摘要
较低级别脑胶质瘤(lower-grade gliomas, LGGs)是指世界卫生组织(World Health Organization, WHO)2级和3级脑胶质瘤,与胶质母细胞瘤相比,LGGs患者的病理级别低,预后较好。但是,由于其侵袭性生长方式,部分患者治疗后仍然会出现复发或恶性转变,因此,早期进行预后预测有望对LGGs患者提供个体化精准治疗,提高生活质量。影像组学可以从图像中提取高通量成像特征,将图像信息转换为直观的数据来反映肿瘤内部异质性信息,这有助于临床医生为患者选择合适的治疗方案。基于MRI的影像组学可以直接预测LGGs患者的预后,也可以将影像组学特征与基因表型或免疫特征结合共同预测预后,但多项研究仍存在局限性,开展基于功能MRI的影像组学,并将影像组学与新发现的预后相关基因或免疫学特征结合用于预后预测是未来研究的方向。本文综述了影响LGGs的预后因素及影像组学在LGGs预后预测中的作用,以拓展基于影像组学预测LGGs患者预后的方法,为临床精准诊治提供新思路。
Lower-grade gliomas(LGGs) are World Health Organization(WHO) grade 2 and 3 gliomas. Compared with glioblastoma,LGGs have lower pathological grade and better prognosis. However, due to its aggressive growth mode, some patients still have recurrence or malignant transformation after treatment. Therefore, early prognosis prediction is expected to provide individualized and accurate treatment for LGGs patients and improve their quality of life. Radiomics, extracting and analyzing high-throughput imaging features from images, and converting the image information into intuitive data to reflect the internal heterogeneity of tumors, is helpful for clinicians to select the appropriate treatment plan for patients. The radiomics based on magnetic resonance imaging can directly predict the prognosis of LGGs, and can also combine the radiomics features with gene phenotype or immune features to predict the prognosis. However, many studies still have limitations. It is the direction of future research to develop radiomics based on MRI functional imaging and combine radiomics with newly discovered prognostic related genes or immunological features for prognosis prediction. This article reviews the prognostic factors of LGGs and the role of radiomics in predicting the prognosis of LGGs, in order to expand the method of predicting the prognosis based on radiomics and provide a new idea for accurate clinical diagnosis and treatment.
作者
李阳阳
谭艳
LI Yangyang;TAN Yan(College of Medical Imaging,Shanxi Medical University,Taiyuan 030001,China;Department of Radiology,First Hospital of Shanxi Medical University,Taiyuan 030001,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2022年第11期129-132,148,共5页
Chinese Journal of Magnetic Resonance Imaging
基金
国家自然科学基金(编号:82071893)。
