A Risk Prediction Tool for Melanoma?

Inter-Rater Agreement in Assessing Risk of Bias in Melanoma Prediction Studies Using the Prediction Model Risk of Bias Assessment Tool (PROBAST): Results from a Controlled Experiment on the Effect of Specific Rater Training

Assessing the risk of bias (ROB) of studies is an important part of the conduct of systematic reviews and meta-analyses in clinical medicine. Among the many existing ROB tools, the Prediction Model Risk of Bias Assessment Tool (PROBAST) is a rather new instrument specifically designed to assess the ROB of prediction studies. In our study we analyzed the inter-rater reliability (IRR) of PROBAST and the effect of specialized training on the IRR. Six raters independently assessed the risk of bias (ROB) of all melanoma risk prediction studies published until 2021 (n = 42) using the PROBAST instrument. The raters evaluated the ROB of the first 20 studies without any guidance other than the published PROBAST literature. The remaining 22 studies were assessed after receiving customized training and guidance. Gwet's AC₁ was used as the primary measure to quantify the pairwise and multi-rater IRR. Depending on the PROBAST domain, results before training showed a slight to moderate IRR (multi-rater AC₁ ranging from 0.071 to 0.535). After training, the multi-rater AC₁ ranged from 0.294 to 0.780 with a significant improvement for the overall ROB rating and two of the four domains. The largest net gain was achieved in the overall ROB rating (difference in multi-rater AC₁: 0.405, 95%-CI 0.149-0.630). In conclusion, without targeted guidance, the IRR of PROBAST is low, questioning its use as an appropriate ROB instrument for prediction studies. Intensive training and guidance manuals with context-specific decision rules are needed to correctly apply and interpret the PROBAST instrument and to ensure consistency of ROB ratings.

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

Simple Summary

The rising incidence of cutaneous melanoma over recent decades, combined with a general interest in cancer risk prediction, has led to a high number of published melanoma risk prediction models. The aim of our work was to assess the validity of these models in order to discuss the current state of knowledge about how to predict incident cutaneous melanoma. To assess the risk of bias, we used a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). Only one of the 42 studies identified was rated as having a low risk of bias. However, it was encouraging to observe a recent reduction of problematic statistical methods used in the analyses. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Abstract

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

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

Transparent and accurate reporting is essential to evaluate the validity and applicability of risk prediction models. Our aim was to evaluate the reporting quality of studies developing and validating risk prediction models for melanoma according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) checklist. We included studies that were identified by a recent systematic review and updated the literature search to ensure that our TRIPOD rating included all relevant studies. Six reviewers assessed compliance with all 37 TRIPOD components for each study using the published “TRIPOD Adherence Assessment Form”. We further examined a potential temporal effect of the reporting quality. Altogether 42 studies were assessed including 35 studies reporting the development of a prediction model and seven studies reporting both development and validation. The median adherence to TRIPOD was 57% (range 29% to 78%). Study components that were least likely to be fully reported were related to model specification, title and abstract. Although the reporting quality has slightly increased over the past 35 years, there is still much room for improvement. Adherence to reporting guidelines such as TRIPOD in the publication of study results must be adopted as a matter of course to achieve a sufficient level of reporting quality necessary to foster the use of the prediction models in applications.

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

The rising incidence of cutaneous melanoma over the past few decades has prompted substantial efforts to develop risk prediction models identifying people at high risk of developing melanoma to facilitate targeted screening programs. We review these models, regarding study characteristics, differences in risk factor selection and assessment, evaluation, and validation methods. Our systematic literature search revealed 40 studies comprising 46 different risk prediction models eligible for the review. Altogether, 35 different risk factors were part of the models with nevi being the most common one (n = 35, 78%); little consistency in other risk factors was observed. Results of an internal validation were reported for less than half of the studies (n = 18, 45%), and only 6 performed external validation. In terms of model performance, 29 studies assessed the discriminative ability of their models; other performance measures, e.g., regarding calibration or clinical usefulness, were rarely reported. Due to the substantial heterogeneity in risk factor selection and assessment as well as methodologic aspects of model development, direct comparisons between models are hardly possible. Uniform methodologic standards for the development and validation of risk prediction models for melanoma and reporting standards for the accompanying publications are necessary and need to be obligatory for that reason.

MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project

PURPOSE

Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics.

MATERIALS AND METHODS

Data were collected within an international collaboration - the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case-control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype.

RESULTS

The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36-1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7% over a base clinical model (P=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%-30%). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%).

CONCLUSION

The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype.

Prediction of Melanoma Risk in a Southern European Population Based on a Weighted Genetic Risk Score

Many single nucleotide polymorphisms (SNPs) have been described as putative risk factors for melanoma. The aim of our study was to validate the most prominent genetic risk loci in an independent Greek melanoma case-control dataset and to assess their cumulative effect solely or combined with established phenotypic risk factors on individualized risk prediction. We genotyped 59 SNPs in 800 patients and 800 controls and tested their association with melanoma using logistic regression analyses. We constructed a weighted genetic risk score (GRSGWS) based on SNPs that showed genome-wide significant (GWS) association with melanoma in previous studies and assessed their impact on risk prediction. Fifteen independent SNPs from 12 loci were significantly associated with melanoma (P < 0.05). Risk score analysis yielded an odds ratio of 1.36 per standard deviation increase of the GRSGWS (P = 1.1 × 10(-7)). Individuals in the highest 20% of the GRSGWS had a 1.88-fold increase in melanoma risk compared with those in the middle quintile. By adding the GRSGWS to a phenotypic risk model, the C-statistic increased from 0.764 to 0.775 (P = 0.007). In summary, the GRSGWS is associated with melanoma risk and achieves a modest improvement in risk prediction when added to a phenotypic risk model.

Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people

Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort.

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies

BACKGROUND

We report the development of a cutaneous melanoma risk algorithm based upon seven factors; hair color, skin type, family history, freckling, nevus count, number of large nevi, and history of sunburn, intended to form the basis of a self-assessment Web tool for the general public.

METHODS

Predicted odds of melanoma were estimated by analyzing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the distribution of the predicted odds in the controls from these studies. Imputation was used to estimate missing data in the pooled datasets. The 30th, 60th, and 90th centiles were used to distribute individuals into four risk groups for their age, sex, and geographic location. Cross-validation was used to test the robustness of the thresholds for each group by leaving out each study one by one. Performance of the model was assessed in an independent UK case-control study dataset.

RESULTS

Cross-validation confirmed the robustness of the threshold estimates. Cases and controls were well discriminated in the independent dataset [area under the curve, 0.75; 95% confidence interval (CI), 0.73-0.78]. Twenty-nine percent of cases were in the highest risk group compared with 7% of controls, and 43% of controls were in the lowest risk group compared with 13% of cases.

CONCLUSION

We have identified a composite score representing an estimate of relative risk and successfully validated this score in an independent dataset.

IMPACT

This score may be a useful tool to inform members of the public about their melanoma risk.

Skin cancer and vitamin D: an update

Exposure of the skin to solar ultraviolet (UV) radiation has both risks and benefits for human health. Absorption of UV-B radiation by DNA results in mutations that underlie the development of skin cancers, as is apparent from genetic studies showing high occurrence of UV signature mutations within these tumors. UV-B radiation is also absorbed by 7-dehydrocholesterol to initiate vitamin D synthesis. In experimental studies vitamin D metabolites enhance apoptosis of malignant cells, inhibit angiogenesis and proliferation and increase differentiation, potentially reducing skin cancer development and improving prognosis after diagnosis. There are some supporting human data. We review the links between sun exposure, vitamin D and skin cancers.

Independent validation of six melanoma risk prediction models

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".

Predicting melanoma risk: theory, practice and future challenges

The incidence of melanoma continues to rise in most fair-skinned populations. Strategies to curb the toll from melanoma include targeting the patients who are at highest risk with the aim of either preventing the onset of cancer or intervening early in order to improve survival. The challenge has been to synthesize the available information on risk factors into prediction tools with clinical utility, such that 'high-risk' patients can be identified with accuracy. While a number of risk prediction tools for melanoma have been developed, few have undergone rigorous evaluation of their performance in order to assess calibration or discrimination, and even fewer have been validated in independent populations. Future research should assess the validity of existing tools and seek to integrate the increasing volumes of data being generated by genomic studies.

Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure: impact of mole phenotype on model performance

Background

Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model.

Methods

Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)).

Results

Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups.

Conclusions

These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.

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

Background

Melanocortin-1 receptor (MC1R) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R, and externally validate these models using an independent dataset from a genetically similar melanoma population.

Methods

Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England.

Results

When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% (P = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03).

Conclusions

Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.

UVB-induced melanocyte proliferation in neonatal mice driven by CCR2-independent recruitment of Ly6c(low)MHCII(hi) macrophages

Intermittent sunburns, particularly in childhood, are the strongest environmental risk factor for malignant melanoma (MM). In mice, a single neonatal UVR exposure induces MM, whereas chronic doses to adult mice do not. Neonatal UVR alters melanocyte migration dynamics by inducing their movement upward out of hair follicles into the epidermis. UVR is known to induce inflammation and recruitment of macrophages into the skin. In this study, we have used a liposomal clodronate strategy to deplete macrophages at the time of neonatal UVR, and have shown functionally that this reduces the melanocyte proliferative response. This effect was not reproduced by depletion of CD11c-expressing populations of dendritic cells. On the basis of epidermal expression array data at various time points after UVR, we selected mouse strains defective in various aspects of macrophage recruitment, activation, and effector functions, and measured their melanocyte UVR response. We identified Ly6c(low)MHCII(hi) macrophages as the major population promoting the melanocyte response across multiple strains. The activity of this subpopulation was CCR2 (C-C chemokine receptor type 2) independent and partly IL-17 dependent. By helping induce this effect, the infiltration of specific macrophage subpopulations after sunburn may be a factor in increasing the risk of subsequent neoplastic transformation of melanocytes.

A novel primary care clinical prediction rule for early detection of osteoporosis

The effects of osteoporosis (OP) can be significantly slowed if disease is detected early. We report on a clinical risk prediction rule developed from patient histories taken in an orthopaedic outpatient clinic, before confirmatory testing for OP. Data were extracted from routine audits of consecutive records of patients with recent wrist fracture, comprising demographic details, medications, past and current disease, and fracture details. Clinical prediction rule elements were tested against clinical suspicion of OP. The clinical prediction elements comprised sex and age risk, medications that predispose patients to OP and/or falls, previous fractures and disease/medical conditions that are known OP risks. The best cut point (6.5) demonstrated 100% sensitivity with clinical suspicion of OP. Patient history information is often available before OP is clinically suspected or a definitive diagnosis is made. Our clinical prediction rule will be useful in primary care settings where objective measures of bone health are not readily available. It will raise OP awareness amongst health care providers and patients, particularly those not previously suspected of having OP. It will assist in identifying at-risk patients early and commencing them on appropriate management, without waiting for definitive bone health tests.

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

BACKGROUND

A study was carried out to describe associations of MC1R variants and melanoma in a US population and to investigate whether genetic risk is modified by pigmentation characteristics and sun exposure measures.

METHODS

Melanoma patients (n = 960) and controls (n = 396) self-reported phenotypic characteristics and sun exposure via structured questionnaire and underwent a skin examination. Logistic regression was used to estimate associations of high- and low-risk MC1R variants and melanoma, overall and within phenotypic and sun exposure strata. A meta-analysis of results from published studies was undertaken.

RESULTS

Carriage of 2 low-risk or any high-risk MC1R variants was associated with increased risk of melanoma (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.0-2.8; and OR, 2.2; 95% CI, 1.5-3.0, respectively). However, risk was stronger in or limited to individuals with protective phenotypes and limited sun exposure, such as those who tanned well after repeated sun exposure (OR, 2.4; 95% CI, 1.6-3.6), had dark hair (OR, 2.4; 95% CI, 1.5-3.6), or had dark eyes (OR, 3.2; 95% CI, 1.8-5.9). We noted this same pattern of increased melanoma risk among persons who did not freckle, tanned after exposure to first strong summer sun, reported little or average recreational or occupational sun exposure, or reported no sun burning events. Meta-analysis of published literature supported these findings.

CONCLUSIONS

These data indicate that MC1R genotypes provide information about melanoma risk in those individuals who would not be identified as high risk based on their phenotypes or exposures alone.

Epidemiology of invasive cutaneous melanoma

Data are presented on the current incidence of melanoma with recent and predicted future trends illustrating a likely continuing increase in incidence. Risk factors for developing melanoma are discussed, including current known melanoma susceptibility genes. Phenotypic markers of high-risk subjects include high counts of benign melanocytic naevi. Other risk factors considered include exposure to natural and artificial ultraviolet radiation, the effect of female sex hormones, socioeconomic status, occupation, exposure to pesticides and ingestion of therapeutic drugs including immunosuppressives and non-steroidal anti-inflammatory drugs. Aids to earlier diagnosis are considered, including public education, screening and use of equipment such as the dermatoscope. Finally, the current pattern of survival and mortality is described.

The value of screening in melanoma

The incidence of cutaneous melanoma has increased substantially in most white populations during the past several decades. Despite improvements in the early recognition of melanoma and the use of novel diagnostic techniques that enhance our diagnostic capabilities, disease-related mortality remains a significant public health issue. In the absence of effective treatment approaches for advanced disease, the best means for reducing deaths by melanoma are screening as well as professional and public education. The role of population-or community-based screening remains controversial, but evidence from self-selected screening campaigns, health care professional surveillance, and specialized pigmented lesions clinics underscores the value of screening and early detection programs, particularly in high-risk groups. Annual screening campaigns coupled with intense media promotion have become commonplace in many countries, and despite their low yield of melanoma detection, the dissemination of educational material and information to the public during these events is important in increasing public awareness. Future directions should include using screening campaigns to target middle-aged and older men and persons of lower socioeconomic status, who suffer most from the burden of the disease and its associated mortality. On a worldwide scale, comprehensive educational and screening campaigns should be implemented or intensified in underserved areas and geographic regions with lower survival rates, such as Eastern European countries. A better understanding of the biology of the disease, already occurring with notable strides, will help us to define better those individuals who will benefit most from screening and early detection efforts. Technologic advances and new diagnostic modalities will afford a more reliable and vigilant surveillance of high-risk individuals, whereas the wide use of the Internet will enhance the distribution of relevant information to the public with the ultimate goal of achieving a better control of melanoma.

Cutaneous melanoma: how does ultraviolet light contribute to melanocyte transformation?

Ascribing a causal role to ultraviolet radiation in melanoma induction is problematic, as the relationship between total lifetime sun exposure and melanoma risk is not as strong as for some other skin cancers. Epidemiological studies show that heightened melanoma risk is most associated with intermittent sunburns. Despite this, lesions can develop on anatomical locations receiving intermittent (e.g., the trunk) or chronic exposures (e.g., the head and neck). Individuals developing melanoma on truncal sites tend to have more nevi, suggesting that in addition to the differences in forms of sun exposure, there may also be innate variation that makes one more susceptible to one or other mechanism of melanoma development. Such differences may depend upon different responses at the time of exposure (e.g., pigmentation characteristics, DNA repair capability and melanocyte proliferative response), and/or the role of the skin microenvironment in limiting proliferation of a ‘primed’ or mutated melanocyte during the latent period leading up to the appearance of a melanocytic lesion.

Screening, early detection, and trends for melanoma: Current status (2000-2006) and future directions

In the past 5 years, there have been notable strides toward the earlier recognition and discovery of melanoma, including new technologies to complement and augment the clinical examination and new insights to help clinicians recognize early melanoma. However, incidence and mortality rates throughout most of the developed world have risen over the past 25 years, while education and screening, potentially the best means for reducing the disease, continue to be severely underutilized. Much progress needs to be made to reach middle-aged and older men and persons of lower socioeconomic status who suffer a disproportionate burden of death from melanoma. Worldwide melanoma control must also be a priority, and comprehensive educational and screening programs should be directed to Northern Ireland and a number of Eastern European nations, whose 5-year survival rates range between 53% and 60%, mirroring those of the United States and Australia more than 40 years ago.

LEARNING OBJECTIVE

After completing this learning activity, participants should be aware of the most recent melanoma epidemiologic data, both in the United States and internationally; worldwide early detection and screening programs; clinical strategies to recognize and improve the detection of early melanoma; the latest technologies for early detection of melanoma; and public and professional education programs designed to enhance early detection.

Lung cancer risk prediction: a tool for early detection

Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected. For some cancers, substantial progress in individual risk estimation has already been made. However, relatively few models have been developed to predict lung cancer risk beyond effects of age and smoking. This paper reviews published models for lung cancer risk prediction, discusses their potential contribution to clinical and research settings and suggests improvements to the risk modeling strategy for lung cancer. The sensitivity and specificity of existing cancer risk models is less than optimal. Improvement in individual risk prediction is important for selection of individuals for prevention or early detection interventions. In addition to smoking, factors related to occupational exposure, personal medical history and family history of cancer can add to the predictive power. A good risk prediction model is one that can identify a small fraction of the population in which a large proportion of the disease cases will occur. In the future, genetic and other biological markers are likely to be useful, although they will require rigorous evaluation. Validation is essential to establish the predictive effect and for ongoing monitoring of the model's continued relevance.

Melanocortin 1 receptor variants and skin cancer risk

Melanocortin 1 receptor (MC1R) gene variants are associated with red hair and fair skin color. We assessed the associations of common MC1R genotypes with the risks of 3 types of skin cancer simultaneously in a nested case-control study within the Nurses' Health Study (219 melanoma, 286 squamous cell carcinoma (SCC), and 300 basal cell carcinoma (BCC) cases, and 873 controls). We found that the 151Cys, 160Trp and 294His variants were significantly associated with red hair, fair skin color and childhood tanning tendency. The MC1R variants, especially the 151Cys variant, were associated with increased risks of the 3 types of skin cancer, after controlling for hair color, skin color and other skin cancer risk factors. Carriers of the 151Cys variant had an OR of 1.65 (95% CI, 1.04-2.59) for melanoma, 1.67 (1.12-2.49) for SCC and 1.56 (1.03-2.34) for BCC. Women with medium or olive skin color carrying 1 nonred hair color allele and 1 red hair color allele had the highest risk of melanoma. A similar interaction pattern was observed for red hair and carrying at least 1 red hair color allele on melanoma risk. We also observed that the 151Cys variant contributed additional melanoma risk among red-haired women. The information on MC1R status modestly improved the risk prediction; the increase was significant for melanoma and BCC (p, 0.004 and 0.05, respectively). These findings indicated that the effects of the MC1R variants on skin cancer risk were independent from self-reported phenotypic pigmentation.