Machine learning and modeling: Data, validation, communication challenges
Machine learning and modeling: Data, validation, communication challenges
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DOI：
10.1002/mp.12811
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发表时间：
2018-10-01
期刊：
影响因子：
3.8
通讯作者：
Ten Haken, Randall
中科院分区：
文献类型：
--
作者：
El Naqa, Issam;Ruan, Dan;Ten Haken, Randall
随着大数据时代的到来，机器学习算法在放射肿瘤学中的应用正在迅速增长，其应用包括：治疗反应建模、治疗计划、轮廓绘制、器官分割、图像引导、运动跟踪、质量保证等。尽管有这种兴趣，但作为日常临床操作的一部分，机器学习的实际临床实施仍然滞后。这篇白色论文的目的是通过强调其尚未开发的优势和临床进步潜力，同时提出当前的挑战和未来研究的开放问题，进一步推动放射肿瘤学机器学习这一新领域的进展。本文的目标受众包括医学物理学/放射肿瘤学领域的新人和从业人员。本文还提供了一般性建议，以避免将这些强大的数据分析工具应用于医学物理学和放射肿瘤学问题时的常见陷阱，并提出了一些透明和信息丰富的机器学习结果报告指南。
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.