Lohmann Philipp, Kocher Martin, Ruge Maximillian I, Visser-Vandewalle Veerle, Shah N Jon, Fink Gereon R, Langen Karl-Josef, Galldiks Norbert
Institute of Neuroscience and Medicine (INM-3/-4/-11), Research Center Juelich, Jülich, Germany.
Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Front Neurol. 2020 Feb 7;11:1. doi: 10.3389/fneur.2020.00001. eCollection 2020.
Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.
尽管目前有多种成像方式被用于脑肿瘤患者（包括脑转移瘤患者），或正处于研究阶段，但迄今为止，临床图像解读仅使用了常规获取的成像数据中潜在的复杂、高维数字信息的一小部分。高性能计算的日益普及使得从医学图像中提取通常超出人类感知范围的定量成像特征成为可能。利用机器学习技术和先进的统计方法，这些成像特征的子集被用于生成数学模型，这些模型代表了与潜在肿瘤生物学相关的特征信号，可能有助于评估预后或治疗反应，或识别分子标记。将合适的、特征性的图像特征识别以及预测性或预后性数学模型的生成统称为放射组学。本综述总结了脑转移瘤患者放射组学的现状。
