Turkish Journal of Medical Sciences




To determine 2 different prognostic indexes (PI) for the differentiation of subgroups of nonmetastatic breast cancer patients with the Cox regression analysis and survival tree (ST) methods and the additional usage of the Kaplan-Meier estimates to investigate the predictive power of these methods. Materials and methods: Prognostic factors data were collected for 410 patients. The Cox regression analysis examines the relationship of the survival distribution and covariates. The ST method is a tree-structured survival analysis based on a recursive partitioning algorithm. In this study, Harrell's concordance indexes of models for training and test sets were computed. Furthermore, survival curves were estimated by the Kaplan-Meier method. Disease-free survival (DFS) was calculated from the time of initial diagnosis (initiation of the first treatment) to the first recurrence of disease. Results: After a median follow-up of 48 months, 100 (24.4%) patients have had at least 1 of the DFS events. In Cox regression analysis, we proposed the simple PI, which is a sum of axillary nodal and HER2/neu status. In the ST method, we identified 3 variables: HER2/neu, axillary nodal, and estrogen receptor status. The axillary nodal status was the most important determining factor for recurrence. Conclusion: We found that the PI of the ST and Cox regression methods had similar performance levels in predicting DFS, and the error rates of the models were close to each other in the training and test sets. Furthermore, we determined that the axillary nodal status and HER2/neu were the most important determining factors for prediction of DFS in breast cancer patients.


Breast cancer, survival tree, recursive partitioning, disease-free survival, prognostic index

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