Department of Internal Medicine, John H. Stroger, Jr. Hospital of Cook County, Chicago, IL, USA.
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Oncologist. 2022 Jun 8;27(6):e471-e483. doi: 10.1093/oncolo/oyac036.
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics-the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends-for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
近年来，肿瘤免疫治疗领域取得了飞速发展，彻底改变了癌症治疗模式，并推动了对肿瘤生物学的进一步研究。尽管有大量证据支持免疫疗法的疗效，但癌症患者对免疫治疗的反应仍存在差异。目前预测免疫治疗反应的方法并不可靠，因为这些检测无法充分考虑肿瘤异质性和微环境。因此，需要一种改进的方法来预测免疫治疗的反应。最近的研究提出了放射组学——将医学图像转换为可以使用机器学习算法处理的定量数据（特征）的过程，以识别复杂的模式和趋势——来预测免疫治疗的反应。由于患者在疾病过程中会进行多次影像学检查，因此有大量的放射影像学数据可用于训练放射组学模型。而且，由于放射组学特征反映了癌症生物学，如肿瘤异质性和微环境，因此这些模型具有巨大的潜力，可以比当前方法更准确地预测免疫治疗的反应。基于现有生物标志物和/或临床结局的模型已经证明可以改善患者分层和治疗结局。在这篇综述中，我们讨论了放射组学在肿瘤学中的当前应用，接着讨论了最近利用放射组学预测免疫治疗反应和毒性的研究。
