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

634
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...
634
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.9K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.9K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.5K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.5K

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

Comprehensive Safety and Efficacy Evaluation of Immunotherapy Combination Approaches Versus Tyrosine Kinase Inhibitor Monotherapy as First-Line Treatment of Hepatocellular Carcinoma: A Network and Individual Patient Data (IPD) Meta-Analysis.

Cancers·2026
Same author

Precision Endocrine-Based Combinations After CDK4/6 Inhibitor Progression in HR-Positive Metastatic Breast Cancer.

Drug design, development and therapy·2026
Same author

Point-of-Care Manufacturing of Anti-CD19.1-Chimeric Antigen Receptor-T Cells Using CliniMACS Prodigy: Real-World Experience From Jordan.

JCO global oncology·2026
Same author

Minimally invasive circulating MicroRNA signatures for breast cancer detection and neoadjuvant therapy response prediction in a multicenter MENA cohort.

Journal of translational medicine·2026
Same author

Advancing Germline Genetic Testing for Breast Cancer in Resource-Restricted Settings: Evidence, Barriers, and a Practical Roadmap for Implementation.

Breast cancer (Dove Medical Press)·2026
Same author

Radiation-induced Lymphopenia in Pediatric Medulloblastoma: A Predictor of Outcomes in a Middle-income Country?

Journal of pediatric hematology/oncology·2026

相关实验视频

Updated: Jan 11, 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

478

一个简化的新算法来预测21个基因的复发率得分.

Maher A Sughayer1, Bayan Maraqa1, Batool Qura'an2

  • 1Department of Pathology and Laboratory Medicine, King Hussein Cancer Center, Amman, Jordan.

World journal of oncology
|November 10, 2025
PubMed
概括

使用组织学等级和孕激素受体 (PR) 表达的新算法准确地预测型DX复发得分 (RS) 类别. 这种工具有助于乳腺癌治疗决策,特别是在分子测试有限的地方.

关键词:
21基因复发率得分 21基因复发率得分乳腺癌 乳腺癌 乳腺癌历史学级的档次是历史学级的.型DX是DX的型型.预测算法 预测算法孕激素受体是什么? 孕激素受体是什么?

更多相关视频

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

相关实验视频

Last Updated: Jan 11, 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

478
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

科学领域:

  • 在瘤学瘤学.
  • 病理学 病理学 病理学
  • 基因组学就是基因组学.

背景情况:

  • 21基因复发得分 (瘤型DX) 对于早期ER+/HER2乳腺癌的辅助化疗决定至关重要.
  • 型DX的高成本和有限的可用性需要使用常规病理参数的更简单的预测模型.

研究的目的:

  • 开发和验证一个实际的,基于规则的算法,用于预测Oncotype DX复发分数 (RS) 类别.
  • 为了利用组织学等级和孕激素受体 (PR) 表达率来进行风险分层.

主要方法:

  • 对528名ER+/HER2-早期乳腺癌患者进行回顾性研究,这些患者接受了型DX测试.
  • 随机分配到学习 (n=377) 和验证 (n=151) 集合.
  • 单变量分析和ROC曲线用于确定风险分层的组织学等级内的PR%切线.

主要成果:

  • 组织学等级和PR%与RS有显著的关联.
  • 1级瘤的风险较低;2级需要PR≥60%的低RS;3级需要PR<40%的高风险.
  • 该算法在验证集中实现了87.5%的灵敏度,100%的特异性和99%的整体准确性,对~65%的病例进行了分层.

结论:

  • 一个简化的算法使用组织学等级和PR%准确地预测型DX RS类别.
  • 这种工具可以在没有分子测试的情况下实现自信的风险分层,提供了低成本,实用的解决方案.
  • 对于临床决策而言,在资源有限的环境中尤其有利.