Machine Learning and Radiogenomics: Lessons Learned and Future Directions.
Machine Learning and Radiogenomics: Lessons Learned and Future Directions.
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DOI：
10.3389/fonc.2018.00228
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发表时间：
2018
影响因子：
4.7
通讯作者：
Rosenstein BS
中科院分区：
文献类型：
--
作者：
Kang J;Rancati T;Lee S;Oh JH;Kerns SL;Scott JG;Schwartz R;Kim S;Rosenstein BS
由于患者数据的可用性迅速增加，人们对精准医疗产生了浓厚的兴趣，因为它可以促进为每位患者制定个性化的治疗计划。放射肿瘤学特别适合预测机器学习 (ML) 模型，因为需要使用大量诊断数据作为输入，并生成大量治疗数据作为输出。可以利用机器学习方法的精准放射肿瘤学的一个新兴领域是放射基因组学，它研究基因组变异对正常组织和肿瘤组织对放射的敏感性的影响。目前，接受放射治疗的患者使用针对肿瘤和周围正常组织的统一剂量限制进行治疗。这在很多方面都不是最理想的。首先，可以输送到目标体积的剂量可能不足以控制，而是受到周围正常组织的限制，因为剂量递增可能导致显着的发病率且罕见。其次，剂量分布几乎相同的两名患者可能具有截然不同的急性和晚期毒性，导致长时间的治疗中断和次优控制，或导致生活质量差的慢性发病。尽管放射基因组学取得了重大进展，但遗传对辐射反应的贡献程度远远超出了我们目前对个体风险变异的理解。在基因组学领域，机器学习方法被用来提取难以检测的知识，但这些方法尚未完全渗透到放射基因组学中。因此，本出版物的目标是概述机器学习在放射基因组学中的应用。我们首先简要介绍放射基因组学的历史及其与精准医学的关系。然后，我们介绍机器学习，并将其与统计假设检验进行比较，以反思共同的教训并避免常见的陷阱。对当前全基因组关联研究的机器学习方法进行了检查。接下来介绍机器学习在放射基因组学中的具体应用。最后，我们总结了将机器学习正确整合到放射基因组学中的重要经验教训。
Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.
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DOI：
10.1016/j.chemolab.2006.04.021
发表时间：
2006-12-01
影响因子：
3.9
作者：
Anderssen, Endre;Dyrstad, Knut;Martens, Harald
通讯作者：
Martens, Harald
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.7
作者：
Breiman, L
通讯作者：
Breiman, L
影响因子：
5.8
作者：
Baggerly, KA;Morris, JS;Coombes, KR
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Coombes, KR
影响因子：
8.4
作者：
BENTZEN, SM;OVERGAARD, M;OVERGAARD, J
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OVERGAARD, J