Gliomas are the most common brain primary tumors worldwide, which is the earliest sequenced cancer gene in the Cancer Genome Atlas (TCGA) project. The World Health Organization Classification Update of Central Nervous System (CNS) Tumors 2016 highlights that glioma is the first tumor classified based on both of the molecular markers and histology. Radiomics is an extraction approach for high-throughput data which collects the quantitative image information appearing. Combined imaging data with genomics and proteomics, radiomics show promising prediction for cancer diagnosis, treatment, and prognosis. In this review, the radiomic analysis methods applied in gliomas are highlighted. Some remarkable findings confirm the considerable potential of radiomics in clinical cancer research.
胶质瘤是世界上最常见的原发性脑肿瘤，也是美国癌症和肿瘤基因图谱(the Cancer Genome Atlas，TCGA)工程中最早完成基因测序的恶性肿瘤。2016年更新的WHO中枢神经系统肿瘤分型中，胶质瘤是所有肿瘤疾病之中第一种通过基因表达水平与病理表现相结合的方法进行分型的肿瘤。影像组学是一种新兴的高通量定量影像数据信息提取分析方法。影像组学是影像特征与基因组学、蛋白质组学的融合，因此在肿瘤诊断、预后及治疗方案选择的临床研究中拥有巨大潜力，对于胶质瘤的研究有着极大帮助。.