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    A Deep Learning Model for Pancreatic Ductal Adenocarcinoma Chemotherapy Outcome Prediction

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    HeSpr22.pdf (549.2Kb)
    Date
    2022-04
    Author
    He, Nichol
    Kamrowski, Connor
    Varatharajan, Thulasi
    Syzmoniak, Amy
    Lathiya, Maulik K.
    Gomes, Rahul
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    Abstract
    Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive abdominal malignancy, with an overall 8.5% 5-year survival rate. PDAC is often detected too late for surgical resection and associated with resistance to chemotherapy and radiation. Morphological characteristics of PDAC tumors can be extracted from CT scans and are associated with tumor characteristics and behavior. In this research, a deep-learning system for predicting chemotherapy outcome based on CT scans is being explored. To establish the foundation for this system, a comparative analysis between 2D-UNet and 3D-UNet has been performed. Experiments reveal 2D-UNet model to have higher accuracy in pancreas segmentation. To increase prediction accuracy, the effects of novel data augmentation techniques, including window level and cropping, have been utilized. By establishing techniques for standardizing CT scans from different datasets and using a flexible segmentation model, we aim to create a pipeline for pancreas segmentation followed by tumor extraction and staging using texture analysis.
    Subject
    Pancreatic cancer
    Machine learning
    Chemotherapy
    Posters
    Department of Computer Science
    Permanent Link
    http://digital.library.wisc.edu/1793/84483
    Type
    Presentation
    Description
    Color poster with text, images, and charts.
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