Details

Autor(en) / Beteiligte

• Soong, Seng-jaw
• Ding, Shouluan
• Coit, Daniel
• Balch, Charles M.
• Gershenwald, Jeffrey E.
• Thompson, John F.
• Gimotty, Phyllis

Titel

Predicting Survival Outcome of Localized Melanoma: An Electronic Prediction Tool Based on the AJCC Melanoma Database

Ist Teil von

• Annals of surgical oncology, 2010-08, Vol.17 (8), p.2006-2014

Ort / Verlag

New York: Springer-Verlag

Erscheinungsjahr

2010

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• Background We sought to develop a reliable and reproducible statistical model to predict the survival outcome of patients with localized melanoma. Methods A total of 25,734 patients with localized melanoma from the 2008 American Joint Committee on Cancer (AJCC) Melanoma Database were used for the model development and validation. The predictive model was developed from the model development data set ( n  = 14,760) contributed by nine major institutions and study groups and was validated on an independent model validation data set ( n  = 10,974) consisting of patients from a separate melanoma center. Multivariate analyses based on the Cox model were performed for the model development, and the concordance correlation coefficients were calculated to assess the adequacy of the predictive model. Results Patient characteristics in both data sets were virtually identical, and tumor thickness was the single most important prognostic factor. Other key prognostic factors identified by stratified analyses included ulceration, lesion site, and patient age. Direct comparisons of the predicted 5- and 10-year survival rates calculated from the predictive model and the observed Kaplan-Meier 5- and 10-year survival rates estimated from the validation data set yielded high concordance correlation coefficients of 0.90 and 0.93, respectively. A Web-based electronic prediction tool was also developed ( http://www.melanomaprognosis.org/ ). Conclusions This is the first predictive model for localized melanoma that was developed based on a very large data set and was successfully validated on an independent data set. The high concordance correlation coefficients demonstrated the accuracy of the predicted model. This predictive model provides a clinically useful tool for making treatment decisions, for assessing patient risk, and for planning and analyzing clinical trials.