Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer.
Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer.
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DOI：
10.12688/f1000research.8201.1
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发表时间：
2016
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影响因子：
--
通讯作者：
Shiga M
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作者：
Shiga M
转移性去势抵抗性前列腺癌（mCRPC）是前列腺癌患者死亡的主要原因。尽管已经开发了一些治疗mCRPC的选择，但最有效的治疗方法仍不清楚。因此，寻找与mCRPC相关的关键患者临床变量是了解mCRPC疾病进展机制和对这些患者进行临床决策的重要问题。前列腺癌梦想挑战赛是一项基于人群的竞赛，旨在利用新的大型临床数据集解决这一重要挑战。本文提出了一种有效的程序来预测这些患者的整体风险和生存时间，针对前列腺癌DREAM挑战的1a和1b亚挑战。该程序实现了两步特征选择过程，首先对数值临床变量进行稀疏特征选择，并对临床分类变量导致的生存曲线差异进行统计假设检验，然后进行前向特征选择以缩小信息特征列表。该方法使用具有这些选定特征的Cox比例风险模型，使用线性模型预测患者的整体风险和生存时间，线性模型的输入是由风险模型计算的中位数时间。挑战结果表明，该方法在全局风险预测和生存时间预测上正确地选择了更多信息特征，优于当前最先进的模型。
Metastatic castrate resistant prostate cancer (mCRPC) is the major cause of death in prostate cancer patients. Even though some options for treatment of mCRPC have been developed, the most effective therapies remain unclear. Thus finding key patient clinical variables related with mCRPC is an important issue for understanding the disease progression mechanism of mCRPC and clinical decision making for these patients. The Prostate Cancer DREAM Challenge is a crowd-based competition to tackle this essential challenge using new large clinical datasets. This paper proposes an effective procedure for predicting global risks and survival times of these patients, aimed at sub-challenge 1a and 1b of the Prostate Cancer DREAM challenge. The procedure implements a two-step feature selection procedure, which first implements sparse feature selection for numerical clinical variables and statistical hypothesis testing of differences between survival curves caused by categorical clinical variables, and then implements a forward feature selection to narrow the list of informative features. Using Cox’s proportional hazards model with these selected features, this method predicted global risk and survival time of patients using a linear model whose input is a median time computed from the hazard model. The challenge results demonstrated that the proposed procedure outperforms the state of the art model by correctly selecting more informative features on both the global risk prediction and the survival time prediction.