深度学习签名用于预测接受肺癌手术的患者的术后焦虑
Qingqing Ji1, Guohua Zhou2, Xiangxiang Sun3
1Shanghai University of Engineering Science, Shanghai, China.
Frontiers in surgery
|April 8, 2025
概括
使用MRI扫描的新型深度学习模型可以预测肺癌患者的术后焦虑. 这种工具有助于识别较高风险的个体,使及时干预成为更好的患者结果.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 精神病学是一个精神病学.
- 人工智能的人工智能
背景情况:
- 术后焦虑是肺癌手术患者的一个重要问题.
- 准确预测焦虑可以促进及时干预并改善患者护理.
- 目前用于预测焦虑的方法可能无法完全捕捉涉及因素的复杂相互作用.
研究的目的:
- 使用磁共振成像 (MRI) 建立和验证深度学习特征,用于预测肺癌患者手术后焦虑.
- 确定与手术后焦虑相关的临床预测因素.
主要方法:
- 使用202名肺癌手术患者的手术前MRI-T1WI图像开发了一个深度学习签名.
- 使用ResNet-152算法来训练深度学习模型.
- 后勤回归分析了临床变量,而接收器运行特征 (ROC) 分析评估了预测性表现.
主要成果:
- 较大的瘤大小 (OR,2.044) 和淋巴结转移 (OR,2.078) 被确定为术后焦虑的独立预测因素.
- 深度学习特征表现出强大的预测性能,曲线下的面积 (AUC) 为0.865和0.822.
- 较高的深度学习分数与经历手术后焦虑的可能性增加有关.
结论:
- 开发的深度学习特征显示出在识别高风险患上术后焦虑症的肺癌患者方面具有重大潜力.
- 这种人工智能驱动的方法可以帮助临床医生积极管理患者的焦虑.
- 将基于MRI的深度学习集成到临床实践中可能会增强术前风险分层和术后护理.
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