Prevalence Of Pulmonary Nodules Research Articles

Associations of long-term exposure to air pollution with prevalence of pulmonary nodules: A cross-sectional study in Shijiazhuang, China

A complete understanding of the associations of ambient air pollution with prevalence of pulmonary nodule is lacking. We aimed to investigate the associations of ambient air pollutants with prevalence of pulmonary nodule. A total of 9991 health examination participants was enrolled and 3166 was elected in the final in Shijiazhuang between April 1st, 2018, and December 31st, 2018. 107 participants were diagnosed in pulmonary nodule while 3059 participants were diagnosed in non-pulmonary (named control). The individual exposure of participants was evaluation by Empirical Bayesian Kriging model according to their residential or work addresses. The pulmonary nodules were found and diagnosed by health examination through chest x-ray detection. Our results suggested that there were positive associations between prevalence of pulmonary nodules and PM2.5 (OR = 1.06, 95% CI: 1.02, 1.11) as well as O3 (OR = 1.49, 95% CI: 1.35, 1.66) levels. The platelet count (PLT) acted as the mediator of pulmonary nodules related with the PM2.5 exposure, while the neutrophil-to-lymphocyte ratio (NLR) as well as platelet-to-lymphocyte ratio (PLR) were the mediators of pulmonary nodules related with the O3 exposure. This study suggests that long-term exposure to PM2.5 and O3 may significantly associated with prevalence of pulmonary nodules, and the above associations are mediated by PLT, NLR and PLR.

Pulmonary Nodules in Juvenile Systemic Sclerosis: A Case-Series from the National Registry for Childhood Onset Scleroderma (NRCOS).

Juvenile systemic sclerosis (jSSc) is a systemic inflammatory and fibrotic autoimmune disease. Adult guidelines recommend obtaining a screening high-resolution computed tomography scan (CT) at diagnosis. As these recommendations are adopted as standard of care for jSSc, increased screening with CT may lead to increased detection of nodules. The implications of nodules identified in jSSc are unclear and unreported. A retrospective chart review was performed on the prospectively enrolled National Registry for Childhood-Onset Scleroderma (NRCOS) cohort over an enrollment period of 20 years. Clinical associations with presence of nodules and nodule characteristics were investigated. In this jSSc cohort, the prevalence of pulmonary nodules was 31% (n = 17 of 54). Nodule characteristics were heterogeneous, and most displayed stability over time. More participants with nodules had structural esophageal abnormalities, restriction, and reduced diffusing capacity on lung function tests, and follow-up imaging. Most participants had multiple nodules, and although most nodules were <5 mm, most participants had at least one nodule >5 mm. Pulmonary nodules are seen in children with jSSc and may be related to more severe disease and/or esophageal dysfunction. More work is needed to provide guidance on radiologic follow-up and clinical management of pulmonary nodules in jSSc.

Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China

PM2.5 is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the computed tomography scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5 exposure and pulmonary nodules was limited. To examine potential associations of exposures to PM2.5 and its major chemical constituents with the prevalence of pulmonary nodules. A total of 16 865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5 and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5 and its components on the risk of pulmonary nodules, respectively. Each 1 mg m−3 increase in PM2.5 (OR 1.011 (95% CI: 1.007–1.014)) was positively associated with pulmonary nodules. Among five PM2.5 components, in single-pollutant effect models, every 1 μg m−3 increase in organic matter (OM), black carbon (BC), and NO3 − elevated the risk of pulmonary nodule prevalence by 1.040 (95% CI: 1.025–1.055), 1.314 (95% CI: 1.209–1.407) and 1.021 (95% CI: 1.007–1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5 components was 1.076 (95% CI: 1.023–1.133) fold. Notably, NO3 − BC and OM contributed higher risks of pulmonary nodules than other PM2.5 components. And the NO3 − particles were identified to have the highest contribution. The impacts of PM2.5 components on pulmonary nodules were consistent across gender and age.These findings provide important evidence for the positive correlation between exposure to PM2.5 and pulmonary nodules in China and identify that NO3 − particles have the highest contribution to the risk.

A bi-directional deep learning architecture for lung nodule semantic segmentation.

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

Overcoming Obstacles in Pathological Diagnosis of Pulmonary Nodules through Circulating Tumor Cell Enrichment.

With the popularity of low-dose computed tomography (LDCT) in clinical examination of the lung, the prevalence of pulmonary nodules has significantly increased, thus significantly improving the early diagnosis of lung cancer, but also potentially contributing to overtreatment. This study aims to develop a noninvasive method to assist in diagnosing the pulmonary nodules. To do so, 3798 patients are recruited from the Department of Thoracic Surgery at Shanghai Pulmonary Hospital and peripheral blood samples are collected from them before surgery. From these samples, circulating tumor cells (CTC) are isolated using folate receptor (FR) positivity, and then enriched and analyzed in relation to cancer gene expression, stage, and level of invasion. The average CTC concentration of patients with lung disease is 11.97 functional unit (FU) in a 3 mL sample of blood. FR-positive CTC levels correlate with the expression of lung cancer driver genes tumor-node-matastasis (TNM) stage, and pleura invasion. The sensitivity of CTC levels to lung cancer diagnosis is 87.05%. Results from this study demonstrate that the determination of FR-positive CTC concentration is a convenient and time-saving strategy to improve the pathological diagnosis of pulmonary nodules.

Factors influencing the prevalence of pulmonary nodules in lung cancer screening trials: re-evaluation of a CT study

Numerous false positive findings are a problem in computed tomography based lung cancer screening trials. The potential patient-related variables that could predict the existence of such lung nodules were studied. 526 construction workers (age 38 - 81 yrs.) previously screened with spiral computed tomography were evaluated. Background features (age, body mass index, stature, sex, C-reactive protein, erythrocyte sedimentation rate, asbestos exposure and smoking history), reported symptoms (general condition, cough, mucous production, dyspnoea, chest pain) and findings in high resolution CT were related to the existence of nodules with logistic regression. There were 107 patients with one or more nodules recorded at least by two of the three observers. Advancing age (OR = 1.042/year, 95% CI = 1.000 - 1.085, p = 0.048) and dyspnoea symptom (p = 0.014) were positively associated with the existence of nodules, while smoking, asbestos exposure history and other studied parameters were not. Nodules ≤ 5 mm (50 patients) were inversely associated with the maximal thickness of pleural plaques (OR = 0.384, 95% CI = 0.169 - 0.873, p = 0.022). No variables helpful to sample subjects for lung cancer screening studies to reduce the number of false positive findings were found. Poor inspiration or associated pathology such as thick pleura may hamper nodule detection. Further studies should focus especially on the possible effect of computer tomography technology on finding nodules.