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

303
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...
303

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

Multivariate structure of semen quality and growth performance in Limousin beef bulls: a CoSTATIS approach.

Journal of animal science·2026
Same author

Providing training and support to Spanish dementia caregivers living in rural and urban areas: insights and results from the iSupport-Sp study.

International journal for equity in health·2026
Same author

Publisher Correction: Deciphering the role of complement system genes in pancreatic cancer susceptibility and prognosis.

Nature communications·2026
Same author

A three-gene resistome signature as a prognostic tool in hepatocellular carcinoma.

Annals of hepatology·2025
Same author

Deciphering the role of complement system genes in pancreatic cancer susceptibility and prognosis.

Nature communications·2025
Same author

The PCovR biplot: a graphical tool for principal covariates regression.

Journal of applied statistics·2025

相关实验视频

Updated: May 14, 2025

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.4K

优化乳腺癌后淋巴管理:临床实践中的预测风险模型.

Enrique Cano-Lallave1, Elisa Frutos-Bernal2, María Anciones-Polo2

  • 1Rehabilitation Service, University Hospital of Salamanca, Salamanca, Spain.

Journal of surgical oncology
|May 13, 2025
PubMed
概括

乳腺癌治疗可能会导致淋巴. 这项研究开发了使用患者数据和治疗细节的预测工具,以识别高风险个体,以便更好地预防和管理.

关键词:
乳腺癌 乳腺癌 乳腺癌淋巴腺切除术是指淋巴腺切除术.淋巴发 淋巴发 淋巴发预测工具 预测工具有关风险因素的风险因素.

更多相关视频

A Revised Method for Inducing Secondary Lymphedema in the Hindlimb of Mice
09:50

A Revised Method for Inducing Secondary Lymphedema in the Hindlimb of Mice

Published on: November 2, 2019

8.0K
A Murine Tail Lymphedema Model
04:38

A Murine Tail Lymphedema Model

Published on: February 10, 2021

5.6K

相关实验视频

Last Updated: May 14, 2025

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.4K
A Revised Method for Inducing Secondary Lymphedema in the Hindlimb of Mice
09:50

A Revised Method for Inducing Secondary Lymphedema in the Hindlimb of Mice

Published on: November 2, 2019

8.0K
A Murine Tail Lymphedema Model
04:38

A Murine Tail Lymphedema Model

Published on: February 10, 2021

5.6K

科学领域:

  • 在瘤学瘤学.
  • 康复医学 康复医学 康复医学
  • 医学统计 医学统计

背景情况:

  • 淋巴是乳腺癌治疗后的常见并发症,对患者的生活质量产生负面影响.
  • 目前用于评估个体淋巴胀风险的现有方法是不够的.
  • 这项研究解决了改善乳腺癌患者风险分层的需要.

研究的目的:

  • 开发用于乳腺癌治疗后淋巴风险评估的预测工具.
  • 将患者特征,瘤特征和治疗方法整合到预测模型中.
  • 优化临床监测,预防策略和早期诊断淋巴.

主要方法:

  • 分析了309名经过淋巴切除术的乳腺癌患者的数据.
  • 包括患者的人口统计,瘤临床病理特征和治疗细节.
  • 单变量和多变量回归分析的应用以及风险预测的诺摩格拉姆开发.

主要成果:

  • 淋巴的累积发病率为18.4%.
  • 确定的独立风险因素包括高BMI,久坐不动的生活方式,N阶段和特定的放射治疗领域.
  • 预测模型实现了0.75的AUC,表明了良好的预测性能.

结论:

  • 开发的预测工具使医疗保健专业人员能够识别患有淋巴结胀高风险的患者.
  • 这些工具支持实施个性化的预防和管理策略.
  • 加强风险识别可以改善患者的治疗结果和生活质量.