相关实验视频

Updated: Sep 14, 2025

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

199

使用监测流行病学和最终结果 (SEER) 数据库进行回顾性研究,以预测宫癌中肺转移的风险和预后因素:诺莫грам开发和验证

Hao Jin1, Hairong Wang2, Junting Guo2

  • 1Clinical Research Management Department, Tianjin Cancer Hospital Airport Hospital, Tianjin, China.

Translational cancer research
|July 21, 2025
PubMed
概括

这项研究开发了用于预测宫癌患者肺转移和预后的诺姆图. 这些工具有助于个性化临床决策,以改善患者管理和治疗结果.

关键词:
宫癌:宫癌是一种癌症.监测流行病学和最终结果数据库 (SEER数据库)肺部转移是肺部转移的原因之一.这个名字是名ogramogram.

更多相关视频

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

Published on: September 27, 2024

380
An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells09:48

An Orthotopic Endometrial Cancer Model with Retroperitoneal Lymphadenopathy Made From In Vivo Propagated and Cultured VX2 Cells

Published on: September 12, 2019

8.2K

相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
456
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
539