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PLoS One. 2013;8(2):e56435. doi: 10.1371/journal.pone.0056435. Epub 2013 Feb 27.

A patient-centered methodology that improves the accuracy of prognostic predictions in cancer.

Author information

  • 1Center for Melanoma Research and Treatment, California Pacific Medical Center and Research Institute, San Francisco, California, United States of America. kashani@cpmcri.org

Erratum in

  • PLoS One. 2013;8(10). doi:10.1371/annotation/2db613f0-06cc-47e7-bde2-7c61bc792bb9.

Abstract

Individualized approaches to prognosis are crucial to effective management of cancer patients. We developed a methodology to assign individualized 5-year disease-specific death probabilities to 1,222 patients with melanoma and to 1,225 patients with breast cancer. For each cancer, three risk subgroups were identified by stratifying patients according to initial stage, and prediction probabilities were generated based on the factors most closely related to 5-year disease-specific death. Separate subgroup probabilities were merged to form a single composite index, and its predictive efficacy was assessed by several measures, including the area (AUC) under its receiver operating characteristic (ROC) curve. The patient-centered methodology achieved an AUC of 0.867 in the prediction of 5-year disease-specific death, compared with 0.787 using the AJCC staging classification alone. When applied to breast cancer patients, it achieved an AUC of 0.907, compared with 0.802 using the AJCC staging classification alone. A prognostic algorithm produced from a randomly selected training subsample of 800 melanoma patients preserved 92.5% of its prognostic efficacy (as measured by AUC) when the same algorithm was applied to a validation subsample containing the remaining patients. Finally, the tailored prognostic approach enhanced the identification of high-risk candidates for adjuvant therapy in melanoma. These results describe a novel patient-centered prognostic methodology with improved predictive efficacy when compared with AJCC stage alone in two distinct malignancies drawn from two separate populations.

PMID:
23460802
[PubMed - indexed for MEDLINE]
PMCID:
PMC3584071
Free PMC Article
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