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

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

495
This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction...
495
Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy05:52

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy

13.0K
This protocol describes the identification and resection of sentinel lymph nodes to make the operation as easy and minimally invasive as...
13.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery06:46

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

648
The study found that male gender, poor tumor grade, and advanced Tumor Node Metastasis stage were associated with poorer cancer-specific survival (CSS) in multiple primary colorectal cancer (MPCC) patients after surgery. We developed a nomogram to predict the CSS of MPCC patients and contribute to clinical treatment...
648
Lymph Node Exam08:06

Lymph Node Exam

416.9K
Source: Richard Glickman-Simon, MD, Assistant Professor, Department of Public Health and Community Medicine, Tufts University School of Medicine, MA
The lymphatic system has two main functions: to return extracellular fluid back to the venous circulation and to expose antigenic substances to the immune system. As the collected fluid passes through lymphatic channels on its way back to the systemic circulation, it encounters multiple nodes consisting of highly concentrated clusters of...
416.9K
Draining Lymph Node Metastasis Model for Assessing the Dynamics of Antigen-Specific CD8+ T Cells During Tumorigenesis07:45

Draining Lymph Node Metastasis Model for Assessing the Dynamics of Antigen-Specific CD8+ T Cells During Tumorigenesis

2.7K
The experimental design presented here provides a useful reproductive model for the studies of antigen-specific CD8+ T cells during lymph node (LN) metastasis, which excludes the perturbation of bystander CD8+ T...
2.7K
Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma07:59

Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma

7.0K
Portal vein injection of colorectal cancer (CRC) organoids generates stroma-rich liver metastasis. This mouse model of CRC hepatic metastasis represents a useful tool to study tumor-stroma interactions and develop novel stroma-directed therapeutics such as adeno-associated virus-mediated gene...
7.0K

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 molybdenum-promoted nickel-aluminum alloy catalyst for high-efficient hydrogenation reduction of nitrate to ammonia and nitrogen.

RSC advances·2026
Same author

Single-cell T-cell landscape in atherosclerosis: implications for targeted therapy and beyond.

Journal of translational medicine·2026
Same author

Preparation and Electrochemical Performance of Macroporous High-Entropy Perovskite Oxide La(Fe<b><sub>0.25</sub></b>Ni<b><sub>0.25</sub></b>Co<b><sub>0.25</sub></b>Mn<b><sub>0.25</sub></b>Cr<b><sub>0.25</sub></b>)O<b><sub>3</sub></b>.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Structural basis of a CDR3-embedded binding mechanism in a nanobody for sensitivity enhancement toward tenuazonic acid.

Analytical and bioanalytical chemistry·2026
Same author

Development and validation of a real-time computer-aided measuring system for colorectal polyp size (with video).

Gastroenterology report·2026
Same author

CD4-Based Chimeric Antigen Receptor (CAR)-T Cells With Resistance to HIV-1 Infection and Enhanced Anti-HIV Efficacy: Covalent Interaction Between CD4-CAR and HIV-1 Envelope Glycoprotein.

Journal of medical virology·2026
Same journal

Lessons from extended induction and practical evidence for improving tofacitinib therapy in ulcerative colitis.

World journal of gastroenterology·2026
Same journal

Small animal <i>ex vivo</i> machine perfusion of the liver: A comprehensive literature review.

World journal of gastroenterology·2026
Same journal

Comparable remission and health care use in real-world inflammatory bowel disease patients initiating originator biologics <i>vs</i> biosimilars.

World journal of gastroenterology·2026
Same journal

Diagnosis and management of metabolic dysfunction-associated steatohepatitis in patients with chronic hepatitis B infection.

World journal of gastroenterology·2026
Same journal

Simultaneous treatment of concomitant achalasia coexisting with epiphrenic diverticulum: The practice of submucosal tunneling technique.

World journal of gastroenterology·2026
Same journal

Lianhe Xiaozhi ointment ameliorates metabolic dysfunction-associated steatotic liver disease <i>via</i> peroxisome proliferator-activated receptor alpha pathway activation.

World journal of gastroenterology·2026
查看所有相关文章

相关实验视频

Updated: Jan 20, 2026

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

495

在结直肠癌中预测淋巴结转移,使用案例级多重实例学习.

Ling-Feng Zou1, Xuan-Bing Wang2,3, Jing-Wen Li1

  • 1Department of Pathology, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400021, China.

World journal of gastroenterology
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

一个新的案例级多个实例学习 (MIL) 框架显著改善了在晚期结直肠癌 (CRC) 中的淋巴结转移 (LNM) 预测. 这种结合病理学和临床数据的AI方法优于传统方法,可以更好地分层患者的风险.

关键词:
结肠直肠癌是一种癌症.深度学习是一种深度学习.组织病理学 组织病理学淋巴结转移是淋巴结的转移.多个实例的学习是多个实例的学习.

更多相关视频

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
05:52

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy

Published on: August 19, 2021

13.0K
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

648

相关实验视频

Last Updated: Jan 20, 2026

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

495
Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
05:52

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy

Published on: August 19, 2021

13.0K
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

648

科学领域:

  • 数字病理学数字病理学
  • 人工智能在瘤学中的应用
  • 大肠直肠癌的研究研究.

背景情况:

  • 准确的淋巴结转移 (LNM) 预测对于局部晚期 (T3/T4) 大肠直肠癌 (CRC) 管理至关重要.
  • 传统的组织病理学和幻灯片级深度学习与稀疏的,关键的转移性特征作斗争.

研究的目的:

  • 开发和验证一个案例级多级学习 (MIL) 框架.
  • 模仿病理学家全面审查,以改善T3/T4 CRC LNM预测.

主要方法:

  • 对130名T3/T4CRC患者的整片图像进行了回顾性分析.
  • 使用CONCH v1.5和UNI2-h深度学习模型的案例级MIL框架.
  • 病理特征与临床数据的整合;通过AUC评估的性能.

主要成果:

  • 案例级 MIL 框架的表现优于幻灯片级的培训 (CONCH v1.5 AUC: 0.899 与 0.814 相比).
  • 整合病理学和临床数据增强了预测 (顶级模型AUC:0.904与仅临床AUC:0.584).
  • 模型识别的区域与病理学家确认的高风险组织病理学特征保持一致.

结论:

  • 案例级 MIL 框架为高级 CRC 中的 LNM 预测提供了一种优越的方法.
  • 这种方法显示了风险分层和指导治疗决策的潜力.
  • 框架的进一步验证是合理的.