scholarly journals Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model

2016 ◽  
Vol 89 (1060) ◽  
pp. 20160016 ◽  
Author(s):  
Henry Zhao ◽  
Henry M Marshall ◽  
Ian A Yang ◽  
Rayleen V Bowman ◽  
John Ayres ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Pulmonary Nodules ◽  
Risk Prediction Model ◽  
Long Term Follow Up

Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists

CHEST Journal ◽  
2019 ◽  
Vol 156 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Heber MacMahon ◽  
Feng Li ◽  
Yulei Jiang ◽  
Samuel G. Armato
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Risk Prediction Model

scholarly journals The Cytokinesis-Blocked Micronucleus Assay as a Strong Predictor of Lung Cancer: Extension of a Lung Cancer Risk Prediction Model

2014 ◽  
Vol 23 (11) ◽  
pp. 2462-2470 ◽  
Author(s):  
Randa A. El-Zein ◽  
Mirtha S. Lopez ◽  
Anthony M. D'Amelio ◽  
Mei Liu ◽  
Reginald F. Munden ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Strong Predictor ◽  
Micronucleus Assay ◽  
Risk Prediction Model

scholarly journals Individual 5-Year Lung Cancer Risk Prediction Model in Korea Using a Nationwide Representative Database

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3496
Author(s):  
Yohwan Yeo ◽  
Dong Wook Shin ◽  
Kyungdo Han ◽  
Sang Hyun Park ◽  
Keun-Hye Jeon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Lung Cancer Screening ◽  
Risk Prediction Model ◽  
C Statistic ◽  
The Mean

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801–0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.

scholarly journals Development and Validation of a Lung Cancer Risk Prediction Model for African-Americans

2008 ◽  
Vol 1 (4) ◽  
pp. 255-265 ◽  
Author(s):  
Carol J. Etzel ◽  
Sumesh Kachroo ◽  
Mei Liu ◽  
Anthony D'Amelio ◽  
Qiong Dong ◽  
...  
Keyword(s):  
Lung Cancer ◽  
African Americans ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Risk Prediction Model ◽  
Development And Validation

scholarly journals Connective Tissue-Activating Peptide III: A Novel Blood Biomarker for Early Lung Cancer Detection

2009 ◽  
Vol 27 (17) ◽  
pp. 2787-2792 ◽  
Author(s):  
John Yee ◽  
Marianne D. Sadar ◽  
Don D. Sin ◽  
Michael Kuzyk ◽  
Li Xing ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Connective Tissue ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Risk Prediction Model ◽  
Blood Biomarkers ◽  
Blood Samples ◽  
Arterial Blood

PurposeThere are no reliable blood biomarkers to detect early lung cancer. We used a novel strategy that allows discovery of differentially present proteins against a complex and variable background.MethodsMass spectrometry analyses of paired pulmonary venous-radial arterial blood from 16 lung cancer patients were applied to identify plasma proteins potentially derived from the tumor microenvironment. Two differentially expressed proteins were confirmed in 64 paired venous-arterial blood samples using an immunoassay. Twenty-eight pre- and postsurgical resection peripheral blood samples and two independent, blinded sets of plasma from 149 participants in a lung cancer screening study (49 lung cancers and 100 controls) and 266 participants from the National Heart Lung and Blood Institute Lung Health Study (45 lung cancer and 221 matched controls) determined the accuracy of the two protein markers to detect subclinical lung cancer.ResultsConnective tissue-activating peptide III (CTAP III)/ neutrophil activating protein-2 (NAP-2) and haptoglobin were identified to be significantly higher in venous than in arterial blood. CTAP III/NAP-2 levels decreased after tumor resection (P = .01). In two independent population cohorts, CTAP III/NAP-2 was significantly associated with lung cancer and improved the accuracy of a lung cancer risk prediction model that included age, smoking, lung function (FEV1), and an interaction term between FEV1and CTAP III/NAP-2 (area under the curve, 0.84; 95% CI, 0.77 to 0.91) compared to CAPIII/NAP-2 alone.ConclusionWe identified CTAP III/NAP-2 as a novel biomarker to detect preclinical lung cancer. The study underscores the importance of applying blood biomarkers as part of a multimodal lung cancer risk prediction model instead of as stand-alone tests.

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