勘误：胶质母细胞瘤治疗反应的影像学生物标志物：近期机器学习研究的系统评价与荟萃分析。
Erratum: Imaging biomarkers of glioblastoma treatment response: a systematic review and meta-analysis of recent machine learning studies.
出版信息
Front Oncol. 2023 May 24;13:1217461. doi: 10.3389/fonc.2023.1217461. eCollection 2023.
Abstract
[This corrects the article DOI: 10.3389/fonc.2022.799662.].
摘要
[本文更正了文章的数字对象标识符：10.3389/fonc.2022.799662。]
相似文献
1
Erratum: Imaging biomarkers of glioblastoma treatment response: a systematic review and meta-analysis of recent machine learning studies.勘误：胶质母细胞瘤治疗反应的影像学生物标志物：近期机器学习研究的系统评价与荟萃分析。
Front Oncol. 2023 May 24;13:1217461. doi: 10.3389/fonc.2023.1217461. eCollection 2023.
2
Erratum: Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status.勘误：基于位置特征对不同切除状态的胶质母细胞瘤患者生存预测的疗效。
Front Oncol. 2021 Aug 5;11:745820. doi: 10.3389/fonc.2021.745820. eCollection 2021.
3
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.机器学习在神经影像学中用于胶质瘤检测和分类的应用：人工智能增强的系统评价。
J Clin Neurosci. 2021 Jul;89:177-198. doi: 10.1016/j.jocn.2021.04.043. Epub 2021 May 13.
4
Corrigendum: Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.勘误：基于CT的深度学习或影像组学预测胃癌新辅助化疗反应的荟萃分析与系统评价
Front Oncol. 2024 May 23;14:1433346. doi: 10.3389/fonc.2024.1433346. eCollection 2024.
5
Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.勘误：基于机器学习的磁共振影像组学分析用于胶质肉瘤和胶质母细胞瘤的分类
Front Oncol. 2021 Oct 18;11:774369. doi: 10.3389/fonc.2021.774369. eCollection 2021.
6
A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization.基于放射组学和深度学习预测模型的脑胶质瘤特征分析研究综述
Acad Radiol. 2021 Nov;28(11):1599-1621. doi: 10.1016/j.acra.2020.06.016. Epub 2020 Jul 10.
7
Erratum: Pertinence of glioma and single nucleotide polymorphism of TERT, CCDC26, CDKN2A/B, and RTEL1 genes in glioma: a meta-analysis.
Front Oncol. 2024 Mar 11;14:1393063. doi: 10.3389/fonc.2024.1393063. eCollection 2024.
8
Corrigendum: Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis.勘误：人工智能应用于早期胃癌内镜诊断准确性的当前证据与未来展望：一项系统评价与Meta分析
Front Med (Lausanne). 2021 May 14;8:698483. doi: 10.3389/fmed.2021.698483. eCollection 2021.
9
Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study.勘误：肝细胞癌肝切除术后肝衰竭的机器学习预测模型：一项多中心研究。
Front Oncol. 2023 Aug 31;13:1278295. doi: 10.3389/fonc.2023.1278295. eCollection 2023.
10
Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging.优化神经肿瘤影像学：胶质瘤成像深度学习方法综述
Cancers (Basel). 2019 Jun 14;11(6):829. doi: 10.3390/cancers11060829.
Zotero 插件