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

645
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...
645

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

Age at natural and surgical menopause and related factors: NHANES 2013-2023.

Menopause (New York, N.Y.)·2026
Same author

Impact of the Availability of Women's Health Clinics on Unwanted Pregnancy Among Active Duty Service Women.

Journal of women's health (2002)·2026
Same author

Perinatal Depression Among U.S. Active Duty Service Women.

Military medicine·2026
Same author

Prevalence of Familial Melanoma Genes and Cancer Risk Among Genomically Ascertained Individuals.

JAMA dermatology·2026
Same author

Human leukocyte antigen alleles associated with inhibitor development in severe hemophilia A: analysis of the "My Life, Our Future" hemophilia A cohort.

Journal of thrombosis and haemostasis : JTH·2026
Same author

The efficacy and safety of benznidazole in adults with seropositive indeterminate form, Trypanosoma cruzi infection: a systematic review and meta-analysis.

BMC infectious diseases·2025

相关实验视频

Updated: Jan 15, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

485

使用机器学习创建用于原发性前列腺癌的预后系统.

Kevin Guan1,2, Andy Guan1,3, Anwar E Ahmed1

  • 1F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences (USUHS), Bethesda, MD 20814, USA.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
概括

一个新的机器学习模型,EACCD,提高了前列腺癌分期的准确性. 它通过更好地分层患者进行个性化治疗,优于目前的AJCC TNM系统.

关键词:
在C指数中,指数是C指数.这是EACCDCD的EACCDCD.癌症的分期 癌症的分期这是一个登德罗图.机器学习是机器学习.前列腺癌是前列腺癌.它们的生存曲线.

更多相关视频

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
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

8.6K

相关实验视频

Last Updated: Jan 15, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

485
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
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

8.6K

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 癌症的分期对于治疗和预后至关重要.
  • AJCC TNM第9版 (2024) 是目前的前列腺癌标准,使用T,N,M,PSA和等级组.
  • 完善预后系统可以提高结果预测和个性化治疗.

研究的目的:

  • 开发和评估改善前列腺癌的预后框架.
  • 将一种新型机器学习方法的性能与已建立的AJCC分阶段系统进行比较.

主要方法:

  • 应用了无监督机器学习集成算法用于集癌症数据 (EACCD).
  • 开发了使用5个AJCC变量 (T,N,M,P,G) 的EACCD模型,并扩展到7个变量 (包括年龄和种族).
  • 利用来自国家癌症研究所SEER计划的前列腺癌患者数据.

主要成果:

  • EACCD有效地将患者分为不同的预后组,并分开生存曲线.
  • 七变量EACCD模型实现了0.8504的C指数,超过了AJCC的TNM系统 (C指数:0.7676).
  • 与当前的AJCC分期系统相比,EACCD方法显示出更高的预测准确性.

结论:

  • EACCD为局部性前列腺癌提供了一个更准确的预后框架.
  • 这种机器学习模型增强了风险分层,支持精确瘤学.
  • 建议在独立队列中进一步验证.