Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA.
Semin Cancer Biol. 2022 Sep;84:310-328. doi: 10.1016/j.semcancer.2020.12.005. Epub 2020 Dec 5.
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
放射影像学是癌症治疗的一个组成部分，包括诊断、分期和治疗反应监测。它包含了关于肿瘤表型的丰富信息，这些信息不仅受到肿瘤细胞内在生物学过程的影响，还受到肿瘤微环境的影响，如肿瘤浸润免疫细胞的组成和功能。通过使用定量放射组学方法分析放射影像学扫描，可以建立特定影像学和分子表型之间的稳健关系。事实上，许多研究已经证明了放射基因组学在预测乳腺癌基于 MRI 的内在分子亚型和基因表达特征方面的可行性。同时，从标准护理放射图像中推断肿瘤浸润淋巴细胞的数量（癌症免疫治疗疗效的关键因素）也取得了有希望的结果。与基于活检的方法相比，放射基因组学提供了一种独特的途径，可以通过纵向成像扫描以非侵入性和整体的方式对肿瘤和免疫微环境的分子组成及其演变进行分析。在这里，我们对免疫治疗时代的放射组学研究现状进行了系统综述，并讨论了人工智能和深度学习方法中的新兴范例和机遇。这些技术进步有望改变放射组学领域，从而发现可靠的成像生物标志物。这将为它们的临床转化铺平道路，以指导精准癌症治疗。
