相关实验视频

Updated: Jun 29, 2025

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

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

Published on: September 27, 2024

基于SEER数据库,开发和验证一个名谱来预测重新定义的厌塑性甲状腺癌患者的整体存活率

Chuyue Zhang¹, Bin Li², Yan Yang³

¹Department of General Surgery, the 920th Hospital of Joint Logistics Support Force, PLA, Kunming, Yunnan, People's Republic of China. zhangchuyue0215@163.com.

International journal of clinical oncology

|April 7, 2024

概括

甲状腺原发性平状细胞癌 (PSCCTh) 现在被归类为厌塑性甲状腺癌 (ATC). 这项研究开发了一个名谱来预测重新定义的形甲状腺癌 (rATC) 的存活率,并发现了ATC和PSCCTh的独特预后因素.

科学领域:

内分泌学在内分泌学.
在瘤学瘤学.
病理学病理学病理学

背景情况:

2022年世卫组织的分类将甲状腺原发性状细胞癌 (PSCCTh) 重归类为形甲状腺癌 (ATC).
了解这些实体的独特特征和预后因素对于准确的诊断和治疗至关重要.

研究的目的:

根据它们的临床特征来区分ATC和PSCCTh.
开发和验证用于预测重新定义的形性甲状腺癌 (rATC) 的整体存活率 (OS) 的名图.

主要方法:

利用了ATC和PSCCTh患者的SEER数据库 (2000-2018) 的数据.
采用了卡普兰-梅尔分析,日志级别测试和Cox相称危险回归用于生存和预后因素分析.
构建并验证的诺姆图来预测rATC的3个月,6个月和12个月的OS.

主要成果:

包括1338名ATC和127名PSCCTh患者;PSCCTh显示了比ATC更好的OS和癌症特异性存活率 (CSS).
确定了CSS的独特的独立预后因素:ATC (年龄,瘤大小,转移,手术,放射治疗,化疗) 与PSCCTh (年龄,手术).
开发了一种具有良好的分辨率 (C指数0.740训练,0.778验证) 和校准用于预测rATC存活率的nomogram.

结论:

一个经过验证的诺米克图提供了一个可靠的工具,用于预测RATC患者的OS.
在ATC和PSCCTh之间,预后因素显著不同,需要量身定制的临床管理策略.
这些发现支持ATC和PSCCTh的不同的临床治疗和管理计划.

关键词:

无塑性是一种无塑性.这个名字叫做Nomogram.甲状腺原发性状细胞癌预测预后预测预测风险风险风险风险风险在 SEER 计划中,甲状腺癌是一种甲状腺癌.

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Spontaneous Murine Model of Anaplastic Thyroid Cancer

Published on: February 3, 2023

相关概念视频

Cancer Survival Analysis

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...

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...