机器学习算法预测切除术后细胞癌患者的存活率:一项回顾性研究
Peipei Wang1, Zhao Hou2, Dingyang Lv3
1Department of Urology, The First Hospital of Shanxi Medical University, Taiyuan, China; School of Public Health, Shanxi Medical University, Taiyuan, China.
Chinese clinical oncology
|June 27, 2025
概括
机器学习模型可以预测细胞癌 (RCC) 患者在切术后的生存率. 拉索-科克斯模型在识别高风险患者进行早期干预方面表现出卓越的性能.
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:
- 细胞癌 (RCC) 患者在切除术后面临着不良预后和重大负担.
- 识别高风险患者对于及时管理和改善结果至关重要.
研究的目的:
- 应用机器学习用于特征选择,预测RCC患者的生存率.
- 开发和验证用于识别和管理高风险RCC患者的预后模型.
主要方法:
- 在切术后对725名RCC患者进行了回顾性分析.
- 使用最小绝对收缩和选择操作员 (LASSO) 回归和随机生存森林 (RSF) 的特征选择.
- 使用考克斯回归的LASSO-Cox和RSF-Cox模型的构建和比较,通过C指数,AUC,校准和决策曲线分析 (DCA) 评估.
主要成果:
- 与RSF-Cox和一个完整的Cox模型相比,LASSO-Cox模型实现了更高的预测准确性 (训练中C指数为0.893,验证时为0.856).
- 确定的主要预测因素包括瘤大小,手术前的血纤维素,N阶段和Fuhrman等级.
- 拉索-科克斯模型在DCA中展示了优异的校准和净益,有效地区分了低风险和高风险组.
结论:
- 开发的LASSO-Cox模型是一种简化和高效的工具,用于预测RCC患者的生存率.
- 这个模型可以帮助早期干预和临床决策,以实现个性化的患者管理.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.4K
07:13Comparison 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
201
相关概念视频
01:21Cancer Survival Analysis
458
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...
458
01:24Kaplan-Meier Approach
277
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,...
277
01:20Comparing the Survival Analysis of Two or More Groups
299
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...
299