论文标题
利用细分标签和表示学习来预测PDAC患者的反应
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
论文作者
论文摘要
胰腺导管腺癌治疗反应的预测是该高病态肿瘤实体的临床挑战性和重要任务。缺乏大型数据集以及胰腺的难以解剖本地化阻碍了能够应对这一挑战的神经网络的培训。在这里，我们提出了一种杂交深神经网络管道，以预测肿瘤对初始化学疗法的反应，该疗法基于实体瘤的反应评估标准（RECIST）评分，这是一种标准化的临床医生和肿瘤标记的癌症反应评估方法，以及对患者的临床评估。我们利用了从分割到分类以及本地化和表示学习的表示形式转移的组合。我们的方法产生了一种非常具有数据效率的方法，能够使用总共477个数据集使用ROC-AUC预测治疗反应。
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.
