相关实验视频

Updated: May 28, 2025

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

204

非致命自我伤害与癌症诊断后的生存之间的关联:基于人口的比较队列研究

Antoine Eskander1,2,3,4,5, Rinku Sutradhar2,3,4, Elie Isenberg-Grzeda6,7

  • 1Division of Surgical Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Journal of the National Comprehensive Cancer Network : JNCCN
|February 11, 2025
PubMed
概括

癌症患者的非致命自我伤害 (NFSI) 显著降低了整体存活率. 识别和治疗癌症患者的痛苦是减轻NFSI风险和改善结果的关键.

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Detecting Anastasis In Vivo by CaspaseTracker Biosensor20:16

Detecting Anastasis In Vivo by CaspaseTracker Biosensor

Published on: February 1, 2018

9.2K

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...
321
Psychoneuroimmunology: Diabetes and Cancer01:19

Psychoneuroimmunology: Diabetes and Cancer

Chronic stress has been linked to both the onset and progression of serious health conditions, including Type 2 diabetes and cancer. Type 2 diabetes, a widespread chronic illness, is closely associated with obesity and insulin resistance, both of which often worsen under stress. Studies indicate that men experiencing high levels of chronic stress face a 45% higher risk of developing diabetes compared to those with minimal stress. Stress triggers physiological responses that elevate blood...
22
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
84
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
127
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
4.7K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
166