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Functional Imaging using Radiomic Features in Assessment of Lymphoma
Methods ( IF 4.8 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.ymeth.2020.06.020
Marius E Mayerhoefer 1 , Lale Umutlu 2 , Heiko Schöder 3
Affiliation  

Lymphomas are typically large, well-defined, and relatively homogeneous tumors, and therefore represent ideal targets for the use of radiomics. Of the available functional imaging tests, [18F]FDG-PET for body lymphoma and diffusion-weighted MRI (DWI) for central nervous system (CNS) lymphoma are of particular interest. The current literature suggests that two main applications for radiomics in lymphoma show promise: differentiation of lymphomas from other tumors, and lymphoma treatment response and outcome prognostication. In particular, encouraging results reported in the limited number of presently available studies that utilize functional imaging suggest that (1) MRI-based radiomics enables differentiation of CNS lymphoma from glioblastoma, and (2) baseline [18F]FDG-PET radiomics could be useful for survival prognostication, adding to or even replacing commonly used metrics such as standardized uptake values and metabolic tumor volume. However, due to differences in biological and clinical characteristics of different lymphoma subtypes and an increasing number of treatment options, more data are required to support these findings. Furthermore, a consensus on several critical steps in the radiomics workflow -most importantly, image reconstruction and post processing, lesion segmentation, and choice of classification algorithm- is desirable to ensure comparability of results between research institutions.

中文翻译:

使用放射学特征评估淋巴瘤的功能成像

淋巴瘤通常是大的、定义明确的和相对均匀的肿瘤,因此是放射组学使用的理想目标。在可用的功能成像测试中,[18F]FDG-PET 用于身体淋巴瘤和弥散加权 MRI (DWI) 用于中枢神经系统 (CNS) 淋巴瘤尤其令人感兴趣。目前的文献表明,放射组学在淋巴瘤中的两个主要应用显示出前景:将淋巴瘤与其他肿瘤区分开来,以及淋巴瘤治疗反应和预后预测。特别是,在目前有限数量的利用功能成像的研究中报告的令人鼓舞的结果表明(1)基于 MRI 的放射组学能够区分 CNS 淋巴瘤和胶质母细胞瘤,以及(2)基线 [18F]FDG-PET 放射组学可能是有用的用于生存预测,添加甚至替换常用指标,例如标准化摄取值和代谢肿瘤体积。然而,由于不同淋巴瘤亚型的生物学和临床特征存在差异以及治疗选择的增加,需要更多数据来支持这些发现。此外,需要就放射组学工作流程中的几个关键步骤——最重要的是,图像重建和后处理、病变分割和分类算法的选择——达成共识,以确保研究机构之间结果的可比性。需要更多数据来支持这些发现。此外,需要就放射组学工作流程中的几个关键步骤——最重要的是,图像重建和后处理、病变分割和分类算法的选择——达成共识,以确保研究机构之间结果的可比性。需要更多数据来支持这些发现。此外,需要就放射组学工作流程中的几个关键步骤——最重要的是,图像重建和后处理、病变分割和分类算法的选择——达成共识,以确保研究机构之间结果的可比性。
更新日期:2020-07-01
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