磁共振成像在原发性脑外肿瘤鉴别诊断中的应 用:放射组学研究综述
- 作者: Kapishnikov A.V.1, Surovcev E.N.1,2
-
隶属关系:
- Samara State Medical University
- Dr. Sergey Berezin Medical Institute (MIBS)
- 期: 卷 4, 编号 4 (2023)
- 页面: 529-542
- 栏目: 系统评价
- URL: https://journals.rcsi.science/DD/article/view/262962
- DOI: https://doi.org/10.17816/DD569149
- ID: 262962
如何引用文章
详细
论证。磁共振成像数据分析是术前原发性脑外肿瘤鉴别诊断的主要方法。然而,仅凭对这些数据的目测评估很难准确区分不同的原发性脑外肿瘤。
放射组学是一种分析医学影像数据的定量方法。其允许确定成像数据与肿瘤表型和基因型特征之间的关系。
此前,一些分析性出版物总结了根据放射组学原理对原发性脑外肿瘤进行鉴别诊断的研究结果。随着新临床病例的迅速积累和相关研究的不断增加,有必要对其进行进一步分析和系统化。这就是本研究的基础。
该研究的目的是系统整理有关放射组学在原发性脑外肿瘤鉴别诊断方面潜力的现有数据。
材料与方法。我们搜索并分析了过去五年中用俄语和英语发表的出版物。搜索是在PubMed/Medline、Google Scholar和eLibrary数据库中进行。最终分析包括19篇关于原发性脑外肿瘤鉴别诊断的出版物。这些出版物包括用于肿瘤鉴别诊断的放射组学特征。
结果。所有研究都表明了,放射组学参数(纹理的和直方图的)与肿瘤类型之间存在相关性。通过放射组学模型对肿瘤进行鉴别诊断的效率优于放射科医生对肿瘤进行分类的效率。
为了创建肿瘤分类的模型,我们最常使用了以下算法:支持向量法、逻辑回归法和随机森林法。支持向量法和逻辑回归法显示出更好、更稳定的结果。
结论。放射组学概念在原发性脑外肿瘤鉴别诊断中的应用显示出良好效果。这一方向的进一步发展需要分割方法和特征集的标准化,以及有效的数学建模方法。
作者简介
Aleksandr V. Kapishnikov
Samara State Medical University
Email: a.v.kapishnikov@samsmu.ru
ORCID iD: 0000-0002-6858-372X
SPIN 代码: 6213-7455
Scopus 作者 ID: 6507900025
MD, Dr. Sci. (Med.), Professor
俄罗斯联邦, SamaraEvgeniy N. Surovcev
Samara State Medical University; Dr. Sergey Berezin Medical Institute (MIBS)
编辑信件的主要联系方式.
Email: evgeniisurovcev@mail.ru
ORCID iD: 0000-0002-8236-833X
SPIN 代码: 5252-5661
Scopus 作者 ID: 57224906215
俄罗斯联邦, Samara; Togliatti
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