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

TL;DR

This paper presents a data-efficient deep learning pipeline that combines segmentation, representation transfer, and clinical data to predict therapy response in pancreatic cancer patients, addressing dataset scarcity and anatomical challenges.

Contribution

It introduces a hybrid neural network approach that leverages segmentation labels and representation learning for improved therapy response prediction in PDAC.

Findings

Achieved ROC-AUC of 63.7% with only 477 datasets

Utilized a hybrid deep neural network combining segmentation and classification

Demonstrated effective prediction despite limited data

Abstract

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.