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

Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.

World journal of urology·2026
Same author

Exploring the advantages of mixed reality technology in Robot-assisted laparoscopic partial adrenalectomy.

World journal of surgical oncology·2026
Same author

Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer.

BMC cancer·2026
Same author

Machine learning model integrating radiomics and clinical features for predicting postoperative bleeding after percutaneous nephrolithotomy.

European journal of medical research·2026
Same author

A radiomics-driven machine learning model for predicting bladder cancer prognosis identifies genes associated with radiomic features.

Clinical & experimental metastasis·2025
Same author

Preliminary experience with minimally invasive transvaginal single-port laparoscopic vesicovaginal fistula repair: report of 10 cases.

International urology and nephrology·2025

相关实验视频

Updated: Jan 15, 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

7.4K

组装机器学习模型,用于预测膀癌中骨转移的发生.

Zhan Jiang Yu1, Xiang Da Xu1, Xin Chang Zou1

  • 1The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Frontiers in oncology
|October 13, 2025
PubMed
概括

机器学习模型准确地预测了膀癌 (BC) 的骨转移. 梯度增强机 (GBM) 模型表现出高性能,有助于个性化治疗和查膀癌骨转移 (BCBM).

关键词:
在 SEER 数据库中,我们可以看到膀癌:膀癌是一种癌症.骨转移的发生.机器学习是机器学习.预测价值是一个预测值.

更多相关视频

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone
06:53

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Published on: September 9, 2020

3.2K
Models of Bone Metastasis
08:49

Models of Bone Metastasis

Published on: September 4, 2012

43.0K

相关实验视频

Last Updated: Jan 15, 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

7.4K
Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone
06:53

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Published on: September 9, 2020

3.2K
Models of Bone Metastasis
08:49

Models of Bone Metastasis

Published on: September 4, 2012

43.0K

科学领域:

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

背景情况:

  • 晚期膀癌 (BC) 的骨转移 (BM) 是一个重要的预后指标.
  • 准确预测BC中的BM目前具有挑战性,影响患者的治疗结果.
  • 开发可靠的预测工具对于个性化临床管理至关重要.

研究的目的:

  • 开发和评估用于预测膀癌骨转移的机器学习模型.
  • 确定与BCBM相关的独立风险因素.
  • 提高BCBM的临床决策和查效率.

主要方法:

  • 利用了来自SEER数据库 (2010-2015) 和外部验证队列的数据.
  • 使用单变量和多变量逻辑回归识别的风险因素.
  • 开发并比较了七种机器学习模型:LR,SVM,GBM,NN,RF,XGB和KNN.
  • 使用AUC,精度,灵敏度和特异性评估模型性能.

主要成果:

  • 总共分析了22,114名BC患者,其中2.4%的人患有BM.
  • 确定的风险因素包括年龄,种族,瘤阶段 (T,N),组织学,等级和转移到其他器官.
  • 梯度增强机 (GBM) 模型在测试组中实现了最高的性能 (AUC 0.855,精度 0.813).
  • 在外部验证集中,GBM模型表现出强的性能 (AUC 0.766,精度 0.945).
  • 在GBM模型中,T阶段,N阶段和放射治疗史是最重要的预测因素.

结论:

  • 开发的GBM模型为预测BCBM提供了精确和个性化的方法.
  • 这种预测工具有可能改善膀癌患者的临床决策.
  • 加强BCBM的预测可以导致更有效的查策略和更好的患者管理.