Association of melanoma and neurocutaneous melanocytosis with large congenital melanocytic naevi--results from the NYU-LCMN registry.

BACKGROUND: Large congenital melanocytic naevi (LCMN), which develop in utero and are present in approximately one in 20,000 newborns, are associated with markedly increased risks of cutaneous melanoma, leptomeningeal melanoma and neurocutaneous melanocytosis (NCM). OBJECTIVES: This study examined clinical characteristics associated with melanoma and NCM among patients with LCMN, and estimated the risk of developing melanoma and NCM in these patients. METHODS: Two hundred and five LCMN patients enrolled in the New York University registry were studied. One hundred and seventy of these patients were followed prospectively. The remaining 35 patients had either melanoma at the time of entry into the registry (n = 6), or had insufficient follow-up information (n = 29). The outcome measures were the occurrence of melanoma and NCM. The associations between these outcomes and the clinical covariates (anatomical location of the LCMN, size of the LCMN, number of satellite lesions, family history of melanoma, patient sex and treatment) were assessed. RESULTS: Four of 170 (2.3%) prospectively followed patients developed melanomas, representing a standardized morbidity ratio of 324. Among the entire cohort (n = 205), there were associations between increasing numbers of satellite naevi and the occurrence of melanoma (P = 0.04), and the presence of NCM (P = 0.06). Compared with patients who did not develop these diseases, median LCMN diameters were larger among patients who developed melanoma (49 vs. 39 cm) and NCM (55 vs. 46 cm). CONCLUSIONS: In LCMN patients, increasing numbers of satellite lesions and larger LCMN diameters are associated with melanoma and NCM.

Evaluation of Elispot assays: influence of method and operator on variability of results.

In this study, a comprehensive comparative analysis of different evaluation methods of Elispot plates was performed. Three investigators using three different evaluation approaches read 50 randomly selected wells at three different time points. The methods were: (1) manual evaluation using a stereomicroscope, (2) automated evaluation using an image analysis reader system with reading parameters established by each investigator, and (3) automated evaluation using a reader system with preset reading parameters using assay-specific controls. We demonstrate that manual evaluation had the highest variability both within the same method and when comparing all methods, followed by automated evaluation with investigator-dependent parameters. The variability was low only when all investigators used the same parameters established using assay-specific controls. This variability was independent of operator or spot number per well. Based on this study, recommendations for standardization and validation procedures of Elispot assay performance and evaluation procedures are presented.

A note on competing risks in survival data analysis.

Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events.