相关实验视频

Updated: Sep 19, 2025

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

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

对于患有间歇性肺病和肺癌患者的一年死亡风险预测模型

Xiaorui Ding¹, Wanqing Zhou¹, Guanning Zhong¹

¹Department of Pulmonary and Critical Care Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Translational lung cancer research

|June 19, 2025

概括

这项研究开发了一种1年死亡风险预测模型,用于患有间歇性肺病 (ILD) 和肺癌 (LC) 的患者. 该模型有助于识别高风险个体,以精确管理这些复杂的案件.

科学领域:

肺部病理学肺部病理学
在瘤学瘤学.
医疗信息学医疗信息学

背景情况:

间歇性肺病 (ILD) 和肺癌 (LC) 的同时发生是一个日益增长的临床和研究兴趣的领域.
了解ILD和LC患者的临床特征和预后因素对于有效的患者管理至关重要.

研究的目的:

描述诊断出ILD和LC的患者的临床特征.
为了确定与这种患者队列中死亡率相关的预后因素.
开发和验证一种预测模型,用于同时患有ILD和LC的患者1年死亡风险的预测模型.

主要方法:

从2017年到2022年进行了一项回顾性研究,涉及206名ILD和LC患者.
收集的数据包括人口统计,LC组织学和阶段,ILD高分辨率计算机断层扫描 (HRCT) 模式,实验室结果和治疗结果.
使用逻辑回归开发了一种基于nomogram的1年全因死亡率预测模型,并使用AUC,校准和决策曲线进行验证.

主要成果:

腺癌 (45.6%) 是最常见的LC亚型;43.7%的瘤位于ILD病变内.
常见间歇性肺炎 (UIP) 是最常见的ILD HRCT模式 (49.5%).
一年内全因死亡率为41.3%. 预测模型,结合年龄,性别,中性粒细胞计数,LDH和白蛋白,实现AUC为0.775 (训练) 和0.716 (验证).

结论:

经过验证的纳米图,包括临床和实验室变量,可以预测同时患有ILD和LC的患者的1年死亡率.
该模型有助于识别高风险患者,以定制管理策略.
建议在多中心研究中进一步进行外部验证.

关键词:

间歇性肺部疾病 (ILD) 是一种肺部疾病.肺癌 (LC) 是一种肺癌.死亡率死亡率预测模型预测模型预后因素预后因素

更多相关视频

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

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

相关概念视频

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,...

Statistical Methods for Analyzing Epidemiological Data

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:

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...