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Systematic Reviews Dec 2021Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and...
BACKGROUND
Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.
METHODS
We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.
RESULTS
A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.
CONCLUSIONS
The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42020161559.
Topics: Humans; Lung; Lung Neoplasms; Prognosis; Risk Factors; Solitary Pulmonary Nodule
PubMed: 34872592
DOI: 10.1186/s13643-021-01856-6 -
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 -
Cureus Jan 2022Lung cancer has been the leading cause of cancer-associated deaths worldwide. While numerous reasons, including tobacco smoking, may lead to lung cancer, the purpose of... (Review)
Review
Lung cancer has been the leading cause of cancer-associated deaths worldwide. While numerous reasons, including tobacco smoking, may lead to lung cancer, the purpose of this article was to explore the association between sarcoidosis, a multisystem granulomatous disorder, and lung neoplasms. A literature search was done on multiple databases with appropriate keywords, and the authors selected case reports where patients were diagnosed with sarcoidosis and lung cancer. These reports were analyzed in detail, and nine reports were included in this study. Each case was evaluated for the presenting symptoms, age, gender, and diagnostic procedures, including a follow-up analysis. After the evaluation, it can be concluded that sarcoidosis and lung cancer can occur simultaneously, despite being rare. Appropriate diagnostic procedures, including histopathological examination of the affected lymph nodes, showed either cancerous or non-cancerous cells (granulomas), thus altering the treatment on a case-by-case basis. Being aware of all possible associations between these two diseases could alter the clinical management, whether curative or palliative, and clinicians must rule out metastatic cancer in individuals with sarcoidosis-like clinical and radiographic features.
PubMed: 35103216
DOI: 10.7759/cureus.21169 -
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 -
Frontiers in Oncology 2022Establishing risk-based follow-up management strategies is crucial to the surveillance of subsolid pulmonary nodules (SSNs). However, the risk factors for SSN growth are...
BACKGROUND
Establishing risk-based follow-up management strategies is crucial to the surveillance of subsolid pulmonary nodules (SSNs). However, the risk factors for SSN growth are not currently clear. This study aimed to perform a systematic review and meta-analysis to identify clinical and CT features correlated with SSN growth.
METHODS
Relevant studies were retrieved from Web of Science, PubMed, Cochrane Library, and EMBASE. The correlations of clinical and CT features with SSN growth were pooled using a random-effects model or fixed-effects model depending on heterogeneity, which was examined by the test and test. Pooled odds ratio (OR) or pooled standardized mean differences (SMD) based on univariate analyses were calculated to assess the correlation of clinical and CT features with SSN growth. Pooled ORs based on multivariate analyses were calculated to find out independent risk factors to SSN growth. Subgroup meta-analysis was performed based on nodule consistency (pure ground-glass nodule (pGGN) and part-solid nodule (PSN). Publication bias was examined using funnel plots.
RESULTS
Nineteen original studies were included, consisting of 2444 patients and 3012 SSNs. The median/mean follow-up duration of these studies ranged from 24.2 months to 112 months. Significant correlations were observed between SSN growth and eighteen features. Male sex, history of lung cancer, nodule size > 10 mm, nodule consistency, and age > 65 years were identified as independent risk factors for SSN growth based on multivariate analyses results. Eight features, including male sex, smoking history, nodule size > 10 mm, larger nodule size, air bronchogram, higher mean CT attenuation, well-defined border, and lobulated margin were detected to be significantly correlated with pGGNs growth. Smoking history showed no significant correlation with pGGN growth based on the multivariate analysis results.
CONCLUSIONS
Eighteen clinical and CT features were identified to be correlated with SSN growth, among which male sex, history of lung cancer, nodule size > 10 mm, nodule consistency and age > 65 years were independent risk factors while history of lung cancer was not correlated with pGGN growth. These factors should be considered when making risk-based follow-up plans for SSN patients.
PubMed: 35860567
DOI: 10.3389/fonc.2022.929174 -
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 -
Diagnostics (Basel, Switzerland) Nov 2019The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT)... (Review)
Review
The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review.
The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68-99.6% and a detection accuracy between 80.6-94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.
PubMed: 31795409
DOI: 10.3390/diagnostics9040207 -
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 -
Clinical Imaging Oct 2022Common CT abnormalities of pulmonary aspergillosis represent a cavity with air-meniscus sign, nodule, mass, and consolidation having an angio-invasive pattern. This...
PURPOSE
Common CT abnormalities of pulmonary aspergillosis represent a cavity with air-meniscus sign, nodule, mass, and consolidation having an angio-invasive pattern. This study aims to conduct a systematic review and an individual patient-level image analysis of CT findings of COVID-19-associated pulmonary aspergillosis (CAPA).
METHODS
A systematic literature search was conducted to identify studies reporting CT findings of CAPA as of January 7, 2021. We summarized study-level clinical and CT findings of CAPA and collected individual patient CT images by inviting corresponding authors. The CT findings were categorized into four groups: group 1, typical appearance of COVID-19; group 2, indeterminate appearance of COVID-19; group 3, atypical for COVID-19 without cavities; and group 4, atypical for COVID-19 with cavities. In group 2, cases had only minor discrepant findings including solid nodules, isolated airspace consolidation with negligible ground-glass opacities, centrilobular micronodules, bronchial abnormalities, and cavities.
RESULTS
The literature search identified 89 patients from 25 studies, and we collected CT images from 35 CAPA patients (mean age 62.4 ± 14.6 years; 21 men): group 1, thirteen patients (37.1%); group 2, eight patients (22.9%); group 3, six patients (17.1%); and group 4, eight patients (22.9%). Eight of the 14 patients (57.1%) with an atypical appearance had bronchial abnormalities, whereas only one (7.1%) had an angio-invasive fungal pattern. In the study-level analysis, cavities were reported in 12 of 54 patients (22.2%).
CONCLUSION
CAPA can frequently manifest as COVID-19 pneumonia without common CT abnormalities of pulmonary aspergillosis. If abnormalities exist on CT images, CAPA may frequently accompany bronchial abnormalities.
Topics: Aged; COVID-19; Data Analysis; Humans; Lung; Male; Middle Aged; Pulmonary Aspergillosis; Tomography, X-Ray Computed
PubMed: 35908455
DOI: 10.1016/j.clinimag.2022.07.003 -
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