Gopal Nikhil, Yazdian Anari Pouria, Turkbey Evrim, Jones Elizabeth C, Malayeri Ashkan A
Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, USA.
Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, USA.
Cancers (Basel). 2022 Feb 4;14(3):793. doi: 10.3390/cancers14030793.
With improved molecular characterization of clear cell renal cancer and advances in texture analysis as well as machine learning, diagnostic radiology is primed to enter personalized medicine with radiogenomics: the identification of relationships between tumor image features and underlying genomic expression. By developing surrogate image biomarkers, clinicians can augment their ability to non-invasively characterize a tumor and predict clinically relevant outcomes (i.e., overall survival; metastasis-free survival; or complete/partial response to treatment). It is thus important for clinicians to have a basic understanding of this nascent field, which can be difficult due to the technical complexity of many of the studies. We conducted a review of the existing literature for radiogenomics in clear cell kidney cancer, including original full-text articles until September 2021. We provide a basic description of radiogenomics in diagnostic radiology; summarize existing literature on relationships between image features and gene expression patterns, either computationally or by radiologists; and propose future directions to facilitate integration of this field into the clinical setting.
随着肾透明细胞癌分子特征的改善以及纹理分析和机器学习的进展,诊断放射学正准备通过放射基因组学进入个性化医疗领域:确定肿瘤图像特征与潜在基因组表达之间的关系。通过开发替代图像生物标志物,临床医生可以增强其非侵入性地描述肿瘤特征并预测临床相关结果(即总生存期、无转移生存期或对治疗的完全/部分反应)的能力。因此,临床医生对这个新兴领域有基本的了解很重要,然而由于许多研究的技术复杂性,这可能具有挑战性。我们对肾透明细胞癌放射基因组学的现有文献进行了综述,包括截至2021年9月的原始全文文章。我们提供了诊断放射学中放射基因组学的基本描述;总结了关于图像特征与基因表达模式之间关系的现有文献,这些关系通过计算或由放射科医生得出;并提出了未来的方向,以促进该领域融入临床实践。