Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Department of Human Oncology, University of Wisconsin, Madison, WI.
Department of Human Oncology, University of Wisconsin, Madison, WI.
Semin Radiat Oncol. 2023 Jul;33(3):243-251. doi: 10.1016/j.semradonc.2023.03.002.
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.
开发能够指导个性化放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关键目标。高通量分子检测与现代计算技术相结合，具有识别个体肿瘤特异性特征并创建工具的潜力，可帮助了解放疗后异质患者的结果，使临床医生能够充分受益于分子谱分析和计算生物学的技术进步，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，这需要仔细选择分析策略。此外，现代机器学习技术检测细微数据模式的能力需要特别考虑，以确保结果具有可推广性。本文综述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将其应用于基于分子数据的放射生物标志物开发，同时还探讨了挑战和新兴的研究趋势。
