Sensitivity of two Australian melanoma risk tools to identify high-risk individuals among people presenting with their first primary melanoma.
high-risk
melanoma
prediction
risk
tool
Journal
The Australasian journal of dermatology
ISSN: 1440-0960
Titre abrégé: Australas J Dermatol
Pays: Australia
ID NLM: 0135232
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
revised:
08
03
2022
received:
25
12
2021
accepted:
26
03
2022
pubmed:
7
5
2022
medline:
17
8
2022
entrez:
6
5
2022
Statut:
ppublish
Résumé
Regular skin examinations for early detection of melanoma are recommended for high-risk individuals, but there is minimal consensus regarding what constitutes 'high-risk'. Melanoma risk prediction models may guide this. We compared two online melanoma risk prediction tools: Victorian Melanoma Service (VMS) and Melanoma Institute Australia (MIA) risk tools; to assess classification differences of patients at high-risk of a first primary melanoma. Risk factor data for 357 patients presenting with their first primary melanoma were entered into both risk tools. Predicted risks were recorded: 5-year absolute risk (VMS tool and MIA tool); 10-year, lifetime, and relative risk estimates (MIA tool). Sensitivities for each tool were calculated using the same high-risk thresholds. The MIA risk tool showed greater sensitivity on comparison of 5-year absolute risks (90% MIA vs 78% VMS). Patients had significantly higher odds of being classified as high or very-high risk using the MIA risk tool overall, and for each patient subgroup. Using either tool, patients of male gender or with synchronous multiple first primary melanomas were more likely to be correctly classified as high- or very-high risk using 5-year absolute risk thresholds; but tumour invasiveness was unrelated to risk. Classification differed when using the MIA risk categories based on relative risk. Both melanoma risk prediction tools had high sensitivity for identifying individuals at high-risk and could be used for optimising prevention campaigns. The choice of which risk tool, measure, and threshold for risk stratification depends on the intended purpose of risk prediction, and ideally requires information on specificity.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
352-358Subventions
Organisme : National Health and Medical Research Council
ID : NHMRC Centre of Research Excellence #1135285
Organisme : National Health and Medical Research Council
ID : NHMRC Early Career Fellowship grant #1160757
Organisme : National Health and Medical Research Council
ID : NHMRC Investigator Grant #2008454
Informations de copyright
© 2022 The Australasian College of Dermatologists.
Références
Geller AC, Elwood M, Swetter SM, et al. Factors related to the presentation of thin and thick nodular melanoma from a population-based cancer registry in Queensland Australia. Cancer. 2009;115:1318-27.
Keung EZ, Gershenwald JE. The eighth edition American Joint Committee on Cancer (AJCC) melanoma staging system: implications for melanoma treatment and care. Expert. Rev. Anticancer. Ther. 2018;18:775-84.
Watts CG, McLoughlin K, Goumas C, et al. Association Between Melanoma Detected During Routine Skin Checks and Mortality. JAMA Dermatol. 2021;157:1425-36.
Cancer Council Australia's National Skin Cancer Committee. Position Statement - Early detection of skin cancer [internet] Australia: Cancer Council Australia; 2019 [Available from: https://wiki.cancer.org.au/policy/Position_statement_-_Screening_and_early_detection_of_skin_cancer.
Marsden JR, Newton-Bishop JA, Burrows L, et al. Revised U.K. guidelines for the management of cutaneous melanoma 2010. Br. J. Dermatol. 2010;163:238-56.
Vuong K, Armstrong BK, Weiderpass E, et al. Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors. JAMA Dermatol. 2016;152:889-96.
Mar V, Wolfe R, Kelly JW. Predicting melanoma risk for the Australian population. Australas. J. Dermatol. 2011;52:109-16.
Olsen CM, Pandeya N, Thompson BS, et al. Risk Stratification for Melanoma: Models Derived and Validated in a Purpose-Designed Prospective Cohort. J. Natl. Cancer Inst. 2018;110:1075-83.
Kaiser I, Pfahlberg AB, Uter W, et al. Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation. Int. J. Environ. Res. Public Health. 2020;17:7919.
Johnston BC, Alonso-Coello P, Friedrich JO, et al. Do clinicians understand the size of treatment effects? A randomized survey across 8 countries. CMAJ. 2016;188:25-32.
Bian J, Weir C, Unni P, et al. Interactive visual displays for interpreting the results of clinical trials: formative evaluation with case vignettes. J. Med. Internet Res. 2018;20:e10507.
Bell NR, Dickinson JA, Grad R, et al. Understanding and communicating risk: Measures of outcome and the magnitude of benefits and harms. Can. Fam. Physician. 2018;64:181-5.
Youlden DR, Youl PH, Soyer HP, et al. Distribution of subsequent primary invasive melanomas following a first primary invasive or in situ melanoma Queensland, Australia, 1982-2010. JAMA Dermatol. 2014;150:526-34.
Cust AE, Badcock C, Smith J, et al. A risk prediction model for the development of subsequent primary melanoma in a population-based cohort. Br. J. Dermatol. 2020;182:1148-57.
Rayner JE, Laino AM, Nufer KL, et al. Clinical Perspective of 3D Total Body Photography for Early Detection and Screening of Melanoma. Front Med (Lausanne). 2018;5:152.