The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis |
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Authors: | Koziol James A Feng Anne C Jia Zhenyu Wang Yipeng Goodison Seven McClelland Michael Mercola Dan |
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Affiliation: | The Scripps Research Institute, La Jolla, San Diego, CA, USA. |
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Abstract: | MOTIVATION: Classification and regression trees have long been used for cancer diagnosis and prognosis. Nevertheless, instability and variable selection bias, as well as overfitting, are well-known problems of tree-based methods. In this article, we investigate whether ensemble tree classifiers can ameliorate these difficulties, using data from two recent studies of radical prostatectomy in prostate cancer. RESULTS: Using time to progression following prostatectomy as the relevant clinical endpoint, we found that ensemble tree classifiers robustly and reproducibly identified three subgroups of patients in the two clinical datasets: non-progressors, early progressors and late progressors. Moreover, the consensus classifications were independent predictors of time to progression compared to known clinical prognostic factors. |
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