Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.
Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.
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DOI：
10.1093/noajnl/vdaa148
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Tiwari P
中科院分区：
文献类型：
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作者：
Beig N;Bera K;Tiwari P
神经肿瘤学主要包括大脑和中枢神经系统的恶性肿瘤，包括原发肿瘤和转移性肿瘤。目前，神经肿瘤学的一个重大临床挑战是根据患者对传统或实验治疗的生存结果或治疗反应的先验知识，为患者量身定制治疗方案。放射组学或从常规放射成像中定量提取亚视觉数据最近已成为一种强大的数据驱动方法，可提供与诊断、预测、预后相关的临床相关问题的见解，以及评估治疗反应。此外，放射基因组学方法提供了一种机制，可以建立放射组学特征与点突变和下一代测序数据的统计相关性，以进一步利用常规MRI扫描的潜力作为“虚拟活组织检查”图。在这篇综述中，我们介绍了神经肿瘤学中的放射组学和放射基因组学方法，包括包括预处理、肿瘤分割和从分割的感兴趣区域中提取“手工制作的”特征的工作流程，以及识别最终可能导致在神经肿瘤学应用中开发可靠的预后和预测模型的放射基因组学关联。最后，我们讨论了放射组学和放射基因组学方法在神经肿瘤学个人化治疗决策中的前景，以及临床采用的挑战，这将在很大程度上依赖于它们对跨站点和扫描仪的成像协议的非标准化所表现出的韧性，以及它们在大型多机构队列中展示重复性的能力。
Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.