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Background:The aim of this study was to systematically evaluate the prognostic role of pretreatment lactate dehydrogenase (LDH) concentration for survival in patients with lung cancer through performing a meta-analysis.Methods:PubMed, EMBASE, Cochrane Library, Web of Science, and China National Knowledge Infrastructure were searched for potentially relevant literature. The study and patients’ characteristics were extracted. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were pooled to estimate the prognostic role of LDH in patients with lung cancer.Results:Fourteen studies with 4084 patients were included. Higher pretreatment LDH concentration was significantly associated with an increased risk of overall mortality in patients with lung cancer (HR = 1.49, 95% CI, 1.38–1.59). Subgroup analysis of studies also resulted in a significantly increased risk of mortality in patients with small cell lung cancer (SCLC, HR = 1.54, 95% CI, 1.43–1.67) or nonsmall cell lung cancer (NSCLC, HR = 1.25, 95% CI, 1.06–1.46), with high pretreatment LDH concentration. No significant between-study heterogeneity was observed (I2 = 12.0%, P = .321). No significant publication bias was found (P = .352) in the meta-analysis.Conclusion:The results suggested that higher pretreatment LDH concentration was associated with worse overall survival in patients with lung cancer. The findings may assist future research on anticancer therapy by targeting LDH and help predict prognosis in lung cancer patients. However, high-quality studies are required to further research and support these associations. Moreover, confounding factors such as patient ethnicity and tumor type should be considered in future studies.

Background:To perform a meta-analysis of retrospective studies exploring the association of C-reactive protein to albumin (CAR) with overall survival (OS) in patients with lung cancer.Methods:Relevant studies were enrolled by searching databases of PubMed, Cochrane Library, Web of Science, and Embase were searched until July 16, 2017. We combined the hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the correlation between CAR and OS in patients with lung cancerResults:Four studies involving 1257 participants from several countries were involved in the meta-analysis. In a pooled analysis of all studies, elevated CAR predicted poor OS (HR: 2.13; 95% CI: 1.52–2.97; P < .05). Subgroup analysis showed that high level of CAR predicted poor OS in patients with lung cancer though multivariate analyses on 1092 participants (HR: 1.63; 95% CI: 1.24–2.51; P < .001) and the heterogeneity decreased to 45.4%. Moreover, a similar trend was observed in patients receiving surgery (HR: 2.64; 95% CI: 2.08–3.35; P < .001) and chemotherapy (HR: 1.75; 95% CI: 1.93–2.57; P = .004). And the HRs for patients receiving surgery was moderately higher than that for patients receiving chemotherapy.Conclusion:Our findings indicate that CAR may have a prognostic value in lung cancer as we detected a significant association between elevated CAR and poorer OS. However, further studies are warranted to draw firm conclusions.

Background The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients. Methods Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared. Results Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P <  0·0001), and to have more frequent lymphopenia (79·2% vs 43·7%, P = 0·003) and eosinopenia (84·2% vs 57·0%, P = 0·046). Conclusions The symptoms of patients in west China were relatively mild, and an appreciable proportion of infected cases had no fever, warranting special attention.

Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. Methods: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). Results: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. Conclusions: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.