzed by univariable and multivariable logistic regression analysis. Finally, 104 patients were included, and 50 of whom died. Age [ OR 5.73 (95% CI, 1.14-28.81)], chest tightness [OR 5.50 (95%CI, 1.02-29.64)], AST [OR 6.57 (95%CI, 1.33-32.48)]and BUN [5.59 (95%CI, 1.05-29.74)] at admission were considered to predict the risk of death in critical patients and were selected to construct the nomogram. Subsequently, we established a nomogram model and validated it. The sensitivity and specificity of the nomogram were 96.0% and 74.1%, respectively. The AUC was 0.893 (95% CI, 0.807-0.980).

Background     We aimed to develop a new EGFR mutation–predictive scoring system to use in screening for EGFR-mutated lung adenocarcinomas (lacs). Methods     The study enrolled 279 patients with lac, including 121 patients with EGFR wild-type tumours and 158 with EGFR-mutated tumours. The Student t-test, chi-square test, or Fisher exact test was applied to discriminate clinical and computed tomography (ct) features between the two groups. Using a principal component analysis (pca) model, we derived predictive coefficients for the presence of EGFR mutation in lac. Results   The EGFR mutation–predictive score includes sex, smoking history, homogeneity, ground-glass opacity (ggo) on imaging, and the presence of pericardial effusion. The pca predictive model took this form: sex × 16 + smoking history × 15 + ggo × 12 + pericardial effusion × 10 + emphysema × 11. Model scores ranged from 79 to 147. The area under the receiver operating characteristic curve was 0.752 [95% confidence interval (ci): 0.697 to 0.801] in the lac population at the optimal cut-off value of 109, and the sensitivity and specificity were 68.4% (95% ci: 60.5% to 75.5%) and 74.4% (95% ci: 65.6% to 81.9%) respectively. Conclusions The EGFR mutation risk scoring system based on clinical data and ct features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.

Background: To improve the preoperative diagnostic accuracy and reduce the non-therapeutic thymectomy rate, we established a comprehensive predictive nomogram based on radiomics data and computed tomography (CT) features and further explored its potential use in clinical decision-making for anterior mediastinal masses (AMMs).Methods: A total of 280 patients, including 280 with unenhanced CT (UECT) and 241 with contrast-enhanced CT (CECT) scans, all of whom had undergone thymectomy for AMM with confirmed histopathology, were enrolled in this study. A total of 1,288 radiomics features were extracted from each labeled mass. The least absolute shrinkage and selection operator model was used to select the optimal radiomics features in the training set to construct the radscore. Multivariate logistic regression analysis was conducted to establish a combined clinical radiographic radscore model, and an individualized prediction nomogram was developed.Results: In the UECT dataset, radscore and the UECT ratio were selected for the nomogram. The combined model achieved higher accuracy (AUC: 0.870) than the clinical model (AUC: 0.752) for the prediction of therapeutic thymectomy probability. In the CECT dataset, the clinical and combined models achieved higher accuracy (AUC: 0.851 and 0.836, respectively) than the radscore model (AUC: 0.618) for the prediction of therapeutic thymectomy probability.Conclusions: In patients who underwent UECT only, a nomogram integrating the radscore and the UECT ratio achieved good accuracy in predicting therapeutic thymectomy in AMMs. However, the use of radiomics in patients with CECT scans did not improve prediction performance; therefore, a clinical model is recommended.