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Lung Cancer (Amsterdam, Netherlands) Jun 2023Navigation bronchoscopy has seen rapid development in the past decade in terms of new navigation techniques and multi-modality approaches utilizing different techniques... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Navigation bronchoscopy has seen rapid development in the past decade in terms of new navigation techniques and multi-modality approaches utilizing different techniques and tools. This systematic review analyses the diagnostic yield and safety of navigation bronchoscopy for the diagnosis of peripheral pulmonary nodules suspected of lung cancer.
METHODS
An extensive search was performed in Embase, Medline and Cochrane CENTRAL in May 2022. Eligible studies used cone-beam CT-guided navigation (CBCT), electromagnetic navigation (EMN), robotic navigation (RB) or virtual bronchoscopy (VB) as the primary navigation technique. Primary outcomes were diagnostic yield and adverse events. Quality of studies was assessed using QUADAS-2. Random effects meta-analysis was performed, with subgroup analyses for different navigation techniques, newer versus older techniques, nodule size, publication year, and strictness of diagnostic yield definition. Explorative analyses of subgroups reported by studies was performed for nodule size and bronchus sign.
RESULTS
A total of 95 studies (n = 10,381 patients; n = 10,682 nodules) were included. The majority (n = 63; 66.3%) had high risk of bias or applicability concerns in at least one QUADAS-2 domain. Summary diagnostic yield was 70.9% (95%-CI 68.4%-73.2%). Overall pneumothorax rate was 2.5%. Newer navigation techniques using advanced imaging and/or robotics(CBCT, RB, tomosynthesis guided EMN; n = 24 studies) had a statistically significant higher diagnostic yield compared to longer established techniques (EMN, VB; n = 82 studies): 77.5% (95%-CI 74.7%-80.1%) vs 68.8% (95%-CI 65.9%-71.6%) (p < 0.001).Explorative subgroup analyses showed that larger nodule size and bronchus sign presence were associated with a statistically significant higher diagnostic yield. Other subgroup analyses showed no significant differences.
CONCLUSION
Navigation bronchoscopy is a safe procedure, with the potential for high diagnostic yield, in particular using newer techniques such as RB, CBCT and tomosynthesis-guided EMN. Studies showed a large amount of heterogeneity, making comparisons difficult. Standardized definitions for outcomes with relevant clinical context will improve future comparability.
Topics: Humans; Bronchoscopy; Lung Neoplasms; Solitary Pulmonary Nodule; Bronchi; Cone-Beam Computed Tomography
PubMed: 37130440
DOI: 10.1016/j.lungcan.2023.107196 -
Thoracic Cancer Mar 2022Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary... (Review)
Review
BACKGROUND
Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.
METHODS
The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.
RESULTS
A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.
CONCLUSION
The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
Topics: Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Multiple Pulmonary Nodules; Retrospective Studies
PubMed: 35137543
DOI: 10.1111/1759-7714.14333 -
The Cochrane Database of Systematic... Aug 2022Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT). OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening.
SEARCH METHODS
We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021. SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes. DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I statistic to investigate heterogeneity. MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years. None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence). Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence). False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour. AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.
Topics: Adult; Bias; Early Detection of Cancer; Female; Humans; Lung Neoplasms; Male; Randomized Controlled Trials as Topic; Tomography, X-Ray Computed
PubMed: 35921047
DOI: 10.1002/14651858.CD013829.pub2 -
Pulmonary Circulation 2019Pulmonary tumor thrombotic microangiopathy (PTTM) is a fatal disease process in which pulmonary hypertension (PH) develops in the setting of malignancy. The purpose of...
Pulmonary tumor thrombotic microangiopathy (PTTM) is a fatal disease process in which pulmonary hypertension (PH) develops in the setting of malignancy. The purpose of this study is to present a detailed analysis of cases of PTTM reported in literature in the hopes of achieving more ante-mortem diagnoses. We conducted a systematic review of currently published and available cases of PTTM by searching the term "pulmonary tumor thrombotic microangiopathy" on the Pubmed.gov database. Seventy-nine publications were included consisting of 160 unique cases of PTTM. The most commonly reported malignancy was gastric adenocarcinoma (94 cases, 59%). Cough and dyspnea were reported in 61 (85%) and 102 (94%) cases, respectively. Hypoxemia was reported in 96 cases (95%). Elevation in D-dimer was noted in 36 cases (95%), presence of anemia in 32 cases (84%), and thrombocytopenia in 30 cases (77%). Common findings on chest computed tomography (CT) included ground-glass opacities (GGO) in 28 cases (82%) and nodules in 24 cases (86%). PH on echocardiography was noted in 59 cases (89%) with an average right ventricular systolic pressure of 71 mmHg. Common features of PTTM that are reported across the published literature include presence of dyspnea and cough, hypoxemia, with abnormal CT findings of GGO, nodules, and mediastinal/hilar lymphadenopathy, and PH. PTTM is a universally fatal disease process and this analysis provides a detailed examination of all the available published data that may help clinicians establish an earlier diagnosis of PTTM.
PubMed: 31032740
DOI: 10.1177/2045894019851000 -
NPJ Digital Medicine Apr 2021Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic... (Review)
Review
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
PubMed: 33828217
DOI: 10.1038/s41746-021-00438-z -
Frontiers in Public Health 2022Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.
METHODS
Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.
RESULTS
The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78-0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73-0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77-0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66-0.82).
CONCLUSION
The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.
SYSTEMATIC REVIEW REGISTRATION
https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.
Topics: Artificial Intelligence; Deep Learning; Humans; Lung; Lung Neoplasms; Neoplasm Staging
PubMed: 35923964
DOI: 10.3389/fpubh.2022.938113 -
International Journal of Environmental... Feb 2022Lung cancer (LC) represents the main cause of cancer-related deaths worldwide, especially because the majority of patients present with an advanced stage of the disease... (Review)
Review
Lung cancer (LC) represents the main cause of cancer-related deaths worldwide, especially because the majority of patients present with an advanced stage of the disease at the time of diagnosis. This systematic review describes the evidence behind screening results and the current guidelines available to manage lung nodules. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The following electronic databases were searched: PubMed, EMBASE, and the Web of Science. Five studies were included in the systematic review. The study cohort included 46,364 patients, and, in this case series, LC was detected in 9028 patients. Among the patients with detected LC, 1261 died of lung cancer, 3153 died of other types of cancers and 4614 died of other causes. This systematic review validates the use of CT in LC screening follow-ups, and bids for future integration and implementation of nodule management protocols to improve LC screening, avoid missed cancers and to reduce the number of unnecessary investigations.
Topics: Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Mass Screening; Research
PubMed: 35206646
DOI: 10.3390/ijerph19042460 -
Asian Journal of Surgery Aug 2021Cough is a common complication following pulmonary resection. Persistent and severe cough after pulmonary resection can cause significant impairments in quality of life... (Review)
Review
Cough is a common complication following pulmonary resection. Persistent and severe cough after pulmonary resection can cause significant impairments in quality of life among postoperative patients. Complications of cough can be life-threatening. To improve patients' probability and quality of life, factors that induce cough after pulmonary resection (CAP) and potential treatments should be explored and summarized. Previous studies have identified various factors related to CAP. However, those factors have not been categorized and analyzed in a sensible manner. Here, we summarized the different factors and classified them into four groups. Potential therapies might be developed to selectively target different factors that affect CAP. However, the exact mechanism underlying CAP remains unknown, making it difficult to treat and manage CAP. In this review, we summarized the latest studies in our understanding of the factors related to CAP and potential treatments targeting those factors. This review can help understand the mechanism of CAP and develop efficient therapies and management.
Topics: Cough; Humans; Quality of Life
PubMed: 33610443
DOI: 10.1016/j.asjsur.2021.01.001 -
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 -
Computer Methods and Programs in... Feb 2016This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to... (Review)
Review
This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.
Topics: Algorithms; Evidence-Based Medicine; Humans; Lung Neoplasms; Machine Learning; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Software; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 26652979
DOI: 10.1016/j.cmpb.2015.10.006