Genomics models in radiotherapy: From mechanistic to machine learning.

Genomics models in radiotherapy: From mechanistic to machine learning.
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DOI:
10.1002/mp.13751
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发表时间:
2020-06
期刊:
影响因子:
3.8
通讯作者:
Kerns SL
Kerns SL
中科院分区:
医学3区
文献类型:
--
作者:
Kang J;Coates JT;Strawderman RL;Rosenstein BS;Kerns SL

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机器学习(ML)为分类和回归中的高维预测问题提供了一个广泛的框架。虽然机器学习通常应用于医学物理中的成像问题,但有许多努力将这些原理应用于辐射生物学问题的生物数据。在这里,我们提供了一个回顾放射基因组学建模框架和努力基因组引导放射治疗。我们首先讨论医学肿瘤学在开发精确生物标志物方面的努力。接下来,我们将讨论创建正常组织或肿瘤放射敏感性临床试验的类似努力。然后,我们讨论了放射敏感性的建模框架和ML的发展,以创建放射基因组学的预测模型。
Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.
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