Metastatic, castrate-resistant prostate cancer (mCRPC) is one of the most prevalent cancers and is the third leading cause of cancer death among men. Several treatment options have been developed to combat mCRPC, however none have produced any tangible benefits to patients' overall survivability. As part of a crowd-sourced algorithm development competition, participants were asked to develop new prognostic models for mCRPC patients treated with docetaxel. Such results could potentially assist in clinical decision making for future mCRPC patients.
In this thesis, we present a new ensemble prognostic model to perform risk prediction for mCRPC patients treated with docetaxel. We rely on traditional survival analysis model like the Cox Proportional Hazard model, as well as more recently developed boosting model that incorporates smooth approximation of the concordance index for direct optimization. Our model performs better than the the current state-of-the-art mCRPC prognostic models for the concordance index performance measure and is competitive with these models on the integrated time-dependent area under the receiver operating characteristic curve.
|Commitee:||Ihler, Alexander, Yu, Zhaoxia|
|School:||University of California, Irvine|
|Department:||Computer Science - M.S.|
|School Location:||United States -- California|
|Source:||MAI 56/01M(E), Masters Abstracts International|
|Subjects:||Statistics, Bioinformatics, Computer science, Oncology|
|Keywords:||Concoradance index, Cox Proprotional Hazard, Ensemble, Gradient boosting machine, Machine learning, Prostate cancer|
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