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The prediction of pancreatic ductal adenocarcinoma therapy response is aclinically challenging and important task in this high-mortality tumour entity.The training of neural networks able to tackle this challenge is impeded by alack of large datasets and the difficult anatomical localisation of thepancreas. Here, we propose a hybrid deep neural network pipeline to predicttumour response to initial chemotherapy which is based on the ResponseEvaluation Criteria in Solid Tumors (RECIST) score, a standardised method forcancer response evaluation by clinicians as well as tumour markers, andclinical evaluation of the patients. We leverage a combination ofrepresentation transfer from segmentation to classification, as well aslocalisation and representation learning. Our approach yields a remarkablydata-efficient method able to predict treatment response with a ROC-AUC of63.7% using only 477 datasets in total.

Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients