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Machine Learning and Radiogenomics: Lessons Learned and Future Directions

机译：机器学习和放射基因组学：经验教训和未来方向

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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.
机译：由于患者数据可用性的迅速增加，人们对精密医学产生了极大的兴趣，这种医学可以促进针对每个患者制定个性化治疗计划。放射肿瘤学特别适合于预测性机器学习（ML）模型，因为大量的诊断数据用作输入，而治疗数据则作为输出。可以利用ML方法的精密放射肿瘤学的新兴领域是放射基因组学，它是研究基因组变异对正常组织和肿瘤组织对放射敏感性的影响的研究。目前，正在接受放射治疗的患者使用针对肿瘤和周围正常组织的均匀剂量限制进行治疗。这在许多方面都不理想。首先，可以传递到目标体积的剂量可能不足以控制，但受到周围正常组织的限制，因为剂量增加可能导致明显的发病率和罕见性。其次，两名具有几乎相同剂量分布的患者可能具有截然不同的急性和晚期毒性，导致漫长的治疗中断和次佳的控制，或慢性病导致生活质量下降。尽管放射基因组学取得了重大进步，但是遗传对辐射反应的贡献却远远超出了我们目前对单个风险变体的理解。在基因组学领域，机器学习方法被用于提取难以检测的知识，但是这些方法尚未完全渗透到放射基因组学领域。因此，本出版物的目的是概述应用于放射基因组学的ML。我们从放射基因组学及其与精密医学的关系的简要历史开始。然后，我们介绍ML并将其与统计假设检验进行比较，以反思共享的课程并避免常见的陷阱。研究了目前用于全基因组关联研究的ML方法。接下来介绍ML在放射基因组学中的应用。我们以重要的课程结束，以正确地将ML集成到放射基因组学中。

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