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- W4308671143 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." @default.
- W4308671143 created "2022-11-14" @default.
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- W4308671143 date "2022-11-08" @default.
- W4308671143 modified "2023-09-26" @default.
- W4308671143 title "Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients" @default.
- W4308671143 doi "https://doi.org/10.48550/arxiv.2211.04180" @default.
- W4308671143 hasPublicationYear "2022" @default.
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