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

A novel NAMPT activator ameliorates obesity by repressing ACSL1-dependent lipid synthesis.

Acta pharmaceutica Sinica. B·2026
Same author

A multicenter retrospective analysis of canine idiopathic epilepsy in China.

Frontiers in veterinary science·2026
Same author

Functionalized Fluorescent Nanodiamonds Reveal Therapeutic Protein Clearance Through ENDOTAC Linked to AUTOTAC.

Advanced healthcare materials·2026
Same author

Efficacy of NEPA for prevention of chemotherapy induced nausea and vomiting in head and neck cancer patients receiving cisplatin-based chemotherapy.

European journal of clinical pharmacology·2026
Same author

A glimpse into the future of model-informed drug discovery and development.

Advanced drug delivery reviews·2026
Same author

Individualized Prediction of Radiation Pneumonitis Using RP-GAN: Leveraging Global Lung Features and Explainable Artificial Intelligence.

Technology in cancer research & treatment·2026

相关实验视频

Updated: Jan 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K

用剂量引导的混合人工智能模型与深度和手工制作的放射学,用于解释性辐射皮肤炎预测乳腺癌VMATVMAT.

Tsair-Fwu Lee1,2,3, Ling-Chuan Chang-Chien1, Lawrence Tsai1

  • 1Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.

Cancers
|December 11, 2025
PubMed
概括

一个新的混合AI模型准确地预测了接受VMAT的乳腺癌患者的辐射皮肤炎. 这种方法整合了深度学习放射学,临床数据和剂量指标,以改善风险分层和个性化预防.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习 辐射学组合学习组合学习可解释的人工智能放射性皮肤炎是一种辐射皮肤炎.容量调节弧线疗法是指体积调节的弧线疗法.

更多相关视频

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

2.2K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K

相关实验视频

Last Updated: Jan 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

2.2K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K

科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 放射性皮肤炎 (RD) 是乳腺癌放射治疗的常见副作用.
  • 准确预测 RD 风险对于个性化预防策略至关重要.
  • 卷度调节弧线疗法 (VMAT) 是一种广泛使用的放射治疗技术.

研究的目的:

  • 开发和验证一种混合人工智能 (AI) 模型,用于预测VMAT治疗的乳腺癌患者的2级以上辐射皮肤炎 (RD).
  • 整合深度学习放射学 (DLR),手工制造放射学 (HCR),临床特征和剂量体积图 (DVH) 参数,以提高预测准确度.
  • 改善高风险个体的早期识别,以进行个性化预防.

主要方法:

  • 对148名接受VMAT治疗的乳腺癌患者进行了回顾性分析.
  • 使用PyRadiomics从CT图像中提取HCR特征和DLR特征,使用VGG16网络.
  • 使用后勤回归,随机森林,梯度增强和堆叠组合 (SE) 方法开发预测模型.
  • 使用夏普利添加式扩展 (SHAPs) 和梯度加权类激活映射 (Grad-CAM) 评估模型可解释性.

主要成果:

  • 混合人工智能模型整合了DLR,临床和DVH特征,实现了最高的预测性能 (AUC = 0.76).
  • 深度学习放射学模型的表现优于手工制作的放射学模型 (AUC = 0.72 与 0.66).
  • 堆叠组合方法始终比单个分类器提高了性能.
  • SHAP分析确定了DLR特征作为关键预测因素,Grad-CAM突出了高剂量皮下区域.

结论:

  • 拟议的混合人工智能框架在VMAT后提供准确和可解释的等级≥2RD预测.
  • 该模型为乳腺癌患者提供可靠的高风险分层.
  • 这种方法对个性化放射治疗治疗规划和预防具有潜在的临床实用性.