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放射基因组学:在直肠癌管理中的当代应用

Radiogenomics: Contemporary Applications in the Management of Rectal Cancer.

作者信息

O'Sullivan Niall J, Temperley Hugo C, Horan Michelle T, Corr Alison, Mehigan Brian J, Larkin John O, McCormick Paul H, Kavanagh Dara O, Meaney James F M, Kelly Michael E

机构信息

Department of Radiology, St. James's Hospital, D08 NHY1 Dublin, Ireland.

School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland.

出版信息

Cancers (Basel). 2023 Dec 12;15(24):5816. doi: 10.3390/cancers15245816.

Abstract

Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.

摘要

放射基因组学是放射组学的一个子领域,指的是利用非侵入性成像标记预测潜在的肿瘤生物学特性。这项新技术旨在通过开发“影像生物标志物”来降低与传统基因检测相关的高成本、工作量和侵入性,这些标志物有潜力在确定肿瘤生物学特征时作为一种替代的“液体活检”方法。放射基因组学还利用了挖掘肿瘤生物学各个方面的潜力,而这些方面是传统的基于活检的方法无法评估的,例如肿瘤总负荷、病灶内/病灶间异质性以及纵向提供肿瘤生物学信息的可能性。多项研究已表明开发基于放射基因组学的特征以预测治疗结果和肿瘤特征的可行性;然而,许多研究缺乏前瞻性的外部验证。我们对目前有关在直肠癌中使用放射基因组学预测潜在肿瘤生物学特性的文献进行了系统综述。

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A review of radiomics and genomics applications in cancers: the way towards precision medicine.
Radiat Oncol. 2022 Dec 30;17(1):217. doi: 10.1186/s13014-022-02192-2.
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Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.
Biomed Res Int. 2022 Aug 16;2022:6623574. doi: 10.1155/2022/6623574. eCollection 2022.
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Texture analysis imaging "what a clinical radiologist needs to know".
Eur J Radiol. 2022 Jan;146:110055. doi: 10.1016/j.ejrad.2021.110055. Epub 2021 Nov 25.
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Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.
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