The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.

翻译：摘要: 在高致死率的肿瘤实体中，预测胰管腺癌(PDAC)治疗反应是一项临床上具有挑战性和重要性的任务。神经网络能够处理此挑战的训练受到大数据集缺乏和胰腺解剖定位困难的阻碍。在这里，我们提出了一种基于实体肿瘤反应评估标准(RECIST)评分、肿瘤标志物和临床评估的混合深度神经网络管道，用于预测初始化疗的肿瘤反应。我们利用分割到分类的表示转移、本地化和表示学习的组合。我们的方法仅使用477个数据集，就能够以63.7%的ROC-AUC预测治疗反应，具有显着的数据效率。