Jove
Visualize
联系我们

相关概念视频

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

4.7K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
4.7K
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
  1. 首页
  3. 机器学习模型的开发 整合病理学和临床数据 预测支淋巴结转移 乳腺癌:两中心研究
  1. 首页
  3. 机器学习模型的开发 整合病理学和临床数据 预测支淋巴结转移 乳腺癌:两中心研究

相关实验视频

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

8.3K

机器学习模型的开发 整合病理学和临床数据 预测支淋巴结转移 乳腺癌:两中心研究

Long Wang1, Fanli Qu1, Ping Wen2

  • 1Department of Breast Cancer Center, Chongqing University Cancer Hospital, Chongqing, China.

Cancer reports (Hoboken, N.J.)
|September 1, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究开发了一种基于病理学的名图,用于预测乳腺癌 (BC) 患者的下淋巴结转移 (ALNM). 它可以提高手术前预测的准确性, 帮助个性化治疗计划.

关键词:
带淋巴结转移乳腺癌机器学习编号模型病理学

更多相关视频

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

6.9K
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

193

相关实验视频

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

8.3K
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

6.9K
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

193

科学领域:

  • 癌症学
  • 辐射学
  • 数字病理学

背景情况:

  • 准确评估下淋巴结 (ALN) 对于乳腺癌 (BC) 治疗计划至关重要.
  • 在手术前预测ALN转移 (ALNM) 仍然是一个挑战.

研究的目的:

  • 开发和验证一个新的整合病理特征的诺米克,用于BC的ALNM手术前预测.
  • 提高诊断准确性和指导个性化治疗策略.

主要方法:

  • 两所医院的407名BC患者的数字H&E染色图像的回顾性分析.
  • 使用曼-惠特尼U测试,斯皮尔曼等级相关性和LASSO回归的特征选择.
  • 使用逻辑回归进行内部和外部验证的命名图的开发.

主要成果:

  • 除了ER,HER2和瘤大小,病理特征被确定为ALNM的独立预测因素.
  • 与现有模型相比,基于病理学的诺图表显示出更高的预测性能 (IVC中的AUC为0. 783,EVC中的0. 738).
  • 决策曲线的分析证实了诺莫图的临床实用性和益处.

结论:

  • 病理特征在预测乳腺癌患者的ALNM方面是有效的.
  • 开发的基于病理学的诺姆图是个性化手术前评估和治疗规划的宝贵工具.