Literature DB >> 25873368

LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence.

Michael W Marcus1, Ying Chen2, Olaide Y Raji2, Stephen W Duffy3, John K Field2.   

Abstract

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02-1.06], male gender (HR, 1.48; 95% CI, 1.10-1.98), smoking duration (HR, 1.04; 95% CI, 1.03-1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79-3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08-3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04-2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit χ(2) 7.58, P = 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831-0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829-0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs. ©2015 American Association for Cancer Research.

Entities:  

Mesh:

Year:  2015        PMID: 25873368     DOI: 10.1158/1940-6207.CAPR-14-0438

Source DB:  PubMed          Journal:  Cancer Prev Res (Phila)        ISSN: 1940-6215


  24 in total

Review 1.  Biomarkers of risk to develop lung cancer in the new screening era.

Authors:  Thomas Atwater; Pierre P Massion
Journal:  Ann Transl Med       Date:  2016-04

Review 2.  Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening.

Authors:  Chara E Rydzak; Samuel G Armato; Ricardo S Avila; James L Mulshine; David F Yankelevitz; David S Gierada
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

3.  Cancer Progress and Priorities: Lung Cancer.

Authors:  Matthew B Schabath; Michele L Cote
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2019-10       Impact factor: 4.254

4.  Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

Authors:  Hormuzd A Katki; Stephanie A Kovalchik; Lucia C Petito; Li C Cheung; Eric Jacobs; Ahmedin Jemal; Christine D Berg; Anil K Chaturvedi
Journal:  Ann Intern Med       Date:  2018-05-15       Impact factor: 25.391

Review 5.  Blood based biomarkers beyond genomics for lung cancer screening.

Authors:  Samir M Hanash; Edwin Justin Ostrin; Johannes F Fahrmann
Journal:  Transl Lung Cancer Res       Date:  2018-06

6.  Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening.

Authors:  Hormuzd A Katki; Stephanie A Kovalchik; Christine D Berg; Li C Cheung; Anil K Chaturvedi
Journal:  JAMA       Date:  2016-06-07       Impact factor: 56.272

7.  Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

Authors:  Lori C Sakoda; Louise M Henderson; Tanner J Caverly; Karen J Wernli; Hormuzd A Katki
Journal:  Curr Epidemiol Rep       Date:  2017-10-24

8.  A Comparison of Web-Based Cancer Risk Calculators That Inform Shared Decision-making for Lung Cancer Screening.

Authors:  Frederick R Kates; Ryan Romero; Daniel Jones; Jacqueline Egelfeld; Santanu Datta
Journal:  J Gen Intern Med       Date:  2021-04-09       Impact factor: 6.473

9.  Accounting for established predictors with the multistep elastic net.

Authors:  Elizabeth C Chase; Philip S Boonstra
Journal:  Stat Med       Date:  2019-07-17       Impact factor: 2.373

10.  Validation of the SHOX2/PTGER4 DNA Methylation Marker Panel for Plasma-Based Discrimination between Patients with Malignant and Nonmalignant Lung Disease.

Authors:  Gunter Weiss; Anne Schlegel; Denise Kottwitz; Thomas König; Reimo Tetzner
Journal:  J Thorac Oncol       Date:  2016-08-18       Impact factor: 15.609

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.