Big Data Analytics for Prostate Radiotherapy.
Big Data Analytics for Prostate Radiotherapy.
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DOI：
10.3389/fonc.2016.00149
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发表时间：
2016
影响因子：
4.7
通讯作者：
El Naqa I
中科院分区：
文献类型：
--
作者：
Coates J;Souhami L;El Naqa I
放射治疗是局部前列腺癌的一线治疗选择，放射诱导的正常组织损伤往往是现代放射治疗方案的主要限制因素。相反，为了避免邻近健康组织而对靶体积的剂量不足限制了实现局部、长期控制的可能性。因此，为放疗结果生成个性化数据驱动的风险概况的能力将提供有价值的预后信息，以帮助指导临床医生和患者。应用于放射肿瘤学的大数据有望通过收集和整合异构数据类型，包括患者特定的临床参数、治疗相关的剂量-体积指标和生物风险因素，更好地了解结果。综合起来，这些变量构成了多维空间（“RadoncSpace”）的基础，在这个空间中，所提出的建模技术进行搜索，以识别重要的预测因子。在此，我们回顾了肿瘤控制和放疗诱导的正常组织效应的结果建模和大数据挖掘技术。考虑到不同的数据类型和物理和生物参数的大量异质混合，我们将许多提出的建模方法应用于低分割前列腺癌患者队列。还回顾了交叉验证技术，以改进所提出的框架体系结构并检查单个模型的性能。在讨论系统放射生物学方法的潜在未来影响之前，我们将考虑借鉴大数据分析概念的先进建模技术，如机器学习和人工智能。
Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose–volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the “RadoncSpace”) in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.