深度学习模型在基于临床数据的预后预测中优于传统模型,用于成人类型的扩散性质瘤
Xiaopeng Li1,2, Chongshun Zhao2, Yanpeng Xia1
1Department of Neurosurgery, Handan First Hospital, Handan, 056002, Hebei, People's Republic of China.
Discover oncology
|November 26, 2025
概括
深度学习模型,特别是DeepSurv,与传统方法相比,显示成人类型扩散质瘤 (ADG) 的生存预测优越. 这种先进的方法为复杂的患者数据提供了增强的稳定性和性能.
科学领域:
- 神经瘤学神经瘤学
- 计算生物学是一种计算生物学.
- 生物统计学 生物统计学
背景情况:
- 成人类型的扩散性质瘤 (ADG) 具有显著的异质性和数据挑战.
- 准确的生存预测对于指导ADG患者的治疗决策至关重要.
- 传统的统计模型可能无法完全捕捉ADG中的复杂预后因素.
研究的目的:
- 系统地比较传统的统计方法与机器学习方法用于ADG的生存预测.
- 评估不同预测模型的性能,可解释性和临床适用性.
- 确定最有效的模型来预测异质ADG患者队列中的存活率.
主要方法:
- 利用了两个公共和一个私人追溯ADG数据集.
- 开发并验证了四种生存预测模型:Cox比例危险,随机生存森林,神经多任务后勤回归和DeepSurv.
- 对缺失的估值归算策略进行了敏感性分析.
主要成果:
- 深度学习模型,特别是DeepSurv,在内部和外部验证队伍中表现优于传统的Cox模型和随机生存森林.
- 确定了关键的预后因素,包括年龄,分子病理学,化疗,切除程度,疾病程度和放射治疗.
- 通过灵敏度分析证实了模型稳定性;DeepSurv模型是公开的.
结论:
- 与传统方法相比,DeepSurv模型在异质的,部分缺失的ADG数据中展示了优越的生存预测性能和稳定性.
- 深度学习为提高神经瘤学的预后准确性提供了一个有希望的途径.
- 开发的DeepSurv模型为ADG患者管理中的临床应用提供了一个强大的工具.
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