相关实验视频

Updated: Jul 8, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

基于XGBoost算法的肝细胞癌和肝外转移患者的生存预测模型

Jihye Lim¹, Hyeon-Gi Jeon², Yeonjoo Seo¹

¹Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Journal of hepatocellular carcinoma

|December 18, 2023

概括

这项研究使用现实世界的数据开发了一种肝细胞癌 (HCC) 与肝外转移的生存预测模型. 该模型表现良好,有助于个性化治疗和HCC患者的预后.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

准确的生存估计对于肝细胞癌 (HCC) 和肝外转移的患者至关重要.
现实世界的数据为开发复杂疾病的预测模型提供了宝贵的见解.

研究的目的:

开发和验证HCC和肝外转移患者的生存预测模型.
利用现实世界的数据,在具有挑战性的患者队列中进行可靠的生存预测.

主要方法:

分析了993名曾经没有接受过治疗的HCC患者的肝外转移队列.
极端梯度提升 (XGBoost) 算法用于预测3,6个月和12个月的生存率.
患者被随机分配到训练 (70%) 和测试 (30%) 组.

主要成果:

该模型表现出高预测性能,AUC分别为0.778,0.794和0.784在3,6和12个月.
关键预测因素包括Child-Pugh类,瘤特征,转移部位,血清AFP和初级抗HCC治疗.
主要抗HCC治疗成为最重要的生存预测指标.

结论:

成功开发了一种有效的模型,用于预测肝外转移的HCC患者的存活率.
该模型表现出良好的区分能力,支持其在临床决策中的实用性.
这种工具可以帮助个性化治疗策略,并可能改善患者的预后.

关键词:

算法算法是一种算法.肝脏瘤是什么意思可能性概率概率概率.预后预后预后幸存率幸存率幸存率幸存率

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

A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

Published on: October 24, 2015

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...