The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis.
BACKGROUND
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.
Koziol
James A
JA
The Scripps Research Institute, La Jolla, San Diego, CA, USA.
Feng
Anne C
AC
Jia
Zhenyu
Z
Wang
Yipeng
Y
Goodison
Seven
S
McClelland
Michael
M
Mercola
Dan
D
D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D018572:Disease-Free Survival; D052138:Genes, Neoplasm; D006801:Humans; D008297:Male; D011379:Prognosis; D016016:Proportional Hazards Models; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000235:genetics; D012008:Recurrence; D012307:Risk Factors; D013997:Time Factors