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本文引用的文献

1
Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.不一致的分区和非生产性特征关联产生理想化的放射组学模型。
Radiology. 2023 Apr;307(1):e220715. doi: 10.1148/radiol.220715. Epub 2022 Dec 20.
2
Radiomics in neuro-oncological clinical trials.神经肿瘤学临床试验中的放射组学。
Lancet Digit Health. 2022 Nov;4(11):e841-e849. doi: 10.1016/S2589-7500(22)00144-3. Epub 2022 Sep 28.
3
Promoter Methylation as a Prognostic Factor in Primary Glioblastoma: A Single-Institution Observational Study.启动子甲基化作为原发性胶质母细胞瘤的预后因素：一项单机构观察性研究
Biomedicines. 2022 Aug 20;10(8):2030. doi: 10.3390/biomedicines10082030.
4
Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent.用于高级别胶质瘤假进展预测的放射组学：磁共振造影剂的附加价值
Heliyon. 2022 Aug 2;8(8):e10023. doi: 10.1016/j.heliyon.2022.e10023. eCollection 2022 Aug.
5
Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis.高级别胶质瘤中假性进展和真性进展的鉴别因素：系统评价和荟萃分析。
Sci Rep. 2022 Aug 2;12(1):13258. doi: 10.1038/s41598-022-16726-x.
6
Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics.使用深度学习与放射组学预测弥漫性胶质瘤中的MGMT启动子甲基化
J Clin Med. 2022 Jun 15;11(12):3445. doi: 10.3390/jcm11123445.
7
Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study.胶质母细胞瘤患者磁共振成像中影像组学特征的稳健性：多中心研究。
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Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis.放射组学可区分高级别胶质瘤与脑转移瘤：一项系统评价和荟萃分析。
Eur Radiol. 2022 Nov;32(11):8039-8051. doi: 10.1007/s00330-022-08828-x. Epub 2022 May 19.
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磁共振成像放射组学及其在胶质母细胞瘤中的潜在应用。

MRI radiomics and potential applications to glioblastoma.

作者信息

Hooper Grayson W, Ginat Daniel T

机构信息

Landstuhl Regional Medical Center, Department of Radiology, Landstuhl, Germany.

University of Chicago, Department of Radiology, Chicago, IL, United States.

出版信息

Front Oncol. 2023 Feb 17;13:1134109. doi: 10.3389/fonc.2023.1134109. eCollection 2023.

DOI:10.3389/fonc.2023.1134109
PMID:36874083
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9982088/
Abstract

MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.

摘要

磁共振成像（MRI）在胶质母细胞瘤的初始诊断及治疗后的随访评估中均发挥着重要作用。定量分析的放射组学能够在鉴别诊断、基因分型、治疗反应及预后等方面为MRI解读提供深入见解，从而增强其解读能力。本文将对胶质母细胞瘤的各种MRI放射组学特征进行综述。