Literature DB >> 16728488

Identifying individuals at high risk of melanoma: a practical predictor of absolute risk.

Thomas R Fears1, DuPont Guerry, Ruth M Pfeiffer, Richard W Sagebiel, David E Elder, Allan Halpern, Elizabeth A Holly, Patricia Hartge, Margaret A Tucker.   

Abstract

PURPOSE: We developed a model to estimate the 5-year absolute risk of melanoma to efficiently identify individuals at increased risk of melanoma for potential interventions. PATIENTS AND METHODS: We used data from a case-control study with 718 non-Hispanic white patients with invasive cutaneous melanoma from melanoma clinics in Philadelphia, PA and San Francisco, CA; matched controls were 945 patients from outpatient clinics with similar catchment areas. All participants underwent extensive interviews and skin examinations. We selected easily obtained clinical characteristics and responses to simple questions for study in order to develop sex-specific relative risk models. These models were combined with incidence and mortality rates by United States geographic areas to develop estimates of the absolute risk of developing melanoma within 5 years.
RESULTS: Relative risk models yielded an attributable risk of 86% for men and 89% for women, using at most seven variables. Attributable risks did not vary by age, ultraviolet B flux or hours outdoors. The absolute individual risks varied widely, depending on age, other host characteristics, and geographic area. Individual absolute risk can be estimated using a program available online.
CONCLUSION: Our procedures allow for estimating the absolute risk of developing melanoma to assist in the identification of patients at high risk. Such high-risk individuals could undergo interventions including a complete skin examination, counseling to avoid sun exposures, regular self and professional surveillance, or participation in prevention trials. It is important to emphasize that these projections are not intended to identify current melanoma cases.

Entities:  

Mesh:

Year:  2006        PMID: 16728488     DOI: 10.1200/JCO.2005.04.1277

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  36 in total

1.  Does MC1R genotype convey information about melanoma risk beyond risk phenotypes?

Authors:  Peter A Kanetsky; Saarene Panossian; David E Elder; DuPont Guerry; Michael E Ming; Lynn Schuchter; Timothy R Rebbeck
Journal:  Cancer       Date:  2010-05-15       Impact factor: 6.860

2.  A risk prediction model for smoking experimentation in Mexican American youth.

Authors:  Rajesh Talluri; Anna V Wilkinson; Margaret R Spitz; Sanjay Shete
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-07-25       Impact factor: 4.254

3.  Regulatory approval of cancer risk-reducing (chemopreventive) drugs: moving what we have learned into the clinic.

Authors:  Frank L Meyskens; Gregory A Curt; Dean E Brenner; Gary Gordon; Ronald B Herberman; Olivera Finn; Gary J Kelloff; Samir N Khleif; Caroline C Sigman; Eva Szabo
Journal:  Cancer Prev Res (Phila)       Date:  2011-03

Review 4.  Predicting melanoma risk: theory, practice and future challenges.

Authors:  David Whiteman
Journal:  Melanoma Manag       Date:  2014-12-04

5.  Randomized trial of tailored skin cancer prevention for children: the Project SCAPE family study.

Authors:  Karen Glanz; Alana D Steffen; Elinor Schoenfeld; Karyn A Tappe
Journal:  J Health Commun       Date:  2013-06-27

6.  Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma.

Authors:  Fangyi Gu; Ting-Huei Chen; Ruth M Pfeiffer; Maria Concetta Fargnoli; Donato Calista; Paola Ghiorzo; Ketty Peris; Susana Puig; Chiara Menin; Arcangela De Nicolo; Monica Rodolfo; Cristina Pellegrini; Lorenza Pastorino; Evangelos Evangelou; Tongwu Zhang; Xing Hua; Curt T DellaValle; D Timothy Bishop; Stuart MacGregor; Mark I Iles; Matthew H Law; Anne Cust; Kevin M Brown; Alexander J Stratigos; Eduardo Nagore; Stephen Chanock; Jianxin Shi; Melanoma Meta-Analysis Consortium; MelaNostrum Consortium; Maria Teresa Landi
Journal:  Hum Mol Genet       Date:  2018-12-01       Impact factor: 6.150

7.  Perceived risk following melanoma genetic testing: a 2-year prospective study distinguishing subjective estimates from recall.

Authors:  Lisa G Aspinwall; Jennifer M Taber; Wendy Kohlmann; Samantha L Leaf; Sancy A Leachman
Journal:  J Genet Couns       Date:  2013-12-10       Impact factor: 2.537

8.  Incidence of childhood and adolescent melanoma in the United States: 1973-2009.

Authors:  Jeannette R Wong; Jenine K Harris; Carlos Rodriguez-Galindo; Kimberly J Johnson
Journal:  Pediatrics       Date:  2013-04-15       Impact factor: 7.124

9.  Comparison of discriminatory power and accuracy of three lung cancer risk models.

Authors:  A M D'Amelio; A Cassidy; K Asomaning; O Y Raji; S W Duffy; J K Field; M R Spitz; D Christiani; C J Etzel
Journal:  Br J Cancer       Date:  2010-06-29       Impact factor: 7.640

10.  Cancer risk assessment for the primary care physician.

Authors:  Larissa A Korde; Shahinaz M Gadalla
Journal:  Prim Care       Date:  2009-09       Impact factor: 2.907

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