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

5.1K
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...
5.1K

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

Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.

Bioengineering (Basel, Switzerland)·2026
Same author

Cholesterol-Mediated Metabolic-mechanotransductive Crosstalk Orchestrates Castration Resistance in Prostate Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

CD248 acts as a mechanosensory switch in fibroblast subsets to establish distinct pathological niches in renal fibrosis.

Nature communications·2026
Same author

PSMA-Targeting Macrophage Membrane-Coated Nanoparticles for Precision Diagnosis and Combination Therapy of Prostate Cancer.

Exploration (Beijing, China)·2026
Same author

Simulation-Based X-Ray Spectrum Optimization for Dose Enhancement in X-Ray-Induced Photodynamic Therapy with NaLuF<sub>4</sub>:20% Gd, 15% Tb<sup>3+</sup> Nanocrystals.

Bioengineering (Basel, Switzerland)·2026
Same author

Integrative Cross-Modal Fusion of Preoperative MRI and Histopathological Signatures for Improved Survival Prediction in Glioblastoma.

Bioengineering (Basel, Switzerland)·2026

相关实验视频

Updated: Jun 29, 2025

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.5K

使用弱监督的深度学习模型检测MRI隐形的前列腺癌.

Yao Zheng1, Jingliang Zhang2, Dong Huang1

  • 1School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, Shaanxi, China.

International journal of biomedical imaging
|March 27, 2024
PubMed
概括

一个新的AI模型,弱监督的UNet (WSUNet),有效地检测MRI隐形前列腺癌 (MIPCas). 这通过提高精度和减少所需的活检针数量来减少不必要的活检.

更多相关视频

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

189
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

6.8K

相关实验视频

Last Updated: Jun 29, 2025

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.5K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

189
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

6.8K

科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 磁共振成像 (MRI) 对于前列腺癌的检测至关重要.
  • 由于外观与正常组织相似,MRI隐形前列腺癌 (MIPCas) 存在诊断挑战.
  • 为了识别MIPCas,通常需要进行广泛的系统活检.

研究的目的:

  • 开发和验证一个弱监督的UNet (WSUNet) 模型来检测MIPCas.
  • 评估WSUNet在改善前列腺癌诊断和减少不必要的活检方面的有效性.

主要方法:

  • 一组777名患者 (600名培训,177名测试) 接受了MRI-超声波融合导向活检.
  • 从系统活检结果中,根据格里森等级 (≥7) 确定了MIPC.
  • 使用测试集开发和验证了WSUNet模型.

主要成果:

  • 在测试组中,WSUNet在测试组中实现了0.764的AUC (95%CI:0.728-0.798).
  • 该模型显示,比传统方法的精度提高了91.3% (p < 0.01).
  • 不必要的活检针减少了47.6% (p < 0.01),同时保持了检测率.

结论:

  • 开发的WSUNet模型有效地检测MRI隐形的前列腺癌.
  • WSUNet显著减少了不必要的前列腺活检的需要.