INTRODUCTION Predictive survival modeling offers systematic tools for clinical decision-making and individualized tailoring of treatment strategies to improve patient outcomes while reducing overall healthcare costs. In 2015, a number of machine learning and statistical models were benchmarked in the DREAM 9.5 Prostate Cancer Challenge, based on open clinical trial data for metastatic castration resistant prostate cancer (mCRPC). However, applying these models into clinical practice poses a practical challenge due to the inclusion of a large number of model variables, some of which are not routinely monitored or are expensive to measure. OBJECTIVES To develop cost-specified variable selection algorithms for constructing cost-effective prognostic models of overall survival that still preserve sufficient model performance for clinical decision making. METHODS Penalized Cox regression models were used for the survival prediction. For the variable selection, we implemented two algorithms: (i) LASSO regularization approach; and (ii) a greedy cost-specified variable selection algorithm. The models were compared in three cohorts of mCRPC patients from randomized clinical trials (RCT), as well as in a real-world cohort (RWC) of advanced prostate cancer patients treated at the Turku University Hospital. Hospital laboratory expenses were utilized as a reference for computing the costs of introducing new variables into the models. RESULTS Compared to measuring the full set of clinical variables, economic costs could be reduced by half without a significant loss of model performance. The greedy algorithm outperformed the LASSO-based variable selection with the lowest tested budgets. The overall top performance was higher with the LASSO algorithm. CONCLUSION The cost-specified variable selection offers significant budget optimization capability for the real-world survival prediction without compromising the predictive power of the model.

中文翻译：

---

使用临床试验和真实世界的医院注册数据集，对晚期前列腺癌患者进行具有成本效益的生存预测。

引言预测生存模型为临床决策和个性化定制治疗策略提供了系统的工具，以改善患者的预后，同时降低总体医疗费用。2015年，基于针对转移性去势抵抗性前列腺癌（mCRPC）的公开临床试验数据，在DREAM 9.5前列腺癌挑战赛中对许多机器学习和统计模型进行了基准测试。但是，由于将大量模型变量包括在内，将这些模型应用于临床实践提出了一个实际挑战，其中一些模型变量无法常规监控或测量费用昂贵。目的开发成本特定的变量选择算法，以构建整体生存成本有效的预后模型，该模型仍保留足够的模型性能以用于临床决策。方法采用惩罚性Cox回归模型进行生存预测。对于变量选择，我们实现了两种算法：（i）LASSO正则化方法；（ii）贪婪的成本指定变量选择算法。在来自随机临床试验（RCT）的三组mCRPC患者以及在Turku大学医院接受治疗的晚期前列腺癌患者的真实队列（RWC）中对模型进行了比较。医院实验室支出被用作计算将新变量引入模型的成本的参考。结果与测量全套临床变量相比，经济成本可降低一半，而不会显着降低模型性能。贪婪算法以最低的测试预算胜过基于LASSO的变量选择。使用LASSO算法，总体总体性能更高。结论成本指定的变量选择为现实世界的生存预测提供了显着的预算优化功能，而不会损害模型的预测能力。