Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
Sci Rep. 2022 Feb 10;12(1):2244. doi: 10.1038/s41598-022-06100-2.
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for NAC followed by surgery. The ground truth response to NAC was identified for each patient after the surgery using the standard clinical and pathological criteria. Two deep convolutional neural network (DCNN) architectures including the residual network and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. In different experiments, the features maps were derived from the tumor core only, as well as the core and its margin. Evaluation results on an independent test set demonstrate that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had the best performance in response prediction with an accuracy of 88% and an area under the receiver operating characteristic curve of 0.86. Ten-year survival analyses indicate statistically significant differences between the survival of the responders and non-responders identified based on the model prediction at pre-treatment and the standard criteria at post-treatment. The results of this study demonstrate the promising capability of DCNNs with attention mechanisms in predicting breast cancer response to NAC prior to the start of treatment using QUS multi-parametric images.
在这项研究中，调查了一种新的基于深度学习的方法，使用术前定量超声（QUS）多参数成像来预测新辅助化疗（NAC）对乳腺癌的反应。使用从 181 名被诊断为局部晚期乳腺癌并计划接受 NAC 加手术的患者获得的数据生成了乳腺肿瘤的 QUS 多参数图像。术后使用标准的临床和病理标准确定了每位患者对 NAC 的反应。探索了两种深度卷积神经网络（DCNN）架构，包括残差网络和残差注意网络（RAN），从参数图像中提取最佳特征图，使用全连接网络进行反应预测。在不同的实验中，特征图仅来自肿瘤核心，以及核心及其边缘。在独立测试集上的评估结果表明，使用 RAN 架构从肿瘤核心和边缘的扩展参数图像中提取特征图的开发模型在反应预测方面表现最佳，准确率为 88%，接收器操作特征曲线下面积为 0.86。十年生存分析表明，基于模型预测和术后标准标准在治疗前识别的反应者和非反应者的生存之间存在统计学显著差异。这项研究的结果表明，具有注意力机制的 DCNN 在使用 QUS 多参数图像预测治疗前乳腺癌对 NAC 的反应方面具有有前途的能力。
