Khaleel Sari, Katims Andrew, Cumarasamy Shivaram, Rosenzweig Shoshana, Attalla Kyrollis, Hakimi A Ari, Mehrazin Reza
Memorial Sloan Kettering Cancer Center, Department of Urology, New York, NY 10065, USA.
Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Cancers (Basel). 2022 Apr 22;14(9):2085. doi: 10.3390/cancers14092085.
Radiogenomics is a field of translational radiology that aims to associate a disease's radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: "radiogenomics", "renal cell carcinoma", and "clear cell renal cell carcinoma". Articles included were limited to the English language and published between 2009-2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.
放射基因组学是转化放射学的一个领域，旨在将疾病的放射学表型与其潜在的基因型联系起来，从而提供一类具有诊断、预后和治疗潜力的新型非侵入性生物标志物。我们在此回顾了关于透明细胞肾细胞癌（ccRCC）（最常见的肾恶性肿瘤）的当前放射基因组学文献。通过查询PubMed、Medline、Cochrane图书馆、谷歌学术和科学网数据库进行文献综述，使用以下搜索词识别所有相关文章：“放射基因组学”、“肾细胞癌”和“透明细胞肾细胞癌”。纳入的文章限于英文且发表于2009年至2021年之间。在检索到的141篇文章中，16篇符合我们的纳入标准。大多数研究使用来自开源和机构数据库的计算机断层扫描（CT）图像来提取放射组学特征，然后使用各种机器学习算法针对ccRCC中的常见基因组突变进行建模。在最近的研究中，我们注意到研究方向已转向转录组和/或表观遗传疾病谱以及下游临床结果的预测。放射基因组学为ccRCC非侵入性生物标志物的开发提供了一个平台，在小规模回顾性研究中取得了有前景的结果。然而，在整合到临床实践之前，需要更多的研究来识别和验证可靠的放射基因组学生物标志物。
