Literature DB >> 15900041

Cancer risk prediction models: a workshop on development, evaluation, and application.

Andrew N Freedman1, Daniela Seminara, Mitchell H Gail, Patricia Hartge, Graham A Colditz, Rachel Ballard-Barbash, Ruth M Pfeiffer.   

Abstract

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Entities:  

Mesh:

Year:  2005        PMID: 15900041     DOI: 10.1093/jnci/dji128

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  101 in total

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Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

2.  Effects of personalized colorectal cancer risk information on laypersons' interest in colorectal cancer screening: The importance of individual differences.

Authors:  Paul K J Han; Christine W Duarte; Susannah Daggett; Andrea Siewers; Bill Killam; Kahsi A Smith; Andrew N Freedman
Journal:  Patient Educ Couns       Date:  2015-07-19

3.  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network.

Authors:  Panayiotis Petousis; Simon X Han; Denise Aberle; Alex A T Bui
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4.  Copula based prediction models: an application to an aortic regurgitation study.

Authors:  Pranesh Kumar; Mohamed M Shoukri
Journal:  BMC Med Res Methodol       Date:  2007-06-16       Impact factor: 4.615

Review 5.  Risk assessment models to estimate cancer probabilities.

Authors:  Constance M Johnson; Derek Smolenski
Journal:  Curr Oncol Rep       Date:  2007-11       Impact factor: 5.075

6.  Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Authors:  Merlise A Clyde; Rachel Palmieri Weber; Edwin S Iversen; Elizabeth M Poole; Jennifer A Doherty; Marc T Goodman; Roberta B Ness; Harvey A Risch; Mary Anne Rossing; Kathryn L Terry; Nicolas Wentzensen; Alice S Whittemore; Hoda Anton-Culver; Elisa V Bandera; Andrew Berchuck; Michael E Carney; Daniel W Cramer; Julie M Cunningham; Kara L Cushing-Haugen; Robert P Edwards; Brooke L Fridley; Ellen L Goode; Galina Lurie; Valerie McGuire; Francesmary Modugno; Kirsten B Moysich; Sara H Olson; Celeste Leigh Pearce; Malcolm C Pike; Joseph H Rothstein; Thomas A Sellers; Weiva Sieh; Daniel Stram; Pamela J Thompson; Robert A Vierkant; Kristine G Wicklund; Anna H Wu; Argyrios Ziogas; Shelley S Tworoger; Joellen M Schildkraut
Journal:  Am J Epidemiol       Date:  2016-10-03       Impact factor: 4.897

7.  Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines.

Authors:  K Vuong; B K Armstrong; M Drummond; J L Hopper; J H Barrett; J R Davies; D T Bishop; J Newton-Bishop; J F Aitken; G G Giles; H Schmid; M A Jenkins; G J Mann; K McGeechan; A E Cust
Journal:  Br J Dermatol       Date:  2019-09-22       Impact factor: 9.302

8.  Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

Authors:  Katrina Armstrong; Elizabeth A Handorf; Jinbo Chen; Mirar N Bristol Demeter
Journal:  Am J Prev Med       Date:  2013-01       Impact factor: 5.043

9.  Hip bone density predicts breast cancer risk independently of Gail score: results from the Women's Health Initiative.

Authors:  Zhao Chen; Leslie Arendell; Mikel Aickin; Jane Cauley; Cora E Lewis; Rowan Chlebowski
Journal:  Cancer       Date:  2008-09-01       Impact factor: 6.860

10.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

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