Table_1_Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.docx

ObjectiveNeoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT).
Materials and methodsSeveral deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes.
ResultsAmongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network.
ConclusionDeep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

### 研究目标
新辅助化疗（Neoadjuvant chemotherapy, NAC）是局部晚期乳腺癌（Locally Advanced Breast Cancer, LABC）的核心治疗手段之一。在治疗启动前预测局部晚期乳腺癌患者对新辅助化疗的应答情况，有助于优化治疗方案，确保患者接受切实有效的治疗。本研究旨在利用深度学习网络与计算机断层扫描（computed tomography, CT）影像，开发一款用于预测局部晚期乳腺癌患者新辅助化疗肿瘤应答的预测模型。

### 材料与方法
本研究探讨了多种深度学习方法，包括视觉Transformer（Vision Transformer, ViT）以及VGG16、VGG19、ResNet-50、ResNet-101、ResNet-152、InceptionV3与Xception等迁移学习网络（transfer learning networks）。将上述深度学习网络应用于CT影像，以评估患者对新辅助化疗的应答情况。模型性能通过平衡准确率（balanced_accuracy）、准确率、灵敏度与特异度等分类指标进行评估。其中，视觉Transformer（ViT）借助注意力机制为影像中的关键区域赋予更高权重，从而实现更优的类别区分效果。

### 研究结果
本研究共纳入117例局部晚期乳腺癌患者，其中82例（70%）表现为临床病理应答，35例（30%）对新辅助化疗无应答。根据训练集与测试集的划分比例不同，视觉Transformer（ViT）取得了最优性能：准确率范围为71±3%至77±4%，特异度范围为86±6%至76±3%，灵敏度范围为56±4%至52±4%，平衡准确率范围为69±3%至69±3%。Xception网络取得次之最佳结果：准确率范围为72±4%至65±4%，特异度范围为81±6%至73±3%，灵敏度范围为55±4%至52±5%，平衡准确率范围为66±5%至60±4%。性能最差的为VGG-16迁移学习网络。

### 结论
深度学习网络结合CT影像，能够在治疗启动前预测局部晚期乳腺癌患者对新辅助化疗的肿瘤应答情况。其中视觉Transformer（ViT）可取得最佳性能，验证了注意力机制的重要价值。