Next-generation image analytics for chemotherapy and survival prediction in pancreatic ductal adenocarcinoma

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The objective of the project is to establish a robust, quality-controlled image processing pipeline and to generate, validate and test predictive models of chemotherapy response and survival in patients with PDAC from standardized routine clinical computed tomography imaging.Research AimsFollowing the objectives, we aim specifically: (1) to establish an image pre-processing and segmentation pipeline including a seed-based region growing algorithm capable of automatically segmenting pancreatic cancer lesions which can be deployed to client-side devices for usage by radiologists with limited experience in pancreatic cancer;(2) to generate a radiomics-based model predictive of short- and long-term response to current intensified chemotherapy regimens based on baseline and early follow-up imaging data; (3) to create a multivariate disease-free/ overall survival model including the derived radiomic signatures alongside clinical data from the NEOLAP trial to assess the incremental value of a radiomics approach over traditional risk assessment tools.Expected ImpactWe expect our research:(1) to improve our understanding of PDAC biology by identifying distinct radiomic features of treatment-relevant tumor subtypes;(2) to identify radiomic signatures predictive of specific therapy response and individual survival in PDAC;(3) to provide non-expert radiologist and clinical partners with tools for decision support regarding patient stratification (i.e. personalized medicine) and therapy monitoring;(4) to facilitate research in the field of pancreatic cancer and precision oncology, providing advanced image-analysis algorithms for the prediction of patient outcome.