We used data from a Washington State melanoma case-control study with self-assessed risk factors to develop a melanoma risk score. For white persons ages 35–74, we found that the most predictive risk factors were male sex, older age, higher number of severe sunburns between ages 2–18, lighter natural hair color at age 15, higher density of freckles on the arms before age 20, higher number of raised moles on both arms, and prior non-melanoma skin cancer. The validated AUC of 0.70 indicates that the model predicts melanoma moderately well. Screening for melanoma in the top 15% risk category(with full body skin examination by a dermatologist, primary care physician, or other trained health care professional, for example) could capture a relatively high proportion of melanomas (up to 50% if the screening examination was highly sensitive). The one-year PPV, the proportion of persons with a high-risk score who would be expected to develop melanoma in the next year, was low, since melanoma is a rare event despite being the sixth most common cancer in the U.S. in 2004–2008 [30
]. However, the PPV was naturally higher when considering a longer follow-up period of 5 years. The PPV also increased when the risk score was applied to a higher risk population (individuals age 50 or older). The five-year PPV was nearly 1% in the top 15% risk among persons over age 50, so applying the risk score to individuals over 50 could increase the melanoma yield while minimizing the cost and clinical burden of unnecessary screening.
We are aware of only two other comparable self-assessed melanoma risk prediction models derived from case-control or cohort study data [7
], and our model compares favorably to them. One of these studies [11
] used prospective data from three large cohort studies (the Nurses’ Health Studies I and II and the Health Professionals Follow-Up Study) with 535 incident cases of invasive melanoma. The final model included age, male sex, family history of melanoma, number of moles larger than 3 mm on arms or lower legs, and hair color. The AUC was 0.62, which was lower than our AUC of 0.70 and may be an overestimate, as it was not clearly validated using data separate from that used to build the model. The sensitivity was 23% in the top 10% risk and 38% in the top 20% risk participants, which is lower than our sensitivities at those cutoffs, 42% and 61% respectively. The other study [7
] used data from a partly clinic-based case-control study in Austria and included 185 invasive melanomas and 17 in situ melanomas. It was age- and-sex-matched, and risk factors in the final model included Fitzpatrick skin phototype (I–IV), skin damage related to solar radiation (absent, moderate, or severe), and total number of nevi (0–5, 6–10, 11–25, 26–50, or >50). The AUC was 0.73, but it may be an overestimate as it was not clearly validated and is lower than the AUC on our training set of 0.77. The sensitivity was 42% for the top 10% risk, which is identical to our findings. One other self-assessed model is difficult to compare to our study due to differences in methods and no reported AUC or sensitivity for high-risk cutoffs [20
]. There are several self-assessed melanoma risk factor questionnaires [6
], but they were not derived from statistical models, so we are unable to know how well these questionnaires predict melanoma [32
There have been several melanoma risk prediction models that used clinician-assessed risk factors such as number of total nevi, number of atypical nevi, or sun damage on the back [9
]. These types of risk assessments are conceptually different from ours, because a clinic visit with a dermatologist or primary care provider (which is costly and time-consuming) is necessary to determine the risk level, while our risk score can be calculated by a telephone interview or potentially by a self-assessed written questionnaire. Guther et al. created a model to predict invasive or in situ melanoma from a prospective cohort of patients who underwent free total-body skin examination by a dermatologist as part of Germany’s mass skin cancer screening program [41
]. Variables in the final melanoma prediction model included age, red or blond hair color, past history of melanoma, and suspicious melanocytic lesion on dermoscopy with an AUC of 0.86 (with validation by bootstrapping methodology) and unknown sensitivity of the model for melanoma in the high-risk group (since melanoma and squamous cell carcinoma models were combined for the risk stratification component). The model by Fortes et al. was age- and sex-matched and included in the final model total number of nevi as counted by a dermatologist [42
], hair color, skin color, presence of freckles, and history of at least one sunburn in childhood [40
]. The model was derived from an Italian case-control study and validated in a Brazilian population with an AUC of 0.79, and the sensitivity was 49.9% for the top 12.5% risk, which is similar to our findings. English et al. included number of raised moles on arms as assessed by a study nurse, age on arrival in Australia, history of non-melanoma skin cancer, mean hours per week spent outdoors in the summer between ages 10–24, and family history of melanoma [9
]. No AUC was given, but the sensitivity was 54% in the top 16% risk, which is again similar to our findings. Other models with provider-assessed risk factors are difficult to compare to our study, as they did not calculate AUC and the sensitivity of high-risk cutoffs [33
The number of nevi on the arm is a strong predictor of melanoma, when counted by an examiner [20
] and when self-assessed [18
]. There is evidence that nevus counts on the arms are representative of total body nevi [18
] and that self-counting of arm nevi is reliable when compared to examination by a dermatologist [14
]. Focusing on this limited and easily accessible part of the body is ideal for population-based risk assessment and may improve compliance with and precision of self-counting of nevi [18
]. Limiting the counts to raised moles can prevent the inclusion of freckles or solar lentigines [18
], but there is the potential for misclassification due to the counting as nevi of seborrheic keratoses, which are common on the arms in older individuals [50
]. Our study included individuals up to age 75, and the number of nevi on the arms was still a very strong risk factor for melanoma (OR 2.93 for 3 or more compared to none), despite this possibility of misclassification.
In our study, the association of melanoma with self-assessed freckle density was strong (OR 2.69 for “a lot” of freckles compared to none), with a positive dose-response relationship(increasing odds of melanoma with increasing density of freckles). These two factors support the validity of self-assessed freckle density as a melanoma risk factor. Further support is provided by other studies, which have also shown a strong association between self-assessed freckle density and melanoma [19
] with a dose-response relationship [20
Our study has several strengths. It was a large population-based study. We used well-validated risk factors that were self-assessed, making our score easy to use in a broad array of settings. The predictive ability as measured by the AUC is strong compared to other melanoma models and other cancer prediction models. (For example, the AUC range is 0.58–0.64 for breast cancer [51
] and 0.70–0.76 for prostate cancer [52
]. Unlike many prior melanoma risk prediction studies, we calculated the AUC on data separate from that used to develop the model, thus validating it.
Our study may have been limited by response differences by sex, as only 36% of controls were men. Nevertheless, the odds ratio of 2.0 for male sex in our risk model is consistent with current estimates of the relatively higher incidence of melanoma in men compared to women, particularly for whites age 55 and older [30
]. Also, a secondary analysis matching on sex gave very similar results, indicating that response bias is not likely affecting the estimates to a great extent. Another limitation is the potential for recall bias, as with all case control studies. We also did not assess family history of melanoma, but this risk factor has been variably predictive in other studies [7
] and is prone to unreliable measurement [53
]. We did not collect data on the number of dysplastic nevi, but this variableis difficult for patients to assess themselves [54
]. Finally, our participants were from western Washington State only, where melanoma incidence is somewhat surprisingly higher than the overall U.S. incidence [30
], thus generalizability to other areas is unknown.
In summary, our study suggests that melanoma risk assessment could be performed via self-assessed questionnaire and that screening the 15% highest risk persons could detect about 50% of melanomas, assuming moderate fit of the model. This self-assessed risk score could be used as part of a comprehensive program of population-based melanoma screening and education. As opposed to clinic-based interventions, population-based risk assessment and education could potentially better capture older men and persons without a primary care physician, groups at highrisk for presenting with advanced melanoma [15
]. Further study is needed to validate this score in other populations, develop and validate a self-administered written questionnaire from the current telephone-based survey, and to determine the costs, feasibility, and risks of screening a high-risk group.