深度学习模型与传统的考克斯回归模型在预测结肠癌存活率方面的比较:基于SEER数据库
Zihan Qu1, Yashan Wang1, Dingjie Guo1
1Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China.
Journal of gastroenterology and hepatology
|May 10, 2024
概括
包括LSTM在内的深度学习模型在预测结肠癌存活率方面表现出卓越的准确性,与传统的Cox回归相比. 这些先进的算法为患者提供更可靠的预后评估和个性化治疗建议.
科学领域:
- 在瘤学瘤学.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 结肠癌 (CC) 生存预测对于患者管理至关重要.
- 传统的Cox比例危险 (CPH) 模型在复杂的生存数据分析中存在局限性.
- 深度学习为提高预后准确性提供了一个有希望的替代方案.
研究的目的:
- 开发和评估深度学习模型,用于预测结肠癌患者的生存率.
- 将深度学习算法的性能与传统的考克斯回归进行比较.
- 确定最佳的深度学习模型来预测结肠癌生存率.
主要方法:
- 一项基于人口的队列研究利用了来自监测,流行病学和最终结果 (SEER) 数据库 (2010-2015) 的数据.
- 患者被随机分配到训练 (70%) 和测试 (30%) 组.
- 人工神经网络 (ANN),深度神经网络 (DNN) 和长短期记忆 (LSTM) 与使用Brier分数和AUC的Cox比例危险 (CPH) 进行了比较.
主要成果:
- 深度学习模型 (ANN,DNN,LSTM) 在预测结肠癌存活率方面明显优于CPH模型.
- LSTM实现了0.910的最高曲线下面积 (AUC),表明了卓越的预测性能.
- 与CPH (0.170) 相比,深度学习模型 (0.148-0.155) 的Brier分数较低.
结论:
- 深度学习模型,特别是LSTM,显示出改善结肠癌预后评估的巨大潜力.
- 这些模型提供更准确,更可靠的个人生存预测和治疗建议.
- 这些发现表明,在瘤学中,转向人工智能驱动的决策支持的范式转变.
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