Literature DB >> 21399503

Development of an individual score for melanoma risk.

Gaelle Quéreux1, Dominique Moyse, Yves Lequeux, Olivier Jumbou, Anabelle Brocard, Daniel Antonioli, Brigitte Dréno, Jean-Michel Nguyen.   

Abstract

Melanoma is one of the fastest growing cancers worldwide. We need to have tools to identify patients with high risk of melanoma. We carried out a case-control study and tested three methods to develop an individual score of melanoma risk, usable in routine practice. All cases included newly diagnosed invasive cutaneous melanoma of stage I or II (6th American Joint Committee on Cancer) seen in 2007 at the Skin Cancer Unit of Nantes Hospital, France. Controls included 1500 consecutive patients consulting their general practitioners. A self-administrated questionnaire was used for assessment of melanoma risk factors. Three methods of scoring were used and compared: one with common relative risks reported in the literature, one with odds ratios estimated by logistic regression, and a combinatorial analysis. The method based on combinatorial analysis permitted one to obtain a simple rule to define individuals at risk: the association of the rule 'presence of at least three risk factors or presence of more than 20 naevi on the arms' for the patients aged under 60 years and 'presence of at least three risk factors or presence of freckles' for the patients aged 60 years and above (sensitivity: 63.2% and specificity: 68.8%). The tool we propose is easy to use every day in routine health care to select patients with high risk of melanoma. It can be assessed without any computer or calculator and is based on the self-assessment of the melanoma risk factors by the patient and thus is not medical time consuming.

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Mesh:

Year:  2011        PMID: 21399503     DOI: 10.1097/CEJ.0b013e32834474ae

Source DB:  PubMed          Journal:  Eur J Cancer Prev        ISSN: 0959-8278            Impact factor:   2.497


  10 in total

Review 1.  Predicting melanoma risk: theory, practice and future challenges.

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

2.  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

3.  Targeted melanoma prevention intervention: a cluster randomized controlled trial.

Authors:  Cédric Rat; Gaelle Quereux; Christelle Riviere; Sophie Clouet; Rémy Senand; Christelle Volteau; Brigitte Dreno; Jean-Michel Nguyen
Journal:  Ann Fam Med       Date:  2014 Jan-Feb       Impact factor: 5.166

4.  Independent validation of six melanoma risk prediction models.

Authors:  Catherine M Olsen; Rachel E Neale; Adèle C Green; Penelope M Webb; David C Whiteman
Journal:  J Invest Dermatol       Date:  2014-12-30       Impact factor: 8.551

5.  Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies.

Authors:  Isabelle Kaiser; Sonja Mathes; Annette B Pfahlberg; Wolfgang Uter; Carola Berking; Markus V Heppt; Theresa Steeb; Katharina Diehl; Olaf Gefeller
Journal:  Cancers (Basel)       Date:  2022-06-20       Impact factor: 6.575

6.  Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement.

Authors:  Isabelle Kaiser; Katharina Diehl; Markus V Heppt; Sonja Mathes; Annette B Pfahlberg; Theresa Steeb; Wolfgang Uter; Olaf Gefeller
Journal:  Healthcare (Basel)       Date:  2022-01-26

7.  Proposal for an annual skin examination by a general practitioner for patients at high risk for melanoma: a French cohort study.

Authors:  Cédric Rat; Charlotte Grimault; Gaelle Quereux; Maelenn Dagorne; Aurélie Gaultier; Amir Khammari; Brigitte Dreno; Jean-Michel Nguyen
Journal:  BMJ Open       Date:  2015-07-29       Impact factor: 2.692

8.  MC1R genotype as a predictor of early-onset melanoma, compared with self-reported and physician-measured traditional risk factors: an Australian case-control-family study.

Authors:  Anne E Cust; Chris Goumas; Kylie Vuong; John R Davies; Jennifer H Barrett; Elizabeth A Holland; Helen Schmid; Chantelle Agha-Hamilton; Bruce K Armstrong; Richard F Kefford; Joanne F Aitken; Graham G Giles; D Bishop; Julia A Newton-Bishop; John L Hopper; Graham J Mann; Mark A Jenkins
Journal:  BMC Cancer       Date:  2013-09-04       Impact factor: 4.430

9.  Genetic analysis of melanocortin 1 receptor red hair color variants in a Russian population of Eastern Siberia.

Authors:  Anna V Motorina; Nadezhda V Palkina; Anna V Komina; Tatiana G Ruksha; Ivan P Artyukhov; Vasily V Kozlov
Journal:  Eur J Cancer Prev       Date:  2018-03       Impact factor: 2.497

10.  Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation.

Authors:  Isabelle Kaiser; Annette B Pfahlberg; Wolfgang Uter; Markus V Heppt; Marit B Veierød; Olaf Gefeller
Journal:  Int J Environ Res Public Health       Date:  2020-10-28       Impact factor: 3.390

  10 in total

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