Archivos De Bronconeumologia最新文献

Objectives: Little is known about the effect of bronchoscopic lung volume reduction using endobronchial valves (BLVR-EBV) on extrapulmonary manifestations like body composition, muscle function or metabolism. Pulmonary rehabilitation (PR) clearly addresses extrapulmonary manifestations of COPD, including physical inactivity and low muscle mass. However, the added impact of BLVR-EBV+PR remains unknown. Therefore, this study aimed to assess the effect of BLVR-EBV on body composition, muscle function and metabolic markers and whether PR has an additional impact on these outcomes.

Methods: Subjects with severe COPD eligible for both PR and BLVR-EBV were randomized into three groups: PR+BLVR-EBV, BLVR-EBV+PR, or only BLVR-EBV (n=97). Assessments included Dual Energy X-ray Absorptiometry, thigh muscle Computed Tomography, muscle strength measurements, accelerometry, and plasma (leptin, adiponectin, insulin, and triglycerides) at baseline and six months after the last intervention.

Results: A total of 74 participants completed the study. At follow-up, there were significant increases in the groups combined and both groups separated in total weight, lean mass, fat mass, muscle strength, daily physical activity, and triglyceride levels while leptin/fat mass ratio levels were significantly reduced. No differences were found between groups who underwent BLVR-EVR alone or BLVR-EBV with PR.

Conclusions: BLVR-EBV results in significant increases in body weight, lean and fat mass, muscle strength and daily physical activity level, and impacts on adipokine profile, irrespective of PR. This underscores the systemic benefits of addressing lung hyperinflation in patients with severe COPD.

Objectives: Expanding TNM staging system for lung cancer with the addition of new prognostic factors could enhance patient stratification and survival prediction. The goal of this study is to assess if TNM prognosis capacity could be improved by incorporating other pathological characteristics of surgical specimen.

Methods: We retrospectively reviewed lung cancer resections, stages I-II, performed between January 1st 2010 and May 1st 2019. We collected clinical variables and pathological characteristics, including vascular, lymphovascular and perineural invasion, STAS, necrosis and stromal features. Mortality and recurrence-free survival were assessed with univariable and multivariable Cox analysis. We explored how these factors would modify the TNM Harrel's index.

Results: 629 tumors were analyzed. Median overall survival was 53.9 months. Median recurrence-free survival was 47.6 months. Specific survival at 3, 5 and 10 years was 90, 83 and 74%. Recurrence-free survival at 3, 5 and 10 years was 76, 70 and 65%. The multivariable analysis showed that overall survival was significantly related to TNM classification (p<0.0002), vascular infiltration (HR 1.93, CI 1.42-2.64, p<0.0001), lymphovascular invasion (HR 1.88, CI 1.30-2.71, p<0.0015) and necrosis (HR 1.74, CI 1.24-2.45, p<0.0025). Harrell's index for TNM was 0.6139. Adding vascular, lymphovascular invasion and necrosis, it increased up to 0.6531. The multivariable analysis showed that specific survival was significantly related to TNM classification (p<0.001), vascular infiltration (HR 2.23, CI 1.44-3.46, p<0.001) and lymphovascular invasion (HR 1.85, CI 1.09-3.13, p<0.021). Harrell's index for TNM was 0.6645. Adding vascular and lymphovascular invasion, it increased up to 0.7103. Recurrence-free survival was related to TNM, vascular infiltration (HR 1.48, CI 1.05-2.09, p<0.023) and lymphovascular invasion (HR 2.40, CI 1.64-3.50, p<0.001). Harrell's index for TNM was 0.6264. Adding vascular and lymphovascular invasion, it increased up to 0.6794.

Conclusions: Including vascular and angiolymphatic invasion in the staging system classification could better stratify patients at risk of recurrence and tumor-related death.

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans. Additionally, the baseline LDCT examination provides valuable information about smoking-related comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease, and interstitial lung disease (ILD), by identifying relevant markers. Notably, these comorbidities, despite the slow progression of their markers, collectively exceed LC as ultimate causes of death at follow-up in LC screening participants. Computer-assisted diagnosis tools currently improve the reproducibility of radiologic readings and reduce the false negative rate of LDCT. Deep learning (DL) tools that analyze the radiomic features of lung nodules are being developed to distinguish between benign and malignant nodules. Furthermore, AI tools can predict the risk of LC in the years following a baseline LDCT. AI tools that analyze baseline LDCT examinations can also compute the risk of cardiovascular disease or death, paving the way for personalized screening interventions. Additionally, DL tools are available for assessing osteoporosis and ILD, which helps refine the individual's current and future health profile. The primary obstacles to AI integration into the LDCT screening pathway are the generalizability of performance and the explainability.