Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.

中文翻译：

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结直肠癌肝转移的放射组学

放射组学是一个新兴领域，具有浓厚的兴趣，尤其是在肿瘤学领域。放射组学研究的过程包括病变分割、特征提取、特征一致性分析、特征选择和模型构建。手动分割是放射组学中最关键的部分之一。它可能很耗时，并且存在肿瘤描绘的可变性，这导致计算参数和评估空间肿瘤异质性的可重复性问题，特别是在大肿瘤或多个肿瘤中。放射组学特征提供有关肿瘤表型和癌症微环境的数据。当与其他相关数据相关联并与结果数据相关联时，放射组学派生参数可以产生基于临床决策支持系统的准确可靠的证据。主要挑战是以定量方式优化收集和整合不同的多模式数据源，以提供明确的临床预测，根据即将做出的决策准确而稳健地实现结果预测。检索时间为2010年1月至2021年1月，纳入标准为：评价肝大肠转移影像组学的临床研究。排除标准是没有足够报告数据、病例报告、评论或编辑信的研究。我们确认了 2010 年 1 月至 2021 年 1 月评估 mCRC 放射组学的 38 项研究。 20 项针对不同的 tpics，5 项符合大多数标准；3是审稿，或给编辑的信；所以收录了 10 篇文章。