从基于卷积神经网络预测分子亚型的乳腺动态对比增强MRI有效估计药理动力学参数
Liangliang Zhang1,2, Ming Fan3, Lihua Li1,3
1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China.
Physics in medicine and biology
|November 20, 2023
概括
一个新的卷积神经网络 (CNN) 准确有效地估计了乳腺MRI的药理动力学 (PK) 参数,改善了分子亚型的预测,并显著加速了计算.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 标志物动力学模型估计乳腺动态对比度增强MRI的药理动力学 (PK) 参数,与病理特征相关.
- 现有的模型往往是不准确的, PK 参数估计需要大量的时间.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 以准确有效地估计乳腺MRI中的PK参数.
- 用CNN衍生的PK参数来预测分子亚型.
主要方法:
- 集成CNN (GL-CNN) 的全球-本地特征使用已知的PK参数作为基本真相的合成数据进行训练.
- 在GL-CNN直接估计PK参数 (Ktrans,Kep) 地图.
- 来自PK地图的放射性特征与随机森林分类器一起用于预测发现和验证队列中的分子亚型.
主要成果:
- 与基于Tofts的Ktrans估计模型相比,GL-CNN的准确性明显更高 (PSNR,SSIM,CCC).
- 基于GL-CNN的Ktrans实现了对光线B和HER2瘤的卓越诊断性能 (AUC 0.7658,0.8528).
- 与Tofts方法相比,GL-CNN方法的计算速度大约是Tofts方法的79倍.
结论:
- 该GL-CNN方法提供了准确和有效的估计从乳腺MRI的PK参数.
- 这种方法提高了分子亚型的预测,为传统方法提供了更快,更精确的替代方案.
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