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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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

Combining Conventional MRI and DCE-MRI Radiomics for Prediction of Breast Cancer Molecular Subtypes: A Retrospective Single-Center Cross-Sectional Study.

La Clinica terapeutica·2026
Same author

Quantitative hippocampal subfield volumetry using AID-HS in mesial temporal lobe epilepsy: correlation with histopathology.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2026
Same author

Focal Leptomeningeal Metastasis Mimicking Inflammatory Lesion: Role of Serial MRI and MR Spectroscopy.

Medical archives (Sarajevo, Bosnia and Herzegovina)·2026
Same author

Comparison Between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion Weighted Imaging (DWI) in Supratentorial Glioma Grading in Children.

Medical archives (Sarajevo, Bosnia and Herzegovina)·2026
Same author

Silver-dotted titanium dioxide nanocomposites for highly efficient photo-induced enhanced Raman spectroscopy in trace detection of weak-Raman-response molecules.

RSC advances·2025
Same author

Role of Anti-Vinculin Quantitative ELISA Test in Diagnosing Irritable Bowel Syndrome and Inflammatory Bowel Disease.

Medical archives (Sarajevo, Bosnia and Herzegovina)·2025

相关实验视频

Updated: Jul 13, 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

7.3K

来自MRI的放射学用于分类乳腺癌分子亚型:一种建模方法.

Tran Thi Hue1,2, Nguyen Thu Huong2, Tran Quoc Long3

  • 1Department of Radiology, Hanoi Medical University, Hanoi, Vietnam.

Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH
|November 24, 2025
PubMed
概括

核磁共振射线学可以预测乳腺癌分子亚型,对三阴性乳腺癌 (TNBC) 和HER2丰富型的乳腺癌显示高精度. 虽然区分光线A (LA) 和光线B (LB) 仍然具有挑战性,但这种方法有助于精确瘤学.

关键词:
这就是为什么MRI是MRI.乳腺癌 乳腺癌 乳腺癌分子子类型 分子子类型无线电学 (radiomics) 是一种无线电学.

更多相关视频

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.0K

相关实验视频

Last Updated: Jul 13, 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

7.3K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.0K

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:

  • 乳腺癌是一种异质性疾病,有四种主要的分子亚型影响预后和治疗.
  • 准确的亚型识别对于有效的患者管理至关重要.
  • 基于MRI的放射学提供了一种非侵入性的方法来评估瘤异质性和预测亚型.

研究的目的:

  • 开发和验证使用MRI衍生放射性特征的后勤回归模型.
  • 预测侵袭性乳腺癌的四种主要分子亚型:光线A (LA),光线B (LB),HER2丰富 (HER2) 和三阴性乳腺癌 (TNBC).

主要方法:

  • 对169名侵袭性乳腺癌患者的回顾性分析,这些患者接受了治疗前的DCE-MRI.
  • 使用LIFEx.Ex.进行放射性纹理特征的提取和z-score规范化.
  • 通过L1-规范化后勤回归 (LASSO) 选择特征,并训练四种一对其余后勤回归模型,并进行5倍交叉验证.

主要成果:

  • 这些模型实现了TNBC的AUC为0.840,HER2为0.788,LA为0.661,LB为0.635.
  • 对TNBC (0.923) 观察到的精度最高,对LB (0.393) 敏感度最低.
  • 对TNBC和HER2的分类表现良好,但在LA和LB之间经常有错误分类. TNBC的特征主要是基于强度和.

结论:

  • 来自MRI的放射性特征可以非侵入性地区分乳腺癌分子表型.
  • 该模型显示了TNBC和HER2亚型的强大预测性能.
  • 拉索-逻辑回归框架显示出作为精密瘤学的决策支持工具的潜力,尽管LA-LB区分存在局限性.