BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

314 related articles for article (PubMed ID: 26076698)

  • 1. Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort.
    Li K; Hüsing A; Sookthai D; Bergmann M; Boeing H; Becker N; Kaaks R
    Cancer Prev Res (Phila); 2015 Sep; 8(9):777-85. PubMed ID: 26076698
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.
    Ten Haaf K; Jeon J; Tammemägi MC; Han SS; Kong CY; Plevritis SK; Feuer EJ; de Koning HJ; Steyerberg EW; Meza R
    PLoS Med; 2017 Apr; 14(4):e1002277. PubMed ID: 28376113
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts.
    Tammemägi MC; Church TR; Hocking WG; Silvestri GA; Kvale PA; Riley TL; Commins J; Berg CD
    PLoS Med; 2014 Dec; 11(12):e1001764. PubMed ID: 25460915
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Risk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening: an analysis of German federal-wide survey and incidence data.
    Hüsing A; Kaaks R
    Eur J Epidemiol; 2020 Oct; 35(10):899-912. PubMed ID: 32594286
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 'Reduced' HUNT model outperforms NLST and NELSON study criteria in predicting lung cancer in the Danish screening trial.
    Røe OD; Markaki M; Tsamardinos I; Lagani V; Nguyen OTD; Pedersen JH; Saghir Z; Ashraf HG
    BMJ Open Respir Res; 2019; 6(1):e000512. PubMed ID: 31803478
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study.
    Callender T; Imrie F; Cebere B; Pashayan N; Navani N; van der Schaar M; Janes SM
    PLoS Med; 2023 Oct; 20(10):e1004287. PubMed ID: 37788223
    [TBL] [Abstract][Full Text] [Related]  

  • 7. AHRR (cg05575921) Methylation Safely Improves Specificity of Lung Cancer Screening Eligibility Criteria: A Cohort Study.
    Jacobsen KK; Schnohr P; Jensen GB; Bojesen SE
    Cancer Epidemiol Biomarkers Prev; 2022 Apr; 31(4):758-765. PubMed ID: 35064064
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Selection criteria for lung-cancer screening.
    Tammemägi MC; Katki HA; Hocking WG; Church TR; Caporaso N; Kvale PA; Chaturvedi AK; Silvestri GA; Riley TL; Commins J; Berg CD
    N Engl J Med; 2013 Feb; 368(8):728-36. PubMed ID: 23425165
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models.
    Liao W; Coupland CAC; Burchardt J; Baldwin DR; ; Gleeson FV; Hippisley-Cox J
    Lancet Respir Med; 2023 Aug; 11(8):685-697. PubMed ID: 37030308
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Applying the National Lung Screening Trial eligibility criteria to the US population: what percent of the population and of incident lung cancers would be covered?
    Pinsky PF; Berg CD
    J Med Screen; 2012 Sep; 19(3):154-6. PubMed ID: 23060474
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identifying high-risk individuals for lung cancer screening: Going beyond NLST criteria.
    Fu M; Travier N; Martín-Sánchez JC; Martínez-Sánchez JM; Vidal C; Garcia M;
    PLoS One; 2018; 13(4):e0195441. PubMed ID: 29621354
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.
    Katki HA; Kovalchik SA; Petito LC; Cheung LC; Jacobs E; Jemal A; Berg CD; Chaturvedi AK
    Ann Intern Med; 2018 Jul; 169(1):10-19. PubMed ID: 29800127
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Application of risk prediction models to lung cancer screening: a review.
    Tammemägi MC
    J Thorac Imaging; 2015 Mar; 30(2):88-100. PubMed ID: 25692785
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.
    Lu MT; Raghu VK; Mayrhofer T; Aerts HJWL; Hoffmann U
    Ann Intern Med; 2020 Nov; 173(9):704-713. PubMed ID: 32866413
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study.
    Tammemagi MC; Schmidt H; Martel S; McWilliams A; Goffin JR; Johnston MR; Nicholas G; Tremblay A; Bhatia R; Liu G; Soghrati K; Yasufuku K; Hwang DM; Laberge F; Gingras M; Pasian S; Couture C; Mayo JR; Nasute Fauerbach PV; Atkar-Khattra S; Peacock SJ; Cressman S; Ionescu D; English JC; Finley RJ; Yee J; Puksa S; Stewart L; Tsai S; Haider E; Boylan C; Cutz JC; Manos D; Xu Z; Goss GD; Seely JM; Amjadi K; Sekhon HS; Burrowes P; MacEachern P; Urbanski S; Sin DD; Tan WC; Leighl NB; Shepherd FA; Evans WK; Tsao MS; Lam S;
    Lancet Oncol; 2017 Nov; 18(11):1523-1531. PubMed ID: 29055736
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identifying high risk individuals for targeted lung cancer screening: Independent validation of the PLCO
    Weber M; Yap S; Goldsbury D; Manners D; Tammemagi M; Marshall H; Brims F; McWilliams A; Fong K; Kang YJ; Caruana M; Banks E; Canfell K
    Int J Cancer; 2017 Jul; 141(2):242-253. PubMed ID: 28249359
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation.
    Tammemagi CM; Pinsky PF; Caporaso NE; Kvale PA; Hocking WG; Church TR; Riley TL; Commins J; Oken MM; Berg CD; Prorok PC
    J Natl Cancer Inst; 2011 Jul; 103(13):1058-68. PubMed ID: 21606442
    [TBL] [Abstract][Full Text] [Related]  

  • 18. OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.
    Pan Z; Zhang R; Shen S; Lin Y; Zhang L; Wang X; Ye Q; Wang X; Chen J; Zhao Y; Christiani DC; Li Y; Chen F; Wei Y
    EBioMedicine; 2023 Feb; 88():104443. PubMed ID: 36701900
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial.
    Maisonneuve P; Bagnardi V; Bellomi M; Spaggiari L; Pelosi G; Rampinelli C; Bertolotti R; Rotmensz N; Field JK; Decensi A; Veronesi G
    Cancer Prev Res (Phila); 2011 Nov; 4(11):1778-89. PubMed ID: 21813406
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Renal-cell carcinoma risk estimates based on participants in the prostate, lung, colorectal, and ovarian cancer screening trial and national lung screening trial.
    Lotan Y; Karam JA; Shariat SF; Gupta A; Roupret M; Bensalah K; Margulis V
    Urol Oncol; 2016 Apr; 34(4):167.e9-16. PubMed ID: 26602092
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.