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Ziller, A.* ; Erdur, A.C.* ; Jungmann, F.* ; Rueckert, D.* ; Braren, R.* ; Kaissis, G.

Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients.

In: (Proceedings - International Symposium on Biomedical Imaging). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2023. 5 (Proceedings - International Symposium on Biomedical Imaging ; 2023-April)
DOI
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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Publication type Article: Conference contribution
Corresponding Author
Keywords Pdac ; Personalised Treatment ; Representation Learning ; Transfer Learning
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title Proceedings - International Symposium on Biomedical Imaging
Quellenangaben Volume: 2023-April, Issue: , Pages: 5 Article Number: , Supplement: ,
Publisher Ieee
Publishing Place 345 E 47th St, New York, Ny 10017 Usa
Non-patent literature Publications
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)
Grants German Cancer Consortium Joint Funding UPGRADE Programme: Subtyping of Pancreatic Cancer based on radiographic and pathological Features
German Research Foundation
European Research Council