BACKGROUND. Current clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests. METHOD. We developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients’ plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules. RESULTS. A 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve–AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones. CONCLUSION. This study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions. FUNDING. The National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.

Background: Lymph node (LN) dissection is a common procedure for non-small cell lung cancer (NSCLC) to ascertain disease severity and treatment options. However, murine studies have indicated that excising tumor-draining LNs diminished immunotherapy effectiveness, though its applicability to clinical patients remains uncertain. Hence, the authors aim to illustrate the immunological implications of LN dissection by analyzing the impact of dissected LN (DLN) count on immunotherapy efficacy, and to propose a novel ‘immunotherapy-driven’ LN dissection strategy. Materials and methods: The authors conducted a retrospective analysis of NSCLC patients underwent anti-PD-1 immunotherapy for recurrence between 2018 and 2020, assessing outcomes based on DLN count stratification. Results: A total of 144 patients were included, of whom 59 had a DLN count less than or equal to 16 (median, IQR: 11, 7–13); 66 had a DLN count greater than 16 (median, IQR: 23, 19–29). With a median follow-up time of 14.3 months (95% CI: 11.0–17.6), the overall median progression-free survival (PFS) was 7.9 (95% CI: 4.1–11.7) months, 11.7 (95% CI: 7.9–15.6) months in the combination therapy subgroup, and 4.8 (95% CI: 3.1–6.4) months in the immunotherapy alone subgroup, respectively. In multivariable Cox analysis, DLN count less than or equal to 16 is associated with an improved PFS in all cohorts [primary cohort: HR=0.26 (95% CI: 0.07–0.89), P=0.03]; [validation cohort: HR=0.46 (95% CI: 0.22–0.96), P=0.04]; [entire cohort: HR=0.53 (95% CI: 0.32–0.89), P=0.02]. The prognostic benefit of DLN count less than or equal to 16 was more significant in immunotherapy alone, no adjuvant treatment, pN1, female, and squamous carcinoma subgroups. A higher level of CD8+ central memory T cell (Tcm) within LNs was associated with improved PFS (HR: 0.235, 95% CI: 0.065–0.845, P=0.027). Conclusions: An elevated DLN count (cutoff: 16) was associated with poorer immunotherapy efficacy in recurrent NSCLC, especially pronounced in the immunotherapy alone subgroup. CD8+Tcm proportions in LNs may also impact immunotherapy efficacy. Therefore, for patients planned for adjuvant immunotherapy, a precise rather than expanded lymphadenectomy strategy to preserve immune-depending LNs is recommended.

Objective: To evaluate the spectrum of comorbidities and its impact on the clinical outcome in patients with coronavirus disease 2019 (COVID-19). Design: Retrospective case studies Setting: 575 hospitals in 31 province/autonomous regions/provincial municipalities across China Participants: 1,590 laboratory-confirmed hospitalized patients. Data were collected from November 21 ^st , 2019 to January 31 ^st , 2020. Main outcomes and measures: Epidemiological and clinical variables (in particular, comorbidities) were extracted from medical charts. The disease severity was categorized based on the American Thoracic Society guidelines for community-acquired pneumonia. The primary endpoint was the composite endpoints, which consisted of the admission to intensive care unit (ICU), or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared among patients with COVID-19 according to the presence and number of comorbidities. Results: Of the 1,590 cases, the mean age was 48.9 years. 686 patients (42.7%) were females. 647 (40.7%) patients were managed inside Hubei province, and 1,334 (83.9%) patients had a contact history of Wuhan city. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. 269 (16.9%), 59 (3.7%), 30 (1.9%), 130 (8.2%), 28 (1.8%), 24 (1.5%), 21 (1.3%), 18 (1.1%) and 3 (0.2%) patients reported having hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency, respectively. 130 (8.2%) patients reported having two or more comorbidities. Patients with two or more comorbidities had significantly escalated risks of reaching to the composite endpoint compared with those who had a single comorbidity, and even more so as compared with those without (all P <0.05). After adjusting for age and smoking status, patients with COPD (HR 2.681, 95%CI 1.424-5.048), diabetes (HR 1.59, 95%CI 1.03-2.45), hypertension (HR 1.58, 95%CI 1.07-2.32) and malignancy (HR 3.50, 95%CI 1.60-7.64) were more likely to reach to the composite endpoints than those without. As compared with patients without comorbidity, the HR (95%CI) was 1.79 (95%CI 1.16-2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61-4.17) among patients with two or more comorbidities. Conclusion: Comorbidities are present in around one fourth of patients with COVID-19 in China, and predispose to poorer clinical outcomes. Highlights: What is already known on this topic: - Since November 2019, the rapid outbreak of coronavirus disease 2019 (COVID-19) has recently become a public health emergency of international concern. There have been 79,331 laboratory-confirmed cases and 2,595 deaths globally as of February 25 ^th , 2020 - Previous studies have demonstrated the association between comorbidities and other severe acute respiratory diseases including SARS and MERS. - No study with a nationwide representative cohort has demonstrated the spectrum of comorbidities and the impact of comorbidities on the clinical outcomes in patients with COVID-19. What this study adds: - In this nationwide study with 1,590 patients with COVID-19, comorbidities were identified in 399 patients. Comorbidities of COVID-19 mainly included hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency. - The presence of as well as the number of comorbidities predicted the poor clinical outcomes (admission to intensive care unit, invasive ventilation, or death) of COVID-19. - Comorbidities should be taken into account when estimating the clinical outcomes of patients with COVID-19 on hospital admission.

Although the effectiveness of lung cancer screening by low-dose computed tomography (LDCT) could be shown in China, there could be variation in the evidence concerning the economic impact. Our study explores the cost-effectiveness of lung cancer screening and optimizes the best definition of a high-risk population. A Markov model consisting of the natural history and post-diagnosis states was constructed to estimate the costs and quality-adjusted life years (QALYs) of LDCT screening compared with no screening. A total of 36 distinct risk factor-based screening strategies were assessed by incorporating starting ages of 40, 45, 50, 55, 60 and 65 years, stopping ages of 69, 74 and 79 years as well as smoking eligibility criteria. Screening data came from community-based mass screening with LDCT for lung cancer in Guangzhou. Compared with no screening, all screening scenarios led to incremental costs and QALYs. When the willingness-to-pay (WTP) threshold was USD37,653, three times the gross domestic product (GDP) per capita in China, six of nine strategies on the efficiency frontier may be cost-effective. Annual screening between 55 and 79 years of age for those who smoked more than 20 pack-years, which yielded an incremental cost-effectiveness ratio (ICER) of USD35,000.00 per QALY gained, was considered optimal. In sensitivity analyses, the result was stable in most cases. The trends of the results are roughly the same in scenario analyses. According to the WTP threshold of different regions, the optimal screening strategies were annual screening for those who smoked more than 20 pack-years, between 50 and 79 years of age in Zhejiang province, 55–79 years in Guangdong province and 65–74 years in Yunnan province. However, annual screening was unlikely to be cost-effective in Heilongjiang province under our modelling assumptions, indicating that tailored screening policies should be made regionally according to the local epidemiological and economic situation.

Objectives: This study aims to explore how pre-existing conditions such as blood types, family history of cancer and comorbid diseases correlate with the genetic and programmed death-ligand 1 (PD-L1) expression that contributes to the heterogeneous biological behaviours of non-small cell lung cancer (NSCLC). Materials and methods: A cohort of 5507 NSCLC patients who underwent surgical resection between January 2014 and July 2018 was studied. Targeted next-generation sequencing was used to detect mutations in nine pivotal cancer-related genes, and immunohistochemical staining was applied to assess PD-L1 expression. Logistic regression analysis was employed to identify significant correlations. Results: All patients underwent NGS, with 1839 were also evaluated for PD-L1 expression. Several significant findings were found: ROS1 mutations were closely associated with a family history of lung cancer (OR 7.499, 95% CI 1.094 to 30.940, p=0.013). Epidermal growth factor receptor (EGFR) L858R mutations were common among patients with a family history of non-lung cancers and those with hypertension (OR 2.089, 95% CI 1.029 to 4.135, p=0.037 and OR 1.252, 95% CI 1.001 to 1.562, p=0.048, respectively). Pre-existing conditions such as diabetes and hepatitis B surface antigen positivity (OR 1.468, 95% CI 1.042 to 2.047, p=0.026 and OR 1.373, 95% CI 1.012 to 1.847, p=0.038, respectively) were correlated with EGFR exon 19 deletions. RhD negativity showed potential ties to BRAF mutations (OR 0.010, 95% CI 0.001 to 0.252, p=0.001). A history of tuberculosis linked to increased PD-L1 expression in immune cells (OR 3.597, 95% CI 1.295 to 14.957, p=0.034). Conclusion: This large-scale, cross-sectional study reveals a complex interplay between genetic mutations, immunological features and pre-existing conditions in NSCLC patients, offering insights that could inform personalised treatment strategies.