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Wideochirurgia I Inne Techniki... Dec 2023The diagnosis of pulmonary nodules (PNs) has traditionally relied on computed tomography (CT)-guided biopsy. To reduce radiation exposure, low-dose CT-guided PN biopsy...
INTRODUCTION
The diagnosis of pulmonary nodules (PNs) has traditionally relied on computed tomography (CT)-guided biopsy. To reduce radiation exposure, low-dose CT-guided PN biopsy has been employed.
AIM
This meta-analysis aimed at evaluating the efficacy and safety of low-dose CT-guided biopsy in the diagnosis of PNs.
MATERIAL AND METHODS
PubMed, Web of Science, and Wanfang were searched for relevant articles until June 2023. Comparing low-dose CT to normal-dose CT, we considered factors such as diagnostic yield, diagnostic accuracy, biopsy process time, dose-length product (DLP) value, the frequency of pneumothorax and pulmonary bleeding, and the frequency with which complications necessitated the placement of a chest tube.
RESULTS
This meta-analysis included data from a total of 6 investigations. There was a total of 459 patients who had a CT-guided PN biopsy performed at a low dosage, and 384 patients who had a normal-dose CT-guided PN biopsy. There were no statistically significant differences between the low-dose CT and normal-dose CT groups in terms of diagnostic accuracy (p = 0.08), diagnostic yield (p = 0.55), biopsy procedure duration (p = 0.30), pneumothorax (p = 0.61), pulmonary hemorrhage (p = 0.29), or complications requiring a chest tube (p = 0.48). Low-dose CT patients obtained a DLP that was 91% lower than those in the standard-dose CT group (p = 0.01). According to Egger's test, there is a significant possibility of publication bias in DLP (p = 0.034).
CONCLUSIONS
The diagnostic and safety results of low-dose CT-driven PN biopsy are equivalent to those of the standard one, although patients are much less exposed to radiation.
PubMed: 38239580
DOI: 10.5114/wiitm.2023.131563 -
Annals of Medicine Dec 2024Interstitial lung disease (ILD) is the most widespread and fatal pulmonary complication of rheumatoid arthritis (RA). Existing knowledge on the prevalence and risk... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Interstitial lung disease (ILD) is the most widespread and fatal pulmonary complication of rheumatoid arthritis (RA). Existing knowledge on the prevalence and risk factors of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is inconclusive. Therefore, we designed this review to address this gap.
MATERIALS AND METHODS
To find relevant observational studies discussing the prevalence and/or risk factors of RA-ILD, EMBASE, Web of Science, PubMed, and the Cochrane Library were explored. The pooled odds ratios (ORs) / hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated with a fixed/ random effects model. While subgroup analysis, meta-regression analysis and sensitivity analysis were carried out to determine the sources of heterogeneity, the statistic was utilized to assess between-studies heterogeneity. Funnel plots and Egger's test were employed to assess publication bias. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, our review was conducted.
RESULTS
A total of 56 studies with 11,851 RA-ILD patients were included in this meta-analysis. The pooled prevalence of RA-ILD was 18.7% (95% CI 15.8-21.6) with significant heterogeneity ( = 96.4%). The prevalence of RA-ILD was found to be more likely as a result of several identified factors, including male sex (ORs = 1.92 95% CI 1.70-2.16), older age (WMDs = 6.89, 95% CI 3.10-10.67), having a smoking history (ORs =1.91, 95% CI 1.48-2.47), pulmonary comorbidities predicted (HRs = 2.08, 95% CI 1.89-2.30), longer RA duration (ORs = 1.03, 95% CI 1.01-1.05), older age of RA onset (WMDs =4.46, 95% CI 0.63-8.29), positive RF (HRs = 1.15, 95%CI 0.75-1.77; ORs = 2.11, 95%CI 1.65-2.68), positive ACPA (ORs = 2.11, 95%CI 1.65-2.68), higher ESR (ORs = 1.008, 95%CI 1.002-1.014), moderate and high DAS28 (≥3.2) (ORs = 1.87, 95%CI 1.36-2.58), rheumatoid nodules (ORs = 1.87, 95% CI 1.18-2.98), LEF use (ORs = 1.42, 95%CI 1.08-1.87) and steroid use (HRs= 1.70, 1.13-2.55). The use of biological agents was a protective factor (HRs = 0.77, 95% CI 0.69-0.87).
CONCLUSION(S)
The pooled prevalence of RA-ILD in our study was approximately 18.7%. Furthermore, we identified 13 risk factors for RA-ILD, including male sex, older age, having a smoking history, pulmonary comorbidities, older age of RA onset, longer RA duration, positive RF, positive ACPA, higher ESR, moderate and high DAS28 (≥3.2), rheumatoid nodules, LEF use and steroid use. Additionally, biological agents use was a protective factor.
Topics: Humans; Male; Rheumatoid Nodule; Prevalence; Arthritis, Rheumatoid; Risk Factors; Lung Diseases, Interstitial; Steroids
PubMed: 38547537
DOI: 10.1080/07853890.2024.2332406 -
Eco-Environment & Health Dec 2023Micro- and nano-plastics (MNPs) pollution has become a pressing global environmental issue, with growing concerns regarding its impact on human health. However, evidence... (Review)
Review
Micro- and nano-plastics (MNPs) pollution has become a pressing global environmental issue, with growing concerns regarding its impact on human health. However, evidence on the effects of MNPs on human health remains limited. This paper reviews the three routes of human exposure to MNPs, which include ingestion, inhalation, and dermal contact. It further discusses the potential routes of translocation of MNPs in human lungs, intestines, and skin, analyses the potential impact of MNPs on the homeostasis of human organ systems, and provides an outlook on future research priorities for MNPs in human health. There is growing evidence that MNPs are present in human tissues or fluids. Lab studies, including animal models and human-derived cell cultures, revealed that MNPs exposure could negatively affect human health. MNPs exposure could cause oxidative stress, cytotoxicity, disruption of internal barriers like the intestinal, the air-blood and the placental barrier, tissue damage, as well as immune homeostasis imbalance, endocrine disruption, and reproductive and developmental toxicity. Limitedly available epidemiological studies suggest that disorders like lung nodules, asthma, and blood thrombus might be caused or exacerbated by MNPs exposure. However, direct evidence for the effects of MNPs on human health is still scarce, and future research in this area is needed to provide quantitative support for assessing the risk of MNPs to human health.
PubMed: 38435355
DOI: 10.1016/j.eehl.2023.08.002 -
Life (Basel, Switzerland) Sep 2023For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing,... (Review)
Review
OBJECTIVE
For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy.
METHOD
We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively.
RESULTS
Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules.
CONCLUSION
It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research.
PubMed: 37763314
DOI: 10.3390/life13091911 -
Oral Oncology Oct 2023Head and neck squamous cell carcinoma (HNSCC) often presents with synchronous nodules of the lung (sNL), which may be benign nodules, second primary malignancies or... (Review)
Review
Head and neck squamous cell carcinoma (HNSCC) often presents with synchronous nodules of the lung (sNL), which may be benign nodules, second primary malignancies or metastases of HNSCC. We sought to gain an insight into the incidence of sNL and synchronous second primary of the lung (sSPML) in HNSCC patients and current opinions on useful diagnostic and therapeutic approaches. We conducted a systematic search of the PubMed database for articles that reported the simultaneous detection of HNSCC and sNL/sPML, within the timeframe of diagnosis and staging. Only studies involving humans were included, without restrictions for sex, age, ethnicity, or smoking history. All articles were categorised according to the Oxford Centre of Evidence-Based Medicine levels and their data collected. Data from 24 studies were analysed. Amongst HNSCC, the mean overall incidence rate of sNL and sSPML was 11.4% (range: 1.3-27%) and 2.95% (range: 0.4-7.4%), respectively. The possibility of a sNL to be a sSPML cannot be ignored (mean: 35.2%). Studies investigating smoking habits showed that the majority (98-100%) of HNSCC patients with sSPML were previous or active smokers. Detection of human papillomavirus through DNA analysis, p16 immunohistochemistry, and identification of clonal evolution were useful in differentiating metastasis from sSPML. FDG-PET scan was the most reliable method to diagnose sSPML (sensitivity: 95%; specificity: 96%; positive predictive value: 80%). With early sSPML detection and curative treatment, the 5-year overall survival rate is 34-47%. However, the proposed advantage of early detection warrants further evidence-based justification.
Topics: Humans; Squamous Cell Carcinoma of Head and Neck; Neoplasms, Multiple Primary; Head and Neck Neoplasms; Neoplasms, Second Primary; Lung
PubMed: 37506514
DOI: 10.1016/j.oraloncology.2023.106529 -
Insights Into Imaging Sep 2023Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended... (Review)
Review
Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.
PubMed: 37741928
DOI: 10.1186/s13244-023-01480-z -
European Radiology Mar 2024Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations.
METHODS
We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses.
RESULTS
A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90-0.99) and pooled specificity was 0.90 (95% CI: 0.87-0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92-0.99] vs. 0.84 [95% CI: 0.50-0.96]) and lower specificity (0.87 [95% CI: 0.85-0.88] vs. 0.95 (95% CI: 0.92-0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91-0.99] vs. 0.91 [95% CI: 0.67-0.98]) and lower specificity (0.88 [95% CI: 0.85-0.90] vs. 0.93 [95% CI: 0.88-0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83-0.96) and specificity was 0.81 (95% CI: 0.67-0.90).
CONCLUSION
Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence.
CLINICAL RELEVANCE STATEMENT
Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence.
KEY POINTS
• High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0. • In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies. • There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.
Topics: Humans; Tomography, X-Ray Computed; Lung Neoplasms; Early Detection of Cancer; Lung; Sensitivity and Specificity
PubMed: 37646809
DOI: 10.1007/s00330-023-10049-9 -
Diagnostics (Basel, Switzerland) Jan 2024The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance... (Review)
Review
Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort.
The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.
PubMed: 38248024
DOI: 10.3390/diagnostics14020147 -
Radiology. Cardiothoracic Imaging Apr 2024Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and... (Meta-Analysis)
Meta-Analysis
Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT © RSNA, 2024.
Topics: Humans; Lung Neoplasms; Early Detection of Cancer; Multiple Pulmonary Nodules; Asparagales; Magnetic Resonance Imaging
PubMed: 38634743
DOI: 10.1148/ryct.230241 -
Cureus May 2024Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these... (Review)
Review
Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.
PubMed: 38910787
DOI: 10.7759/cureus.61012