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

Peripheral Artery Disease V: Postoperative Nursing Management01:23

Peripheral Artery Disease V: Postoperative Nursing Management

630
During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
630

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

[Survey on the occupational musculoskeletal disorder and its risk factors among male steelworkers].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2013
Same author

Characterisation and identification of dihydroindole-type alkaloids from processed semen strychni by high-performance liquid chromatography coupled with electrospray ionisation ion trap time-of-flight mass spectrometry.

Phytochemical analysis : PCA·2013
Same author

Identification and effect decomposition of risk factors for Brucella contamination of raw whole milk in china.

PloS one·2013
Same author

Different toxicity of the novel Bacillus thuringiensis (Bacillales: Bacillaceae) strain LLP29 against Aedes albopictus and Culex quinquefasciatus (Diptera: Culicidae).

Journal of economic entomology·2013
Same author

Study on the growth and the photosynthetic characteristics of low energy C(+) ion implantation on peanut.

PloS one·2013
Same author

Proteomic analysis reveals that proteasome subunit beta 6 is involved in hypoxia-induced pulmonary vascular remodeling in rats.

PloS one·2013

相关实验视频

Updated: May 5, 2026

A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies
07:15

A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies

Published on: July 28, 2020

10.4K

预测手术后的术后复发风险在软组织末肢和干部的肉瘤使用MRI-based Nomogram.

Ruihuan Wang1, Shilong Wang2, Lei Xu1

  • 1Department of Radiology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), No.300 Guangzhou Road, Nanjing 210029, China (A.W., L.X., Y.W.).

Academic radiology
|March 13, 2026
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个名图,以利用MRI和临床数据预测软组织肉瘤 (STS) 复发风险. 该工具准确地识别高风险患者进行个性化治疗.

关键词:
深度学习是一种深度学习.预测 预后 预测 预测无线电学 (Radiomics) 是一种无线电学.软组织肉瘤 (软组织肉瘤)

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

988
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

842

相关实验视频

Last Updated: May 5, 2026

A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies
07:15

A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies

Published on: July 28, 2020

10.4K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

988
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

842

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:

  • 软组织肉瘤 (STS) 复发是一个重大的临床挑战.
  • 准确预测STS复发风险对于有效的患者管理至关重要.
  • 当前的预测模型可能无法完全整合各种数据源.

研究的目的:

  • 开发和验证一个全面的诺姆图,用于预测软组织肉瘤 (STS) 患者的3年复发风险.
  • 整合手术前的MRI衍生的放射学和临床放射学因素,以提高预测准确度.
  • 为识别高风险的STS患者提供一种工具,这些患者可以接受个性化治疗策略.

主要方法:

  • 分析了一组202名接受手术切除的STS患者.
  • 从CE-T1WI和FS-T2WIMRI序列中提取了放射性特征.
  • 深度学习模型 (VGG11,ResNet18),放射学和临床放射学数据被整合到一个名ogram中.

主要成果:

  • 三年后的术后复发率为47.52%.
  • 诺米图表表现出了出色的预测性能,AUC为0.874 (内部) 和0.822 (外部) 验证.
  • 命名图实现了0.746 (内部) 和0.690 (外部) 的一致性指数,具有显著的预后分层 (p < 0.01).

结论:

  • 开发的诺米图有效预测软组织肉瘤的3年复发风险.
  • 该工具有助于识别可能受益于量身定制的治疗干预措施的高风险患者.
  • 诺米图支持STS患者个性化治疗规划.