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The New England Journal of Medicine Sep 2013Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management... (Clinical Trial)
Clinical Trial
BACKGROUND
Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up.
METHODS
We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer.
RESULTS
In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set.
CONCLUSIONS
Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.).
Topics: Evidence-Based Medicine; Female; Follow-Up Studies; Humans; Logistic Models; Lung; Lung Neoplasms; Male; Models, Statistical; Multiple Pulmonary Nodules; Probability; Prospective Studies; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 24004118
DOI: 10.1056/NEJMoa1214726 -
Diagnostic and Interventional Imaging Oct 2016The investigation of solitary pulmonary nodule (SPN) and non-small cell lung cancer (NSCLC) has rapidly become one of the main indications for F-fluorodeoxyglucose (FDG)... (Review)
Review
The investigation of solitary pulmonary nodule (SPN) and non-small cell lung cancer (NSCLC) has rapidly become one of the main indications for F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). In this literature review, we first attempt to clarify how PET imaging contributes to investigating SPN, in conjunction with conventional CT. We highlight the prospects of research underway to improve our understanding of SPN. In the second part of this review, we analyze the current role of PET-CT in the overall care process for lung cancer. We review the indications for which consensus has been reached, for example initial staging, as well as new indications such as radiation therapy planning or prognostic assessment.
Topics: Algorithms; Early Detection of Cancer; Fluorodeoxyglucose F18; Incidental Findings; Lung; Lung Neoplasms; Neoplasm Staging; Positron Emission Tomography Computed Tomography; Prognosis; Sensitivity and Specificity; Solitary Pulmonary Nodule
PubMed: 27567555
DOI: 10.1016/j.diii.2016.06.020 -
Clinical Microbiology Reviews Apr 2008A pulmonary cavity is a gas-filled area of the lung in the center of a nodule or area of consolidation and may be clinically observed by use of plain chest radiography... (Review)
Review
A pulmonary cavity is a gas-filled area of the lung in the center of a nodule or area of consolidation and may be clinically observed by use of plain chest radiography or computed tomography. Cavities are present in a wide variety of infectious and noninfectious processes. This review discusses the differential diagnosis of pathological processes associated with lung cavities, focusing on infections associated with lung cavities. The goal is to provide the clinician and clinical microbiologist with an overview of the diseases most commonly associated with lung cavities, with attention to the epidemiology and clinical characteristics of the host.
Topics: Humans; Lung; Lung Diseases; Tomography, X-Ray Computed
PubMed: 18400799
DOI: 10.1128/CMR.00060-07 -
Diagnostic and Interventional Imaging Jan 2023Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent... (Review)
Review
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
Topics: Humans; Lung Neoplasms; Artificial Intelligence; Early Detection of Cancer; Neural Networks, Computer; Lung; Solitary Pulmonary Nodule
PubMed: 36513593
DOI: 10.1016/j.diii.2022.11.007 -
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 -
Ugeskrift For Laeger Apr 2024Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary... (Review)
Review
Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary nodules divided into solid and subsolid (including ground glass and part solid). Risk factors for malignancy include age, smoking, female gender, and specific ethnicities. Nodule traits like size, spiculation, upper-lobe location, and emphysema correlate with higher malignancy risk. Managing these potentially malignant nodules relies on evidence-based guidelines and risk stratification. These risk stratification models can standardize the approach for the management of incidental pulmonary findings, as argued in this review.
Topics: Humans; Female; Tomography, X-Ray Computed; Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; Lung Neoplasms; Lung
PubMed: 38606710
DOI: 10.61409/V09230595 -
Respiration; International Review of... 2020With the advent of lung cancer screening, and the increasingly frequent use of computed tomography (CT) scanning for investigating non-pulmonary pathology (for example... (Review)
Review
With the advent of lung cancer screening, and the increasingly frequent use of computed tomography (CT) scanning for investigating non-pulmonary pathology (for example CT coronary angiogram), the number of pulmonary nodules requiring further investigation has risen significantly. Most of these nodules are found in the lung periphery, which presents challenges to biopsy, and many centers rely on trans-thoracic needle biopsy performed under image guidance by radiologists. However, the desire to minimize complications is driving the development of increasingly accurate navigation bronchoscopy platforms, something that will be crucial in the new era of bronchoscopic therapeutics for lung cancer. This review describes these platforms, summarizes the current evidence for their use, and takes a look at future developments.
Topics: Bronchoscopy; Humans; Image-Guided Biopsy; Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Surgery, Computer-Assisted; Surgical Navigation Systems; Tomography, X-Ray Computed
PubMed: 31600761
DOI: 10.1159/000503329 -
Clinics in Chest Medicine Sep 2019Immunoglobulin G4 (IgG4)-Related Disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ and lead to organ dysfunction and irreversible damage. In... (Review)
Review
Immunoglobulin G4 (IgG4)-Related Disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ and lead to organ dysfunction and irreversible damage. In addition to frequent involvement of the salivary glands, lacrimal glands, and/or pancreas, IgG4-RD often affects the chest. Thoracic manifestations include lung nodules and consolidations, pleural thickening, aortitis, and lymphadenopathy. The diagnosis is made after careful clinicopathologic correlation because there is no single diagnostic test with excellent sensitivity or specificity. Biopsy of pulmonary lesions can be useful for distinguishing IgG4-RD from common mimickers. Immunosuppressive regimens, such as glucocorticoids and/or glucocorticoid-sparing agents, form the cornerstone of treatment.
Topics: Humans; Immunoglobulin G4-Related Disease; Lung
PubMed: 31376893
DOI: 10.1016/j.ccm.2019.05.005 -
Pulmonology 2022It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a...
BACKGROUND
It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting.
METHODS
The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy.
RESULTS
From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573.
CONCLUSIONS
The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.
Topics: Male; Humans; Middle Aged; Female; Models, Statistical; Retrospective Studies; Prognosis; Lung Neoplasms; Lung
PubMed: 32739327
DOI: 10.1016/j.pulmoe.2020.06.011 -
Clinical Epigenetics Oct 2021Lung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation...
BACKGROUND
Lung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation biomarkers become a possible method for lung cancer diagnosis.
RESULTS
We identified eleven lung cancer-specific methylation markers (CDO1, GSHR, HOXA11, HOXB4-1, HOXB4-2, HOXB4-3, HOXB4-4, LHX9, MIR196A1, PTGER4-1, and PTGER4-2), which could differentiate benign and malignant pulmonary nodules. The methylation levels of these markers are significantly higher in malignant tissues. In bronchoalveolar lavage fluid (BALF) samples, the methylation signals maintain the same differential trend as in tissues. An optimal 5-marker model for pulmonary nodule diagnosis (malignant vs. benign) was developed from all possible combinations of the eleven markers. In the test set (57 tissue and 71 BALF samples), the area under curve (AUC) value achieves 0.93, and the overall sensitivity is 82% at the specificity of 91%. In an independent validation set (111 BALF samples), the AUC is 0.82 with a specificity of 82% and a sensitivity of 70%.
CONCLUSIONS
This model can differentiate pulmonary adenocarcinoma and squamous carcinoma from benign diseases, especially for infection, inflammation, and tuberculosis. The model's performance is not affected by gender, age, smoking history, or the solid components of nodules.
Topics: Aged; Biomarkers, Tumor; Bronchoalveolar Lavage Fluid; DNA Methylation; Female; Humans; Lung; Male; Middle Aged; Multiple Pulmonary Nodules
PubMed: 34620221
DOI: 10.1186/s13148-021-01163-w