当前的生存预测工具在治疗骨转移后的骨相关事件时是否有用?
Yu-Ting Pan1,2, Yen-Po Lin1,3, Hung-Kuan Yen1,3,4
1Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Clinical orthopaedics and related research
|March 22, 2024
概括
骨瘤学研究小组的机器学习算法 (SORG-MLAs) 在预测随后发生骨转移的患者的存活率方面表现有前途. 虽然这些算法通常是可靠的,但它们高估了脊髓转移的1年生存率,需要仔细的临床考虑.
科学领域:
- 在瘤学瘤学.
- 整形外科 整形外科 整形外科
- 机器学习在医学中的应用
背景情况:
- 骨转移在晚期癌症中存在重大挑战,影响患者的生活质量和生存率.
- 准确的预后模型对于指导治疗决策至关重要,特别是对于经历随后骨相关事件 (SREs) 的患者.
- 现有的预后模型的可靠性,如瘤学研究小组机器学习算法 (SORG-MLAs),对于随后的SREs是不确定的.
研究的目的:
- 评估SORG-MLAs的准确性和可靠性,以预测后续SRE患者的存活率.
- 评估SORG-MLAs的性能,特别是在为随后的脊柱或四肢SREs进行手术或放射治疗的患者.
主要方法:
- 在2010年至2019年期间治疗的584名初始和随后SRE患者的回顾性分析.
- 患者被分为脊柱和四肢SRE亚组.
- 使用SORG-MLA来预测随后的SRE时的存活率,使用AUC,Brier分数和决策曲线分析来评估性能.
主要成果:
- 在脊椎和四肢组中,SORG-MLAs表现出可接受的歧视 (AUC 0.650.73) 和良好的整体性能 (比零模型低的布赖尔得分).
- 这些算法为两个队列的决策曲线分析提供了净收益.
- 在脊椎组中观察到对1年生存概率的显著高估 (中位数log{O:E}为-0.60).
结论:
- SORG-MLAs是后续SRE患者的可行的预测工具,提供令人满意的歧视和净收益.
- 临床医生应谨慎地解释脊柱SREs的1年生存预测,因为过高估计.
- 需要改进的预后算法和创新的工具,以更好地管理随着寿命的增加后续SRE患者.
相关概念视频
01:21Cancer Survival Analysis
345
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...
345
01:24Kaplan-Meier Approach
136
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,...
136
01:20Comparing the Survival Analysis of Two or More Groups
181
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...
181