在肺癌查研究中,用于生存分析的混合CNN-RNN方法
Yaozhi Lu1,2, Shahab Aslani1,3, An Zhao1,2
1Centre for Medical Image Computing, University College London, UK.
Heliyon
|August 21, 2023
概括
这项研究引入了一种混合深度学习模型,将CNN和RNN结合起来,用于预测肺癌查中的心血管和呼吸道死亡率. 该模型的性能优于人类的预测,有助于早期识别高风险个体.
科学领域:
- 人工智能的人工智能
- 医学成像分析 医学成像分析
- 生存分析的分析.
背景情况:
- 肺癌查研究为预测其他死亡原因提供了机会.
- 心血管和呼吸系统疾病是一般人口死亡的重要原因.
- 准确预测死亡风险可以指导临床干预并改善患者的治疗结果.
研究的目的:
- 开发和验证混合深度学习模型,用于预测长期心血管和呼吸道死亡率.
- 评估模型的性能与人类预测和传统的生存分析方法相比.
- 探索成像特征和时间序列数据在死亡率预测中的实用性.
主要方法:
- 开发了一个混合卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型.
- 美国有线电视新闻网 (CNN) 从CT扫描中捕获了成像特征;RNN分析了时间序列数据以获得全球信息.
- 长期短期记忆 (LSTM) 变体被用于处理不规则的患者随访时间.
- 在心血管/呼吸道死亡的受试者和匹配的对照队列上训练模型.
- 考克斯比例危险和考克斯神经网络模型用于生存分析.
主要成果:
- 混合CNN-RNN模型实现了心血管死亡率预测曲线下的面积 (AUC) 为0.76,超过了人类的预测.
- 该模型获得了0.63的F1得分和0.42的马修斯相关系数.
- 纳入随访史改善了生存预测,Cox神经网络实现了IPCW C指数的0.75 (内部) 和0.69 (外部).
- 模型的通用性在外部队列上得到了验证.
结论:
- 混合深度学习模型可以有效地预测肺癌查参与者的心肺呼吸道死亡率.
- 该方法集成成像和时间数据,以提高预测准确度.
- 早期识别高风险个体可以促使进一步调查,可能降低发病率和死亡率.
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