School of Electrical & Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.
Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
Acad Radiol. 2023 May;30(5):966-970. doi: 10.1016/j.acra.2022.10.005. Epub 2022 Nov 2.
Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and localization of prostate cancer (PCa). Thanks to the great success of deep learning models in computer vision, the potential application for early detection of PCa using mpMRI is imminent.
Deep learning analysis of the PROSTATEx dataset.
In this study, we show a simple convolutional neural network (CNN) with mpMRI can achieve high performance for detection of clinically significant PCa (csPCa), depending on the pulse sequences used. The mpMRI model with T2-ADC-DWI achieved 0.90 AUC score in the held-out test set, not significantly better than the model using K instead of DWI (AUC 0.89). Interestingly, the model incorporating T2-ADC- K better estimates grade. We also describe a saliency "heat" map. Our results show that csPCa detection models with mpMRI may be leveraged to guide clinical management strategies.
Convolutional neural networks incorporating multiple pulse sequences show high performance for detection of clinically-significant prostate cancer, and the model including dynamic contrast-enhanced information correlates best with grade.
多参数磁共振成像（mpMRI）越来越多地用于前列腺癌（PCa）的风险分层和定位。由于深度学习模型在计算机视觉方面的巨大成功，使用 mpMRI 进行早期 PCa 检测的潜在应用迫在眉睫。
PROSTATEx 数据集的深度学习分析。
在这项研究中，我们展示了一个简单的卷积神经网络（CNN），可以根据使用的脉冲序列，在检测临床上显著的前列腺癌（csPCa）方面取得很高的性能。使用 T2-ADC-DWI 的 mpMRI 模型在保留测试集中的 AUC 得分为 0.90，与使用 K 而不是 DWI 的模型（AUC 为 0.89）没有显著差异。有趣的是，包含 T2-ADC-K 的模型可以更好地估计分级。我们还描述了一个显着性“热”图。我们的结果表明，mpMRI 的 csPCa 检测模型可用于指导临床管理策略。
包含多个脉冲序列的卷积神经网络在检测临床上显著的前列腺癌方面表现出很高的性能，并且包括动态对比增强信息的模型与分级相关性最佳。
