Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and “omics” assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.

开发可以指导个性化放射治疗临床决策的放射肿瘤生物标志物是精准癌症医学努力的一个关键目标。高通量分子检测与现代计算技术相结合，有可能识别个体肿瘤特异性特征，并创建有助于了解异质患者对放射治疗的反应结果的工具，使临床医生能够充分受益于放射治疗的技术进步。分子分析和计算生物学，包括机器学习。然而，高通量和“组学”检测产生的数据日益复杂，需要仔细选择分析策略。此外，现代机器学习技术检测微妙数据模式的能力需要特殊考虑，以确保结果具有普遍性。在此，我们回顾了肿瘤生物标志物开发的计算框架，并描述了常用的机器学习方法以及如何使用分子数据将它们应用于放射生物标志物开发，以及挑战和新兴研究趋势。