Machine Learning & Molecular Radiation Tumor Biomarkers

IF 2.6 3区 医学 Q3 ONCOLOGY Seminars in Radiation Oncology Pub Date : 2023-07-01 DOI:10.1016/j.semradonc.2023.03.002
Nicholas R. Rydzewski MD, MPH , Kyle T. Helzer PhD , Matthew Bootsma MS , Yue Shi PhD , Hamza Bakhtiar BS , Martin Sjöström MD, PhD , Shuang G. Zhao MD, MSE
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Abstract

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.

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机器学习与分子放射肿瘤生物标志物
开发可指导个性化放射治疗临床决策的放射肿瘤生物标志物是癌症精准医学的关键目标。高通量分子分析与现代计算技术相结合,有可能识别个体肿瘤特异性特征,并创建有助于了解放疗后患者异质性结果的工具,使临床医生能够充分受益于分子图谱和计算生物学(包括机器学习)的技术进步。然而,高通量和“组学”分析产生的数据越来越复杂,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还需要特别考虑,以确保结果可推广。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,以及它们如何应用于利用分子数据开发辐射生物标志物,以及挑战和新兴的研究趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
48
审稿时长
>12 weeks
期刊介绍: Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.
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