Exploiting segmentation labels and representation learning to forecast
therapy response of PDAC patients
- URL: http://arxiv.org/abs/2211.04180v2
- Date: Thu, 30 Mar 2023 08:07:01 GMT
- Title: Exploiting segmentation labels and representation learning to forecast
therapy response of PDAC patients
- Authors: Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel
Rueckert, Rickmer Braren, Georgios Kaissis
- Abstract summary: We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy.
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.
- Score: 60.78505216352878
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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