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Acta Radiologica (Stockholm, Sweden :... Dec 2023Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation.... (Meta-Analysis)
Meta-Analysis Review
Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.
Topics: Humans; Lung Neoplasms; Tomography, X-Ray Computed; Sensitivity and Specificity; Lung
PubMed: 37817511
DOI: 10.1177/02841851231205737 -
Annals of the American Thoracic Society Dec 2023Conventional electromagnetic navigation bronchoscopy and other guided bronchoscopic modalities have a very desirable safety profile, but their diagnostic yield is only... (Meta-Analysis)
Meta-Analysis
Conventional electromagnetic navigation bronchoscopy and other guided bronchoscopic modalities have a very desirable safety profile, but their diagnostic yield is only 60-70% for pulmonary lesions. Recently, robotic-assisted bronchoscopy (RAB) platforms have been introduced to improve the diagnostic performance of bronchoscopic modalities. To determine the diagnostic performance and safety profile of RAB (using shape-sensing and electromagnetic navigation-based platforms) by performing a systematic review and meta-analysis. The PubMed, Embase, and Google Scholar databases were searched to find studies that reported on the diagnostic performance and/or the safety profile of one of the RAB systems. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Meta-analysis was performed using MedCalc version 20.118. Pooled diagnostic yield was calculated using a Freeman-Tukey transformation. We planned to use a random-effects model if the index was >40%. Twenty-five studies were included: 20 including diagnostic and safety analyses and 5 including only safety analyses. The pooled diagnostic yield of RAB (20 studies, 1,779 lesions) was 84.3% (95% confidence interval, 81.1-87.2%). The index was 65.6%. On the basis of our subgroup analyses, the heterogeneity was likely driven by differences in study designs (prospective vs. retrospective) and procedural protocols (such as different RAB systems). Lesion size > 2 cm, the presence of a computed tomography bronchus sign, and concentric radial endobronchial ultrasound view were associated with a statistically significant increase in the odds of diagnosis with RAB. The overall rates of pneumothorax, need for tube thoracostomy, and significant hemorrhage were 2.3%, 1.2%, and 0.5%, respectively. RAB systems have significantly increased the diagnostic yield of navigational bronchoscopy compared with conventional systems such as electromagnetic navigation bronchoscopy, but well-designed prospective studies are needed to better understand the impact of various factors, such as the use of three-dimensional imaging modalities, cryobiopsy, and specific ventilatory protocols, on the diagnostic yield of RAB.
Topics: Humans; Bronchoscopy; Lung Neoplasms; Robotic Surgical Procedures; Prospective Studies; Retrospective Studies
PubMed: 37769170
DOI: 10.1513/AnnalsATS.202301-075OC -
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 -
Acta Radiologica (Stockholm, Sweden :... Dec 2023Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent.
PURPOSE
To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images.
MATERIAL AND METHODS
PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity.
RESULTS
Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies.
CONCLUSION
The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules.
Topics: Humans; Multiple Pulmonary Nodules; Neural Networks, Computer; Lung Neoplasms; Sensitivity and Specificity; ROC Curve
PubMed: 37743663
DOI: 10.1177/02841851231201514 -
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 -
Journal of Global Health Sep 2023China has a high burden of nontuberculous mycobacterial (NTM) infections. Immunocompromised populations, such as those with human immunodeficiency virus/acquired... (Meta-Analysis)
Meta-Analysis
BACKGROUND
China has a high burden of nontuberculous mycobacterial (NTM) infections. Immunocompromised populations, such as those with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), are at a higher risk of being infected with NTM than immunocompetent individuals. Yet, there is a paucity of information on the clinical features of positive NTM isolates from patients with HIV/AIDS in China. To address this gap, we conducted a systematic review and meta-analysis of existing studies, comparing them against current expert consensus to provide guidance for clinical practice.
METHODS
Two researchers independently searched eight databases (SinoMed, China National Knowledge Infrastructure, Wanfang, VIP, Cochrane Library, PubMed, Embase, and Web of Science) from inception to 26 December 2022 to retrieve published Chinese- and English-language studies reporting clinical features of NTM-positive isolates among patients with HIV/AIDS in China.
RESULTS
We included 28 studies with 1861 patients. The rate of positive NTM isolates detected from men among all patients was 87.3%. NTM species distribution was mainly Mycobacterium avium complex (64.3%), which was predominant in different regions. The five most common clinical symptoms were fever (68.5%), cough or expectoration (67.0%), appetite loss (49.4%), weight loss (45.5%), and superficial lymphadenectasis (41.1%). The prevalence of laboratory tests were as follows: albumin <35 g/L (55.6%), erythrocyte sedimentation rate >20 mm/h (91.4%), anaemia (59.0%), predominantly mild, CD4+ T cell count ≤50 pieces/μL (70.3%), and CD4+ T cell count 51-200 pieces/μL (22.1%). Lesion manifestations in thoracic imaging mainly included bilateral lung involvement (83.8%), showed stripe shadows (60.3%), patchy shadows (42.9%), nodules (40.6%), and bronchiectasis (38.6%). Accompanied signs included thoracic lymph node enlargement (49.5%). Seventy per cent of symptoms improved after treatment.
CONCLUSIONS
Focusing on clinical symptoms, laboratory tests, and thoracic imaging helps with initial screening for NTM infections. Physicians should raise awareness of the diagnosis and treatment of Mycobacterium avium complex, providing guidance for experimental treatment, screening of priority populations for NTM infections, and prophylactic treatment of NTM disease.
REGISTRATION
PROSPERO CRD42023388185.
Topics: Humans; Male; Acquired Immunodeficiency Syndrome; China; Nontuberculous Mycobacteria; Mycobacterium Infections, Nontuberculous; HIV Infections
PubMed: 37651639
DOI: 10.7189/jogh.13.04093 -
European Radiology Mar 2024The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis.
METHODS
In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model.
RESULTS
In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I = 30.6%) for recurrence-free survival.
CONCLUSIONS
The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival.
CLINICAL RELEVANCE STATEMENT
Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system.
KEY POINTS
• In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). • Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). • In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13).
Topics: Humans; Prognosis; Retrospective Studies; Artificial Intelligence; Neoplasm Staging; Adenocarcinoma of Lung; Lung Neoplasms; Tomography, X-Ray Computed
PubMed: 37650971
DOI: 10.1007/s00330-023-10160-x -
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 -
Sensors (Basel, Switzerland) Jul 2023Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest... (Review)
Review
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
Topics: Humans; X-Rays; Radiography, Thoracic; Tuberculosis, Pulmonary; Radiography; Tuberculosis
PubMed: 37571564
DOI: 10.3390/s23156781 -
Zhonghua Lao Dong Wei Sheng Zhi Ye Bing... Jul 2023To investigate the epidemiology, clinical characteristics, on-site dust monitoring and individual protection of the patients with artificial stone-related silicosis....
To investigate the epidemiology, clinical characteristics, on-site dust monitoring and individual protection of the patients with artificial stone-related silicosis. In March 2022, the literature on artificial stone-related silicosis published from January 1965 to February 2022 was searched in China Journal Full-text Database, Wanfang Database, VIP Database, EMbase and PubMed. Chinese and English search terms include "silica dust""silica dust""silicosis""artificial stone""pneumoconiosis", etc. References were included according to inclusion and exclusion criteria, and data were extracted. The epidemiological characteristics, natural course of disease, workplace dust concentration and individual protection level of patients with artificial stone-related silicosis were analyzed by systematic review. A total of 30 literatures were included, including 7 cohort studies, 14 cross-sectional studies, 3 case-control studies and 6 case reports. A total of 1358 patients with artificial stone-related silicosis were diagnosed from 1997 to 2020, with an average age of 41.5 years old and an average dust exposure time of 11.3 years. Among them, 36.2% (282/778) had progressive mass fibrosis or accelerated progressive silicosis at first diagnosis. Chest imaging showed diffuse small nodule shadow, pulmonary fibrosis, and silico-alveolar proteinosis. Pulmonary function showed restricted or mixed ventilation disorder with or without decreased diffusion volume. The disease progressed rapidly, with progressive mass fibrosis, respiratory failure, and even death. Patients engaged in artificial quartz stone processing, with high concentration of silica including ultra-fine particles, most of which were dry operation, lack of on-site ventilation measures and no effective personal protection. The artificial stone processing workers suffer from artificial stone-related silicosis due to dry cutting, lack of on-site dust removal facilities and personal protective measures, and the disease progresses rapidly, leading to poor prognosis.
Topics: Humans; Adult; Dust; Cross-Sectional Studies; Occupational Exposure; Silicosis; Silicon Dioxide; Pulmonary Fibrosis
PubMed: 37524674
DOI: 10.3760/cma.j.cn121094-20220408-00185