OBJECTIVE: To develop a prognostic model, based on clinical and pathologic data that are routinely available to the clinician, that would estimate the chance for survival of a patient with primary cutaneous melanoma after definitive surgical therapy. DESIGN: Cohort analytical study. SETTING: University medical center. PATIENTS: 488 patients with primary cutaneous melanoma who had no apparent metastatic disease. Patients were followed prospectively for at least 10 years. An independent validation sample of 142 patients was used to assess the stability of the model. MEASUREMENTS: Six clinical and pathologic variables that predict survival and are readily available to the clinician were used to develop a prediction model. The variables were tested for their association with death by using a univariate logistic regression model. Point estimates were generated for the probability of surviving melanoma at 10 years. Variables that were statistically significantly associated with survival were retained for testing in a logistic regression model. RESULTS: 488 patients were followed prospectively for a median of 13.5 years (minimum, 10.0 years; maximum, 20.5 years). The overall 10-year survival of the study group was 78%. Four variables were found to be independent predictors of survival. Presented as adjusted odds ratios, from strongest to weakest relative predictive strength, these variables were tumor thickness (odds ratio, 50.8), site of primary melanoma (odds ratio, 4.4), age of the patient (odds ratio, 3.0), and sex of the patient (odds ratio, 2.0). The four-variable model was significantly more accurate than tumor thickness alone, particularly for predicting death. Overall, use of the model reduced the error rate of the prediction of death by 50%. CONCLUSIONS: A prognostic model that uses four readily accessible variables more accurately predicts outcome in patients with primary melanoma than does tumor thickness alone. This four-variable model can identify patients at high risk for the recurrence of disease, an identification that becomes increasingly important as adjuvant therapies are developed for treatment of melanoma.
OBJECTIVE: To develop a prognostic model, based on clinical and pathologic data that are routinely available to the clinician, that would estimate the chance for survival of a patient with primary cutaneous melanoma after definitive surgical therapy. DESIGN: Cohort analytical study. SETTING: University medical center. PATIENTS: 488 patients with primary cutaneous melanoma who had no apparent metastatic disease. Patients were followed prospectively for at least 10 years. An independent validation sample of 142 patients was used to assess the stability of the model. MEASUREMENTS: Six clinical and pathologic variables that predict survival and are readily available to the clinician were used to develop a prediction model. The variables were tested for their association with death by using a univariate logistic regression model. Point estimates were generated for the probability of surviving melanoma at 10 years. Variables that were statistically significantly associated with survival were retained for testing in a logistic regression model. RESULTS: 488 patients were followed prospectively for a median of 13.5 years (minimum, 10.0 years; maximum, 20.5 years). The overall 10-year survival of the study group was 78%. Four variables were found to be independent predictors of survival. Presented as adjusted odds ratios, from strongest to weakest relative predictive strength, these variables were tumor thickness (odds ratio, 50.8), site of primary melanoma (odds ratio, 4.4), age of the patient (odds ratio, 3.0), and sex of the patient (odds ratio, 2.0). The four-variable model was significantly more accurate than tumor thickness alone, particularly for predicting death. Overall, use of the model reduced the error rate of the prediction of death by 50%. CONCLUSIONS: A prognostic model that uses four readily accessible variables more accurately predicts outcome in patients with primary melanoma than does tumor thickness alone. This four-variable model can identify patients at high risk for the recurrence of disease, an identification that becomes increasingly important as adjuvant therapies are developed for treatment of melanoma.
Authors: Richard W Joseph; Vijay R Peddareddigari; Ping Liu; Priscilla W Miller; Willem W Overwijk; Nebiyou B Bekele; Merrick I Ross; Jeffrey E Lee; Jeffrey E Gershenwald; Anthony Lucci; Victor G Prieto; John D McMannis; Nicholas Papadopoulos; Kevin Kim; Jade Homsi; Agop Bedikian; Wen-Jen Hwu; Patrick Hwu; Laszlo G Radvanyi Journal: Clin Cancer Res Date: 2011-06-01 Impact factor: 12.531
Authors: Fangyi Gu; Ting-Huei Chen; Ruth M Pfeiffer; Maria Concetta Fargnoli; Donato Calista; Paola Ghiorzo; Ketty Peris; Susana Puig; Chiara Menin; Arcangela De Nicolo; Monica Rodolfo; Cristina Pellegrini; Lorenza Pastorino; Evangelos Evangelou; Tongwu Zhang; Xing Hua; Curt T DellaValle; D Timothy Bishop; Stuart MacGregor; Mark I Iles; Matthew H Law; Anne Cust; Kevin M Brown; Alexander J Stratigos; Eduardo Nagore; Stephen Chanock; Jianxin Shi; Melanoma Meta-Analysis Consortium; MelaNostrum Consortium; Maria Teresa Landi Journal: Hum Mol Genet Date: 2018-12-01 Impact factor: 6.150
Authors: Ann Y Lee; Nicolas Droppelmann; Katherine S Panageas; Qin Zhou; Charlotte E Ariyan; Mary S Brady; Paul B Chapman; Daniel G Coit Journal: Ann Surg Oncol Date: 2016-11-01 Impact factor: 5.344
Authors: Xiaowei Xu; Phyllis A Gimotty; Dupont Guerry; Giorgos Karakousis; Patricia Van Belle; Haohai Liang; Katharine Montone; Terry Pasha; Michael E Ming; Geza Acs; Michael Feldman; Stephen Barth; Rachel Hammond; Rosalie Elenitsas; Paul J Zhang; David E Elder Journal: Hum Pathol Date: 2008-04-28 Impact factor: 3.466
Authors: Susan A Oliveria; Jennifer L Hay; Alan C Geller; Maureen K Heneghan; Mary S McCabe; Allan C Halpern Journal: J Cancer Surviv Date: 2007-03 Impact factor: 4.442