Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Physics and Biology in Medicine IDP, University of California, Los Angeles, CA, USA.
Med Phys. 2021 Jun;48(6):3262-3372. doi: 10.1002/mp.14897. Epub 2021 May 14.
The goal of this study was to predict soft tissue sarcoma response to radiotherapy (RT) using longitudinal diffusion-weighted MRI (DWI). A novel deep-learning prediction framework along with generative adversarial network (GAN)-based data augmentation was investigated for the response prediction.
Thirty soft tissue sarcoma patients who were treated with five-fraction hypofractionated radiation therapy (RT, 6Gy×5) underwent diffusion-weighted MRI three times throughout the RT course using an MR-guided radiotherapy system. Pathologic treatment effect (TE) scores, ranging from 0-100%, were obtained from the post-RT surgical specimen as a surrogate of patient treatment response. Patients were divided into three classes based on the TE score (TE ≤ 20%, 20% < TE < 90%, TE ≥ 90%). Apparent diffusion coefficient (ADC) maps of the tumor from the three time points were combined as 3-channel images. An auxiliary classifier generative adversarial network (ACGAN) was trained on 20 patients to augment the data size. A total of 15,000 synthetic images were generated for each class. A prediction model based on a previously described VGG-19 network was trained using the synthesized data, validated on five unseen validation patients, and tested on the remaining five test patients. The entire process was repeated seven times, each time shuffling the training, validation, and testing datasets such that each patient was tested at least once during the independent test stage. Prediction performance for slice-based prediction and patient-based prediction was evaluated.
The average training and validation accuracies were 86.5% ± 1.6% and 84.8% ± 1.8%, respectively, indicating that the generated samples were good representations of the original patient data. Among the seven rounds of testing, slice by slice prediction accuracy ranged from 81.6% to 86.8%. The overall accuracy of the independent test sets was 83.3%. For patient-based prediction, 80% was achieved in one round and 100% was achieved in the remaining six rounds. The mean accuracy was 97.1%.
This study demonstrated the potential to use deep learning to predict the pathologic treatment effect from longitudinal DWI. Accuracies of 83.3% and 97.1% were achieved on independent test sets for slice-based and patient-based prediction respectively.
本研究旨在利用纵向扩散加权 MRI（DWI）预测软组织肉瘤对放疗（RT）的反应。研究采用一种新的深度学习预测框架和基于生成对抗网络（GAN）的数据增强方法来进行反应预测。
30 例软组织肉瘤患者接受五分割hypofractionated 放疗（RT，6Gy×5），在 MR 引导放疗系统中在 RT 过程中进行三次扩散加权 MRI。从术后 RT 手术标本中获得病理治疗效果（TE）评分，范围为 0-100%，作为患者治疗反应的替代指标。根据 TE 评分将患者分为三组（TE≤20%、20%<TE<90%、TE≥90%）。将三个时间点的肿瘤表观扩散系数（ADC）图组合成 3 通道图像。在 20 例患者上训练辅助分类器生成对抗网络（ACGAN）以增加数据量。每个类别生成 15000 张合成图像。使用合成数据训练基于先前描述的 VGG-19 网络的预测模型，在五个未见的验证患者上进行验证，并在其余五个测试患者上进行测试。整个过程重复七次，每次打乱训练、验证和测试数据集，以使每个患者在独立测试阶段至少测试一次。评估了基于切片的预测和基于患者的预测的预测性能。
平均训练和验证准确率分别为 86.5%±1.6%和 84.8%±1.8%，表明生成的样本是原始患者数据的良好表示。在七轮测试中，切片预测准确率从 81.6%到 86.8%不等。独立测试集的总体准确率为 83.3%。对于基于患者的预测，在一轮中达到 80%，在其余六轮中达到 100%。平均准确率为 97.1%。
本研究表明，使用深度学习从纵向 DWI 预测病理治疗效果具有潜力。在独立测试集上，基于切片和基于患者的预测的准确率分别达到 83.3%和 97.1%。
