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

Cancer Survival Analysis01:21

Cancer Survival Analysis

345
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
345

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

Advancing Predictive Modeling of Inflammatory Bowel Disease (IBD) Flares: A Data-Driven Approach Using Lifestyle and Psychosocial Factors from a Remote Monitoring Platform.

Digestive diseases and sciences·2026
Same author

Challenges and opportunities for real-world evidence in clinical oncology-a view from the UK: proceedings of a national workshop.

ESMO real world data and digital oncology·2026
Same author

Benefits and Limitations of Real-World Patient-Reported Toxicity Symptom Monitoring for Guidelines and Care, as Perceived by Patients, Clinicians, and Guideline Developers.

Cancer medicine·2025
Same author

An Overview of Real-World Data Infrastructure for Cancer Research.

Clinical oncology (Royal College of Radiologists (Great Britain))·2024
Same author

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs).

Clinical and translational radiation oncology·2022
Same author

A Glimmer of Hope Within the Mountain of Hype - Reviewing the Role of Artificial Intelligence in Radiotherapy.

Clinical oncology (Royal College of Radiologists (Great Britain))·2021

相关实验视频

Updated: Jun 28, 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

联合学习生存模型和潜在的放射治疗决策支持非小细胞肺癌使用现实世界的数据进行影响评估.

M Field1, S Vinod1, G P Delaney1

  • 1South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia.

Clinical oncology (Royal College of Radiologists (Great Britain))
|April 17, 2024
PubMed
概括
此摘要是机器生成的。

使用联合学习的新模型预测了非小细胞肺癌 (NSCLC) 患者的生存率. 这种工具可以帮助个性化放射治疗决策,潜在地提高生存率.

关键词:
支持决定的决定支持.联合学习的联合学习.肺癌是一种肺癌.机器学习是机器学习.辐射瘤学 辐射瘤学

更多相关视频

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.7K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K

相关实验视频

Last Updated: Jun 28, 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
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.7K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K

科学领域:

  • 在瘤学瘤学.
  • 辐射疗法 辐射疗法
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 非小细胞肺癌 (NSCLC) 的治疗决策包括平衡治愈和息放射治疗.
  • 准确的生存预测对于优化不可操作的NSCLC治疗策略至关重要.
  • 联合学习提供了一种方法,可以使用来自多个机构的数据构建强大的模型,而不会影响隐私.

研究的目的:

  • 为不可手术的I-III期NSCLC患者开发一个两年总生存模型.
  • 在联合学习网络中利用常规放射瘤学数据.
  • 评估决策支持潜力,以指导治疗与息性放射治疗的选择.

主要方法:

  • 在七个诊所建立了一个联合的基础设施,用于数据提取,非识别和标准化.
  • 在2011-2016年的患者数据上训练了一种后勤回归模型,并在2017-2019年的数据上验证.
  • 使用ROC曲线,AUC,C指数,校准指标和Kaplan-Meier生存曲线来评估模型的性能.

主要成果:

  • 该研究包括1655名患者数据集,总体模型AUC为0.68.
  • 接受息性放射治疗的大量患者预计风险低至中等.
  • 基于模型预测的模拟治疗显示,估计两年生存率增加了11%.

结论:

  • 联合学习使得使用多机构数据开发和验证肺癌决策支持系统成为可能.
  • 开发的模型可以量化其在临床实践中的使用的影响.
  • 这种方法支持个性化医疗,通过整合常规患者数据来定制治疗决策.