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

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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

A modified Delphi consensus on tenosynovial giant cell tumour and giant cell tumour of bone : a report from the Birmingham Orthopaedic Oncology Meeting (BOOM).

The bone & joint journal·2026
Same author

Complications of PI to PIII hemipelvic resections for intermediate and malignant tumours : a systematic review and meta-analysis.

Bone & joint open·2026
Same author

Patient-specific Ti-6Al-4V lattice implants for critical-sized, weightbearing limb reconstruction: average 45-month surgical, oncological, and functional follow-up.

Bone & joint open·2026
Same author

Electromyography of the coracobrachialis-Construct validity and implications for rehabilitation of anterior shoulder dislocation.

Shoulder & elbow·2026
Same author

Enhancing Brain Tumor Classification and Generalization Using DDPM-Generated MRI, Mutual Information and Ensemble Learning.

Technology in cancer research & treatment·2026
Same author

Evaluation of Experience, Training, and Hand Dominance on Drilling Accuracy in Orthopedic Surgeons-A Preliminary Study.

Medicina (Kaunas, Lithuania)·2026
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: May 11, 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

8.2K

基于成像的深度学习用于预测Desmoid瘤进展.

Rabih Fares1, Lilian D Atlan1, Ido Druckmann1

  • 1Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.

Journal of imaging
|May 24, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型可以使用基线MRI扫描以93%的准确度预测desmoid瘤 (DT) 的进展. 这种人工智能方法有助于对DT患者的风险分层和临床决策.

关键词:
这就是为什么MRI是MRI.人工智能的人工智能是人工智能.在决策过程中做出决定.深度学习是一种深度学习.这种瘤是desmoid瘤.

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

6.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

相关实验视频

Last Updated: May 11, 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

8.2K
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
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 德斯莫伊德瘤 (DTs) 是局部具有侵略性的瘤,导致显著的发病率.
  • 目前的监测方法,如用RECIST 1.1进行的MRI/CT,在检测DT反应和进展方面缺乏准确性.
  • 准确预测DT临床过程对于有效管理至关重要.

研究的目的:

  • 从基线MRI中识别独特的深度学习特征,这些特征与未来的desmoid瘤临床过程相关.
  • 开发一种人工智能模型来预测瘤进展.

主要方法:

  • 在2006年至2019年间对51名患有形瘤 (DTs) 的患者进行了回顾性分析.
  • 瘤细分在T2脂肪抑制,未经治疗的MRI序列上.
  • 深度学习软件应用于细分病变,并与临床数据进行比较.

主要成果:

  • 深度学习模型实现了93%的准确性 (±0.04) 和0.89的ROC (±0.08) 在独立预测临床进展从基线MRI.
  • 人工智能模型在识别预测DT行为的独特成像特征方面表现出显著的能力.

结论:

  • 基线MRI的深度学习分析可以准确地预测desmoid瘤进展.
  • 人工智能为风险分层和临床决策提供了一个有前途的工具,用于管理瘤.