ArticleThe Journal of investigative dermatology2015
Independent validation of six melanoma risk prediction models.
Article in The Journal of investigative dermatology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma.Human molecular genetics · 2018Pooled it
- Screening and novel diagnostic technologies for melanoma: an update.Melanoma management · 2025Review
- Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies.Cancers · 2022Article
- The Experience of 3D Total-Body Photography to Monitor Nevi: Results From an Australian General Population-Based Cohort Study.JMIR dermatology · 2022Article
- Nanoparticle-Based Combination Therapy for Melanoma.Frontiers in oncology · 2022Review
- Disease risk scores for skin cancers.Nature communications · 2021Observational
- Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines.The British journal of dermatology · 2020Article
- Article
- 'Mind your Moles' study: protocol of a prospective cohort study of melanocytic naevi.BMJ open · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying people at high risk of melanoma is important for targeted prevention activities and surveillance. Several tools have been developed to classify melanoma risk, but few have been independently validated. We assessed the discriminatory performance of six melanoma prediction tools by applying them to individuals from two independent data sets, one comprising 762 melanoma cases and the second a population-based sample of 42,116 people without melanoma. We compared the model predictions with actual melanoma status to measure sensitivity and specificity. The performance of the models was variable with sensitivity ranging from 97.7 to 10.5% and specificity from 99.6 to 1.3%. The ability of all the models to discriminate between cases and controls, however, was generally high. The model developed by MacKie et al. (1989) had higher sensitivity and specificity for men (0.89 and 0.88) than women (0.79 and 0.72). The tool developed by Cho et al. (2005) was highly specific (men, 0.92; women, 0.99) but considerably less sensitive (men, 0.64; women, 0.37). Other models were either highly specific but lacked sensitivity or had low to very low specificity and higher sensitivity. Poor performance was partly attributable to the use of non-standardized assessment items and various differing interpretations of what constitutes "high risk".
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.