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LDCT lung cancer screening eligibility and use of CT scans for lung cancer among sexual minorities.

ABSTRACT: OBJECTIVE:To compare eligibility for lung cancer screening and receipt of a CT scan for lung cancer among sexual minorities. METHODS:Secondary data analysis of cross-sectional data from older U.S. adults in the Behavioral Risk Factor Surveillance System survey during the 2017 cycle (n?=?20,685). RESULTS:Rates of eligibility for low-dose helical computed tomography (LDCT) were roughly twice as high among sexual minorities than among heterosexuals (21.1% vs. 11.7%). The odds of gay men and lesbian women indicating eligibility for LDCT screening were four to five times higher when compared to their heterosexual peers. No statistically significant differences were found between sexual minorities and heterosexuals with respect to having a CT scan for lung cancer in the past year. CONCLUSIONS:There are potential sexual-identity-related disparities in the utilization of lung cancer screening among eligible smokers. Interventions are needed to increase awareness and uptake of lung cancer screening in order to detect and manage this common form of cancer in the U.S.

SUBMITTER: Veliz P

PROVIDER: S-EPMC6527458 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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LDCT lung cancer screening eligibility and use of CT scans for lung cancer among sexual minorities.

Veliz Philip P Matthews Alicia K AK Arslanian-Engoren Cynthia C Evans-Polce Rebecca J RJ Lee Joseph G L JGL Boyd Carol J CJ Hughes Tonda T McCabe Vita V VV McCabe Sean Esteban SE

Cancer epidemiology 20190323

<h4>Objective</h4>To compare eligibility for lung cancer screening and receipt of a CT scan for lung cancer among sexual minorities.<h4>Methods</h4>Secondary data analysis of cross-sectional data from older U.S. adults in the Behavioral Risk Factor Surveillance System survey during the 2017 cycle (n = 20,685).<h4>Results</h4>Rates of eligibility for low-dose helical computed tomography (LDCT) were roughly twice as high among sexual minorities than among heterosexuals (21.1% vs. 11.7%). The odds ...[more]

PMID: 30909153

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Project description:BackgroundLung cancer screening (LCS) via chest computed tomography (CT) scans can save lives by identifying early-stage tumors. However, most smokers die of comorbid smoking-related diseases. LCS scans contain information about smoking-related conditions that is not currently systematically assessed. Identifying these common comorbid diseases on CT could increase the value of screening with minimal impact on LCS programs. We determined the prevalence of 3 comorbid diseases from LCS eligible scans and quantified related adverse outcomes.MethodsWe studied COPD Genetic Epidemiology study (COPDGene®) participants (n=4078) who met criteria for LCS screening at enrollment (age > 55 years, and < 80 years, > 30 pack years smoking, current smoker or former smoker within 15 years of smoking cessation). CT scans were assessed for coronary artery calcification (CAC), emphysema, and vertebral bone density. We tracked the following clinically significant events: myocardial infarctions (MIs), strokes, pneumonia, respiratory exacerbations, and hip and vertebral fractures.ResultsOverall, 77% of eligible CT scans had one or more of these diagnoses identified. CAC (> 100 mg) was identified in 51% of scans, emphysema in 44%, and osteoporosis in 54%. Adverse events related to the underlying smoking-related diseases were common, with 50% of participants reporting at least one. New diagnoses of cardiovascular disease, emphysema and osteoporosis were made in 25%, 7% and 46%, of participants respectively. New diagnosis of disease was associated with significantly more adverse events than in participants who did not have CT diagnoses for both osteoporosis and cardiovascular risk.ConclusionsExpanded analysis of LCS CT scans identified individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis that corresponded with adverse events. LCS CT scans can potentially facilitate diagnoses of these smoking-related diseases and provide an opportunity for treatment or prevention.

Project description:ObjectiveThe research aims to develop an advanced and precise lung cancer screening model based on Convolutional Neural Networks (CNN).MethodsBased on the health medical big data platform of Shandong University, we developed a VGG16-Based CNN lung cancer screening model. This model was trained using the Computed Tomography scans data of patients from Pingyi Traditional Chinese Medicine Hospital in Shandong Province, from January to February 2023. Data augmentation techniques, including random resizing, cropping, horizontal flipping, color jitter, random rotation and normalization, were applied to improve model generalization. We used five-fold cross-validation to robustly assess performance. The model was fine-tuned with an SGD optimizer (learning rate 0.001, momentum 0.9, and L2 regularization) and a learning rate scheduler. Dropout layers were added to prevent the model from relying too heavily on specific neurons, enhancing its ability to generalize. Early stopping was implemented when validation loss did not decrease over 10 epochs. In addition, we evaluated the model's performance with Area Under the Curve (AUC), Classification accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV), Sensitivity, Specificity and F1 score. External validation used an independent dataset from the same hospital, covering January to February 2022.ResultsThe training and validation loss and accuracy over iterations show that both accuracy metrics peak at over 0.9 by iteration 15, prompting early stopping to prevent overfitting. Based on five-fold cross-validation, the ROC curves for the VGG16-Based CNN model, demonstrate an AUC of 0.963 ± 0.004, highlighting its excellent diagnostic capability. Confusion matrices provide average metrics with a classification accuracy of 0.917 ± 0.004, PPV of 0.868 ± 0.015, NPV of 0.931 ± 0.003, Sensitivity of 0.776 ± 0.01, Specificity of 0.962 ± 0.005 and F1 score of 0.819 ± 0.008, respectively. External validation confirmed the model's robustness across different patient populations and imaging conditions.ConclusionThe VGG16-Based CNN lung screening model constructed in this study can effectively identify lung tumors, demonstrating reliability and effectiveness in real-world medical settings, and providing strong theoretical and empirical support for its use in lung cancer screening.

Project description:PurposeTo measure reader agreement in determining whether lung nodules detected at baseline screening computed tomography (CT) had changed at subsequent screening examinations and to evaluate the variability in recommendations for further follow-up.Materials and methodsAll subjects were enrolled in the National Lung Screening Trial (NLST), and each participant consented to the use of their de-identified images for research purposes. The authors randomly selected 100 cases of nodules measuring at least 4.0 mm at 1-year screening CT that were considered by the original screening CT reader to be present on baseline CT scans; nodules considered by the original reader to have changed were oversampled. Selected images from each case showing the entire nodule at both examinations were preloaded on a picture archiving and communication system workstation. Nine radiologists served as readers, and they evaluated whether the nodule was present at baseline and recorded the bidimensional measurements and nodule characteristics at each examination, presence or absence of change, results of screening CT, and follow-up recommendations (high-level follow-up, low-level follow-up, no follow-up).ResultsOn the basis of reviews during case selection, five nodules seen at follow-up were judged not to have been present at baseline; for 19 of the remaining 95 cases, at least one reader judged the nodule not to have been present at baseline. For the 76 nodules that were unanimously considered to have been present at baseline, 21%-47% (mean ± standard deviation, 30% ± 9) were judged to have grown. The κ values were similar for growth (κ = 0.55) and a positive screening result (κ = 0.51) and were lower for a change in margins and attenuation (κ = 0.27-0.31). The κ value in the recommendation of high- versus low-level follow-up was high (κ = 0.66).ConclusionReader agreement on nodule growth and screening result was moderate to substantial. Agreement on follow-up recommendations was lower.

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LDCT lung cancer screening eligibility and use of CT scans for lung cancer among sexual minorities.

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LDCT lung cancer screening eligibility and use of CT scans for lung cancer among sexual minorities.

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