Dataset Information

Effect of emphysema on lung cancer risk in smokers: a computed tomography-based assessment.

ABSTRACT: The contribution of emphysema to lung cancer risk has been recognized, but the effect size needs to be further defined. In this study, 565 primary lung cancer cases were enrolled though a prospective lung cancer cohort at Mayo Clinic, and 450 controls were smokers participating in a lung cancer screening study in the same institution using spiral computed tomography (CT). Cases and controls were frequency matched on age, gender, race, smoking status, and residential region. CT imaging using standard protocol at the time of lung cancer diagnosis (case) or during the study (control) was assessed for emphysema by visual scoring CT analysis as a percentage of lung tissue destroyed. The clinical definition of emphysema was the diagnosis recorded in the medical documentation. Using multiple logistic regression models, emphysema (? 5% on CT) was found to be associated with a 3.8-fold increased lung cancer risk in Caucasians, with higher risk in subgroups of younger (<65 years old, OR = 4.64), heavy smokers (? 40 pack-years, OR = 4.46), and small-cell lung cancer (OR = 5.62). When using >0% or ? 10% emphysema on CT, lung cancer risk was 2.79-fold or 3.33-fold higher than controls. Compared with CT evaluation (using criterion ? 5%), the sensitivity, specificity, positive and negative predictive values, and the accuracy of the clinical diagnosis for emphysema in controls were 19%, 98%, 73%, 84%, and 83%, respectively. These results imply that an accurate evaluation of emphysema could help reliably identify individuals at greater risk of lung cancer among smokers.

SUBMITTER: Li Y

PROVIDER: S-EPMC3018159 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Effect of emphysema on lung cancer risk in smokers: a computed tomography-based assessment.

Li Yan Y Swensen Stephen J SJ Karabekmez Leman Günbey LG Marks Randolph S RS Stoddard Shawn M SM Jiang Ruoxiang R Worra Joel B JB Zhang Fang F Midthun David E DE de Andrade Mariza M Song Yong Y Yang Ping P

Cancer prevention research (Philadelphia, Pa.) 20101130 1

The contribution of emphysema to lung cancer risk has been recognized, but the effect size needs to be further defined. In this study, 565 primary lung cancer cases were enrolled though a prospective lung cancer cohort at Mayo Clinic, and 450 controls were smokers participating in a lung cancer screening study in the same institution using spiral computed tomography (CT). Cases and controls were frequency matched on age, gender, race, smoking status, and residential region. CT imaging using stan ...[more]

PMID: 21119049

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Project description:Background:Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. Objective:To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Design:Population-based prospective studies. Setting:United States. Participants:Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Measurements:Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). Results:At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen. Limitation:No consensus on risk thresholds for screening. Conclusion:The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening. Primary Funding Source:Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Project description:BackgroundIn vivo high-resolution micro-computed tomography allows for longitudinal image-based measurements in animal models of lung disease. The combination of repetitive high resolution imaging with fully automated quantitative image analysis in mouse models of lung fibrosis lung benefits preclinical research. This study aimed to develop and validate such an automated micro-computed tomography analysis algorithm for quantification of aerated lung volume in mice; an indicator of pulmonary fibrosis and emphysema severity.MethodologyMice received an intratracheal instillation of bleomycin (n = 8), elastase (0.25 U elastase n = 9, 0.5 U elastase n = 8) or saline control (n = 6 for fibrosis, n = 5 for emphysema). A subset of mice was scanned without intervention, to evaluate potential radiation-induced toxicity (n = 4). Some bleomycin-instilled mice were treated with imatinib for proof of concept (n = 8). Mice were scanned weekly, until four weeks after induction, when they underwent pulmonary function testing, lung histology and collagen quantification. Aerated lung volumes were calculated with our automated algorithm.Principal findingsOur automated image-based aerated lung volume quantification method is reproducible with low intra-subject variability. Bleomycin-treated mice had significantly lower scan-derived aerated lung volumes, compared to controls. Aerated lung volume correlated with the histopathological fibrosis score and total lung collagen content. Inversely, a dose-dependent increase in lung volume was observed in elastase-treated mice. Serial scanning of individual mice is feasible and visualized dynamic disease progression. No radiation-induced toxicity was observed. Three-dimensional images provided critical topographical information.ConclusionsWe report on a high resolution in vivo micro-computed tomography image analysis algorithm that runs fully automated and allows quantification of aerated lung volume in mice. This method is reproducible with low inherent measurement variability. We show that it is a reliable quantitative tool to investigate experimental lung fibrosis and emphysema in mice. Its non-invasive nature has the unique benefit to allow dynamic 4D evaluation of disease processes and therapeutic interventions.

Project description:There is a paucity of literature on symptom score (SS) plus fiberoptic bronchoscopy (FOB) -based response evaluation (RE) to chemotherapy for lung cancer. This study aimed to compare the reliability of RE by SS, chest radiograph (CXR), and FOB with computed tomography (CT) -based assessment (Response Evaluation Criteria in Solid Tumors (RECIST) and WHO criteria) for lung cancer chemotherapy.This was a prospective observational study involving treatment-naïve patients with lung cancer planned for chemotherapy, with one or more lesions on FOB and CT. Patients underwent assessment twice by SS, CXR, FOB, and CT (at baseline and after chemotherapy). Six symptoms (dyspnea, cough, chest pain, hemoptysis, anorexia, and weight loss) were noted on visual analog scale. Respiratory symptom burden (RSB) and total symptom burden (TSB) were calculated from the first four and all six symptoms, respectively, as the mean of individual SS. Bronchoscopic findings were recorded as per European Respiratory Society classification for tracheobronchial stenosis. Responses were classified as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD) by each method. For FOB and SS, improvement or worsening by ≥ 20% was taken as PR or PD, respectively, whereas < 20% change was considered SD. Agreements were tested using Cohen's κ statistic.All individual SS, RSB, and TSB scores, and the number and distribution of FOB lesions improved significantly after chemotherapy. Individually, CXR and SS had no or minimal agreement with FOB-based and CT-based responses. RECIST and WHO criteria had strong agreement overall (Cohen's κ = 0.872) and perfect agreement for PD (Cohen's κ = 1.000). Cohen's κvalues for FOB-based assessment with RECIST and WHO were 0.324 and 0.349, respectively for overall RE, and 0.462 and 0.501 for differentiating responders (CR and PR) from nonresponders (SD and PD), respectively. Cohen's κvalues for PD were 0.629 (FOB alone), 0.672 (FOB and RSB), 0.739 (FOB and TSB), and 0.764 (FOB and CXR).CT-based assessment should remain the reference for objective RE of chemotherapy in lung cancer. A combination of FOB and CXR may be used as a surrogate to diagnose PD if CT is not feasible.

Project description:ObjectiveChest computed tomography (CT) imaging is being increasingly used for potential lung donor assessment. However, the efficacy of CT imaging in this setting remains unknown. We hypothesize that chest CT imaging independently affects the decision-making process in donor lung utilization.MethodsWe conducted a retrospective cohort study of all adult donation after brain death donors managed through our local organ procurement organization from June 2011 to November 2016. An experienced thoracic radiologist independently reviewed donor chest CT and chest x-ray images in a blinded, standardized manner to determine the presence of structural lung disease (eg, emphysema, interstitial lung disease [ILD]) and acute abnormalities (eg, traumatic lung injury [TLI]). Distinct models of lung utilization were fit to groups with initial partial pressure of oxygen (iPaO2) ≤300 mm Hg (suboptimal) and iPaO2 >300 mm Hg (optimal).ResultsThe organ procurement organization managed 753 donors during the study period, with a lung utilization rate ([lung donors/all organ donors] × 100) of 36.5% (275 of 753). Four hundred forty-five (59.1%) donors received chest CT imaging, revealing emphysema (13.7%), ILD (2.5%), and TLI (7.2%). In univariate analysis, findings of TLI (odds ratio [OR], 2.23; 95% confidence interval [CI], 1.08-4.61) were positively associated with lung utilization, whereas findings of emphysema (OR, 0.18; CI, 0.08-0.40) were negatively associated with utilization. In multivariate analysis, CT findings of emphysema (OR, 0.21; CI 0.08-0.54) remained negatively associated with utilization. No potential donors with CT findings of ILD became lung donors. After controlling for chest x-ray findings, chest CT imaging findings of structural lung disease remained negatively associated with utilization (P = .0001). Lung utilization rate in the suboptimal and optimal iPaO2 populations was 35.1% and 41.4%, respectively, and CT findings of emphysema had a significant association with nonutilization in both groups.ConclusionsIn the evaluation of potential lung donors, chest CT imaging findings of structural lung disease, such as emphysema and ILD, have a significant negative association with lung utilization. Our findings suggest that chest CT imaging might be an important adjunct to conventional lung donor assessment criteria.

Project description:BackgroundLung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many incident lung cancers. An automated deep-learning approach based on chest radiograph images may identify more smokers at high risk for lung cancer who could benefit from screening with CT.ObjectiveTo develop and validate a convolutional neural network (CXR-LC) that predicts long-term incident lung cancer using data commonly available in the electronic medical record (EMR) (chest radiograph, age, sex, and whether currently smoking).DesignRisk prediction study.SettingU.S. lung cancer screening trials.ParticipantsThe CXR-LC model was developed in the PLCO (Prostate, Lung, Colorectal, and Ovarian) Cancer Screening Trial (n = 41 856). The final CXR-LC model was validated in additional PLCO smokers (n = 5615, 12-year follow-up) and NLST (National Lung Screening Trial) heavy smokers (n = 5493, 6-year follow-up). Results are reported for validation data sets only.MeasurementsUp to 12-year lung cancer incidence predicted by CXR-LC.ResultsThe CXR-LC model had better discrimination (area under the receiver-operating characteristic curve [AUC]) for incident lung cancer than CMS eligibility (PLCO AUC, 0.755 vs. 0.634; P < 0.001). The CXR-LC model's performance was similar to that of PLCOM2012, a state-of-the-art risk score with 11 inputs, in both the PLCO data set (CXR-LC AUC of 0.755 vs. PLCOM2012 AUC of 0.751) and the NLST data set (0.659 vs. 0.650). When compared in equal-sized screening populations, CXR-LC was more sensitive than CMS eligibility in the PLCO data set (74.9% vs. 63.8%; P = 0.012) and missed 30.7% fewer incident lung cancers. On decision curve analysis, CXR-LC had higher net benefit than CMS eligibility and similar benefit to PLCOM2012.LimitationValidation in lung cancer screening trials and not a clinical setting.ConclusionThe CXR-LC model identified smokers at high risk for incident lung cancer, beyond CMS eligibility and using information commonly available in the EMR.Primary funding sourceNone.

Dataset Information

Effect of emphysema on lung cancer risk in smokers: a computed tomography-based assessment.

Publications

Effect of emphysema on lung cancer risk in smokers: a computed tomography-based assessment.

Similar Datasets

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