Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Serum uric acid-to-HDL cholesterol ratio and stroke prevalence: NHANES 1999-2018 with external support from an imaging-confirmed hemorrhagic stroke dataset.

Frontiers in neurology·2026
Same author

Integrative multi-omics analysis and machine learning identify M2 macrophage-induced ferroptosis resistance in glioblastoma.

Functional & integrative genomics·2026
Same author

Morphology spectrum and molecular landscape in eosinophilic solid and cystic renal cell carcinoma.

Human pathology·2026
Same author

Incidental Finding of Secondary Focal Segmental Glomerulosclerosis in Renal Allograft due to Renal Artery Stenosis.

Pediatric transplantation·2026
Same author

The global hydrogen budget.

Nature·2025
Same author

TRIM67 Suppresses Glioblastoma Glycolysis and Increases Temozolomide Sensitivity by Promoting ARSD Ubiquitinated Degradation to Inactivate the β-Catenin Pathway.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2025

相关实验视频

Updated: Jun 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

基于机器学习的综合整合开发了一种新的质母细胞瘤预后模型.

Qian Jiang1, Xiawei Yang2, Teng Deng1

  • 1Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.

Molecular therapy. Oncology
|July 29, 2024
PubMed
概括

这项研究引入了一种新的人工智能预后签名 (AIPS),用于质母细胞瘤 (GBM),可以准确预测患者的结果并指导治疗策略.

关键词:
质母细胞瘤 (glioblastoma) 是一个免疫疗法. 免疫疗法.机器学习是机器学习.预后签名 预测签名瘤免疫的微环境

更多相关视频

Glioblastoma Relapse Post-Resection Model for Therapeutic Hydrogel Investigations
04:46

Glioblastoma Relapse Post-Resection Model for Therapeutic Hydrogel Investigations

Published on: February 24, 2023

1.5K
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

7.8K

相关实验视频

Last Updated: Jun 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Glioblastoma Relapse Post-Resection Model for Therapeutic Hydrogel Investigations
04:46

Glioblastoma Relapse Post-Resection Model for Therapeutic Hydrogel Investigations

Published on: February 24, 2023

1.5K
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

7.8K

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 质母细胞瘤 (GBM) 仍然是一个具有挑战性的脑瘤,具有有限的有效预后模型.
  • 准确预测GBM患者的预后对于开发个性化治疗策略至关重要.

研究的目的:

  • 开发和验证一种基于机器学习的新型,集成的质母细胞瘤预后模型.
  • 将新模型的性能与现有的预测工具进行比较.
  • 研究新的预后特征与瘤免疫微环境 (TIME) 和免疫治疗反应之间的关系.

主要方法:

  • 单变Cox回归分析用于识别6个GBM队列中的预后基因.
  • 通过将10个机器学习算法集成到117个组合中,开发了一种人工智能预测签名 (AIPS).
  • 用C指数评估AIPS的表现,并与之前发布的10个模型进行比较.

主要成果:

  • 使用随机生存森林算法的AIPS实现了最高的平均C指数 (0.868),超过了现有的模型.
  • 该AIPS表明与GBM临床特征有很强的相关性.
  • 较低的AIPS得分与更好的预后,更活跃的时间和增强的免疫治疗敏感性有关.

结论:

  • 开发的AIPS作为质母细胞瘤的有效预后工具.
  • 这个签名为分层GBM患者和优化治疗方法提供了宝贵的见解.
  • 通过西部涂抹和免疫组织化学进一步验证关键基因表达,支持AIPS的发现.