Literature DB >> 9835458

Instrumental measurement of skin colour and skin type as risk factors for melanoma: a statistical classification procedure.

P Barbini1, G Cevenini, P Rubegni, M R Massai, M L Flori, P Carli, L Andreassi.   

Abstract

A statistical procedure to evaluate melanoma risk in Caucasian subjects on the basis of colorimetric measurement of skin colour and Fitzpatrick phototype is described. One hundred and sixty melanoma patients and 546 randomized healthy subjects of similar age, sex and place of origin were examined in the same period for skin colour using a tristimulus colorimeter and for Fitzpatrick phototype. A clinical score for classification purposes was obtained by statistical discriminant analysis with multivariate data transformation and dimension reduction techniques. A Fisher linear classifier was chosen for its simplicity and robustness in correctly predicting melanoma risk in new subjects. The classification rule was designed to avoid classifying subjects at high risk for melanoma as low risk, i.e. to give a negligible number of false negatives at the expense of more false positives. The procedure is objective and readily adapted to different clinical requirements. This is only a preliminary study but it is hoped that by performing more complex statistical analyses, e.g. neural networks, and adding other parameters (proven risk factors such as number of naevi) the performance will be further improved.

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Year:  1998        PMID: 9835458     DOI: 10.1097/00008390-199810000-00009

Source DB:  PubMed          Journal:  Melanoma Res        ISSN: 0960-8931            Impact factor:   3.599


  8 in total

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

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

2.  Pharmacologic induction of epidermal melanin and protection against sunburn in a humanized mouse model.

Authors:  Alexandra Amaro-Ortiz; Jillian C Vanover; Timothy L Scott; John A D'Orazio
Journal:  J Vis Exp       Date:  2013-09-07       Impact factor: 1.355

3.  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

4.  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

5.  Comparison of logistic and Bayesian classifiers for evaluating the risk of femoral neck fracture in osteoporotic patients.

Authors:  D Testi; A Cappello; L Chiari; M Viceconti; S Gnudi
Journal:  Med Biol Eng Comput       Date:  2001-11       Impact factor: 3.079

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.  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

8.  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

  8 in total

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