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作 者:陈瑞宏 谭艳[2] CHEN Ruihong;TAN Yan(College of Medical Imaging,Shanxi Medical University,Taiyuan 030001,China;Department of Radiology,First Hospital of Shanxi Medical University,Taiyuan 030001,China)
机构地区:[1]山西医科大学医学影像学院,太原030001 [2]山西医科大学第一医院影像科,太原030001
出 处:《磁共振成像》2023年第3期159-164,共6页Chinese Journal of Magnetic Resonance Imaging
基 金:国家自然科学基金(编号:82071893)。
摘 要:脑胶质瘤是脑实质最常见的原发恶性肿瘤。脑胶质瘤分为1~4级,其中2级和3级被称为较低级别脑胶质瘤(lower-grade gliomas,LGGs)。2021年世界卫生组织中枢神经系统分类(World Health Organization central nervous system,WHO CNS)深化了分子分型对LGGs诊疗的重要性,以LGGs分子分型进行病理分级升级诊断。影像组学作为新兴领域,可术前无创预测LGGs分子分型,为LGGs的治疗评估及预后预测提供依据。目前已有较多研究通过分析MRI常规序列、功能序列,结合临床信息,运用机器学习和深度学习建立影像组学模型,术前无创预测LGGs分子分型。虽然其存在局限性,但仍具有一定的科研与临床意义。本文就LGGs分子分型在MRI影像组学的研究进展予以综述,以期通过MRI影像组学术前预测LGGs分子分型,便于指导临床个体化诊疗方案制订及预后预测。Glioma is the most common primary malignant tumor of brain parenchyma.Gliomas are divided into grades 1-4,of which grades 2 and 3 are called lower-grade gliomas(LGGs).The 2021 World Health Organization Central Nervous System(WHO CNS)deepens the importance of molecular typing for the diagnosis and treatment of LGGs,using LGGs molecular typing for pathological grading upgrade diagnosis.As an emerging field,radiomics can noninvasively predict the molecular subtypes of LGGs before surgery,providing a basis for treatment evaluation and prognosis prediction of LGGs.At present,many studies have established radiomics models by analyzing MRI routine sequences,functional sequences,combined with clinical information,using machine learning and deep learning to predict LGGs molecular typing noninvasively before surgery.Although it has limitations,it still has certain scientific research and clinical significance.This article reviews the research progress of LGGs molecular typing in MRI radiomics,in order to predict LGGs molecular typing by MRI radiomics,and to facilitate the formulation of clinical individualized diagnosis and treatment plans and prognosis prediction.
关 键 词:较低级别脑胶质瘤 脑胶质瘤 磁共振成像 影像组学 分子分型
分 类 号:R445.2[医药卫生—影像医学与核医学] R730.264[医药卫生—诊断学]
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