广州医药 ›› 2024, Vol. 55 ›› Issue (3): 221-230.DOI: 10.3969/j.issn.1000-8535.2024.03.002
梁芳蓉, 杨蕊梦
收稿日期:
2023-11-01
出版日期:
2024-03-20
发布日期:
2024-04-12
通讯作者:
杨蕊梦,E-mail:eyruimengyang@scut.edu.cn
作者简介:
杨蕊梦 医学博士,主任医师,博士研究生导师,华南理工大学附属第二医院(广州市第一人民医院)放射科副主任,美国圣路易斯华盛顿大学医学院及暨南大学临床医学博士后; 获“广东省杰出青年医学人才”、“广州市医学重点人才”、“广州市医学骨干人才”、“广州市青年后备人才”等荣誉称号; 担任《中华放射学杂志》、《南方医科大学学报》、European Radiology、Acta Biomaterialia等杂志审稿专家,《广州医药》杂志编委; 主持国家自然科学基金3项,广州市重点实验室建设项目1项,省市级项目7项; 研究方向为恶性肿瘤分子-智能影像及磁共振新技术研究,以第一作者或通信作者发表SCI收录论文30余篇,申请并获授权国家发明专利11项; 担任中华医学会放射学分会青年学组委员、磁共振学组Youth Club成员、中国研究型医院学会磁共振专业委员会委员、广东省医学会放射医学分会委员、秘书兼磁共振学组副组长、广东省医师协会放射科医师分会委员兼泌尿生殖学组副组长
基金资助:
LIANG Fangrong, YANG Ruimeng
Received:
2023-11-01
Online:
2024-03-20
Published:
2024-04-12
摘要: 胶质瘤是颅内最常见的原发性恶性肿瘤,其分级对患者治疗方式的选择和预后至关重要。尽管目前组织病理学仍是其最为可靠的分级手段,但需通过有创性手术以获取组织样本,存在一定的风险。相较之下,磁共振成像(MRI)作为一种非侵入性影像诊断工具,在胶质瘤分级中发挥着不可或缺的作用。然而,传统MRI评估受限于医师个体主观性强和可重复性差的问题,一定程度上影响了准确的分级结果。近年来,影像组学技术的崭露头角为解决上述难题开辟了新视角,通过高通量提取影像数据特征捕捉并量化肿瘤的影像学表现,避免因主观因素而导致的不确定性,协助医师更准确地评估肿瘤的恶性程度。本文对近五年来MRI影像组学在胶质瘤术前分级预测方面的相关研究进行了简要综述,旨在为相关领域研究者提供有益的参考和借鉴,以推动MRI影像组学在临床实践中的应用。
梁芳蓉, 杨蕊梦. MRI影像组学在胶质瘤术前分级预测中的研究进展[J]. 广州医药, 2024, 55(3): 221-230.
LIANG Fangrong, YANG Ruimeng. Advancement in MRI radiomics for preoperative glioma grading prediction[J]. Guangzhou Medical Journal, 2024, 55(3): 221-230.
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