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：胰腺导管腺癌治疗反应的预测是该高死亡率肿瘤实体中一项临床挑战性且重要的任务。能够应对这一挑战的神经网络训练受到缺乏大规模数据集以及胰腺解剖定位困难的阻碍。本文提出一种混合深度神经网络流水线，用于预测患者对初始化疗的肿瘤反应，该预测基于实体瘤疗效评价标准（RECIST）评分（临床医生用于癌症反应评估的标准化方法）、肿瘤标志物以及患者的临床评价。我们综合利用了从分割到分类的表征迁移，以及定位与表征学习。该方法具有显著的数据高效性，仅使用总计477个数据集即可实现ROC-AUC为63.7%的治疗反应预测。