OBJECTIVE:To develop an ensemble a deep transfer learning model of CT images for predicting pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). METHODS:The data were obtained from the public dataset ‘QIN-Breast’ from The Cancer Imaging Archive (TCIA). CT Images were gathered before and after the first cycle of NAC. CT images of 121 breast cancer patients were used to train and test the model. Among these patients, 58 achieved a pCR and 63 showed a non-pCR based pathology examination of surgical results after NAC. The dataset was split into training and testing subsets with a ratio of 7:3. In addition, the number of training samples in the dataset was increased from 656 to 1,968 by performing an image augmentation method. Two deep transfer learning models namely, DenseNet201 and ResNet152V2, and the ensemble model with a concatenation of two models, were trained and tested using CT images. RESULTS:The ensemble model obtained the highest accuracy of 100% on the testing dataset. Furthermore, we received the best performance of 100% in recall, Precision and f1-score value for the ensemble model. This supports the fact that the ensemble model results in better-generalized model and leads to efficient framework. Although a 0.004 and 0.003 difference were seen between the AUC of two base models (DenseNet201 and ResNet152V2) and the proposed ensemble, this increase in the model quality is critical in medical research. T-SNE revealed that in the proposed ensemble, no points were clustered into the wrong class. These results expose the strong performance of the proposed ensemble. CONCLUSION:The study concluded that the ensemble model can increase the ability to predict breast cancer response to first-cycle NAC than two DenseNet201 and ResNet152V2 models.

中文翻译：

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基于 CT 图像的集成深度迁移学习预测乳腺癌对新辅助化疗的反应

目的：开发一个集成的 CT 图像深度迁移学习模型，用于预测接受新辅助化疗 (NAC) 的乳腺癌患者的病理完全缓解 (pCR)。方法：数据来自癌症影像档案 (TCIA) 的公共数据集“QIN-Breast”。在 NAC 的第一个周期之前和之后收集 CT 图像。使用 121 名乳腺癌患者的 CT 图像对模型进行训练和测试。在这些患者中，58 人获得了 pCR，63 人在 NAC 后对手术结果进行了基于非 pCR 的病理学检查。数据集以 7:3 的比例分为训练和测试子集。此外，通过执行图像增强方法，数据集中的训练样本数量从 656 个增加到 1,968 个。两种深度迁移学习模型，即 DenseNet201 和 ResNet152V2，以及两个模型串联的集成模型，使用 CT 图像进行训练和测试。结果：集成模型在测试数据集上获得了 100% 的最高准确率。此外，我们在集成模型的召回率、精度和 f1 值方面获得了 100% 的最佳性能。这支持了集成模型产生更好的泛化模型并导致有效框架的事实。尽管两个基础模型（DenseNet201 和 ResNet152V2）的 AUC 与提议的集成之间存在 0.004 和 0.003 的差异，但模型质量的这种提高在医学研究中至关重要。T-SNE 显示，在提议的集成中，没有任何点被聚集到错误的类别中。这些结果揭示了所提出的集成的强大性能。结论：