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

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...

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

Biomarkers and Prognosis After Myocardial Infarction: Towards Precision Cardiology.

European heart journal. Quality of care & clinical outcomes·2026
Same author

Case Report: From metabolic normalization to incidental type A aortic dissection in immune checkpoint inhibitor-associated aortitis.

Frontiers in oncology·2026
Same author

Prediction of Lower Third Molar Eruption in Panoramic Radiography Using Artificial Intelligence (AI): PDApp.

Diagnostics (Basel, Switzerland)·2026
Same author

Quantitative Dual-Energy CT in Abdominal Imaging: Technical Considerations and Emerging Clinical Applications.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Predicting Stroke Etiology with Radiomics: A Retrospective Study.

Medical sciences (Basel, Switzerland)·2025
Same author

MRI's evolving role in rectal cancer in the era of personalized medicine.

Abdominal radiology (New York)·2025
Same journal

Precision Proteomic Profiling of Systemic Lupus Erythematosus-Correlating Disease Activity and Complement Levels with Clinical Phenotypes.

Biomedicines·2026
Same journal

The Role of Salivary Microbiota in Pancreatic Cancer: From Screening to Tumor Progression and Treatment Response.

Biomedicines·2026
Same journal

Diagnostic Utility of Surface Electromyography for Identifying Muscles Affected by Myofascial Trigger Points: A Scoping Review.

Biomedicines·2026
Same journal

Performance Assessment of a Locally Semi-Automated NGS-Based Workflow for Homologous Recombination Deficiency Testing in High-Grade Serous Ovarian Carcinoma.

Biomedicines·2026
Same journal

Coupling and Uncoupling Pleiotropy Between Hypertension and Type 2 Diabetes Contribute to Exploring Potential Heterogeneity in Cardiovascular Risk in East Asian Population.

Biomedicines·2026
Same journal

Maternal Response to Therapeutic Plasma Exchange in Early Gestation: A Case Series of Thrombotic Microangiopathies and Neurological Disorders.

Biomedicines·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2026

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

基于CT的放射学预测CRC患者的KRAS突变使用机器学习算法:一项回顾性研究

Jacobo Porto-Álvarez1, Eva Cernadas2, Rebeca Aldaz Martínez1

  • 1Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain.

Biomedicines
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

基于计算机断层扫描 (CT) 的放射学可以预测结直肠癌 (CRC) 患者的KRAS突变. 这种非侵入性方法对管理CRC和在侵入性手术之前对患者进行潜在的诊断充满希望.

关键词:
在KRAS突变中发生突变.结肠直肠癌是什么意思辐射基因组学 辐射基因组学无线电学 (radiomics) 是一种无线电学.质地分析,质地分析.

更多相关视频

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging
04:43

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Published on: February 27, 2020

6.8K
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

相关实验视频

Last Updated: Jun 27, 2026

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
Detection of Lung Tumor Progression in Mice by Ultrasound Imaging
04:43

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Published on: February 27, 2020

6.8K
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

科学领域:

  • 在瘤学瘤学.
  • 放射学 放射学是一门学科.
  • 医疗成像医学成像

背景情况:

  • 大肠直肠癌 (CRC) 是一种普遍存在的全球性恶性瘤.
  • KRAS突变发生在30-50%的CRC病例中,并预测抗EGFR治疗的耐药性.
  • 准确预测KRAS突变状态对于有效的CRC治疗策略至关重要.

研究的目的:

  • 评估基于计算机断层扫描 (CT) 的放射学在预测CRC患者KRAS突变方面的有效性.
  • 探索CT成像特征与KRAS突变状态之间的相关性.
  • 评估放射学作为CRC患者管理的非侵入性工具的潜力.

主要方法:

  • 对56名确诊KRAS状态的CRC患者进行了回顾性研究.
  • 在治疗前从对比度增强CT (CECT) 扫描中提取放射性特征.
  • 应用各种机器学习分类器 (AdaBoost,神经网络,决策树,SVM,随机森林) 进行预测.
  • 分析纹理描述器以识别与KRAS突变相关的成像模式.

主要成果:

  • 使用临床数据的AdaBoost组合实现了最高的预测准确度 (76.8%) 和kappa (53.7%),敏感度为73.3%,特异性为80.8%.
  • 纹理描述器的准确度为73.2%,kappa为46%,灵敏度为76.7%,特异性为69.2%.
  • 在CT纹理模式和KRAS突变状态之间观察到显著的相关性.

结论:

  • 基于CT的放射学可以有效地预测结直肠癌患者的KRAS突变.
  • 放射学为评估KRAS状态提供了一种有前途的非侵入性方法,有可能指导治疗决策.
  • 这种方法可能在未来在早期诊断和治疗CRC方面发挥作用,减少对侵入性手术的需要.