对放射学和深度学习算法的比较分析,用于预测肝细胞癌的生存率
Felix Schön1, Aaron Kieslich2, Heiner Nebelung3
1Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany. felix.schoen@uniklinikum-dresden.de.
Scientific reports
|January 5, 2024
概括
深度学习 (CNN) 模型比传统的放射学更好地预测肝癌 (HCC) 患者的整体存活率. CNNs提供了卓越的预后见解,特别是在有限的临床数据下.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 肝细胞癌研究 肝细胞癌研究
背景情况:
- 肝细胞癌 (HCC) 的生存预测依赖于各种因素.
- 放射学和深度学习为预后评估提供了新的方法.
- 对比这些AI方法的稳定性对于临床应用至关重要.
研究的目的:
- 为了比较基于计算机断层扫描 (CT) 的放射学和深度学习卷积神经网络 (CNN) 在HCC患者的整体存活率 (OS) 的预测性能.
- 在临床条件下评估这些模型的稳定性.
主要方法:
- 追溯分析114名HCC患者的治疗前CT扫描.
- 使用放射学特征和CNN结合临床参数的Cox回归模型的开发和验证.
- 使用一致性指数 (C指数) 和风险分层的日志等级测试进行绩效评估.
主要成果:
- 临床的Cox回归模型显示了最佳的OS预测性能 (C指数为0.74).
- 在图像分析方面,CNN模型 (最高C指数为0.71) 的表现优于放射学模型 (最高C指数为0.66).
- 仅使用基于成像的模型无法实现显著的风险分层.
结论:
- 深度学习CNN算法显示,与传统放射学相比,在HCC患者中预测OS的预后潜力更高.
- CNNs可以提供有价值的预后信息,特别是当临床数据有限时.
- 进一步的研究可能会将CNN纳入HCC管理的临床决策.
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