Evaluation of novel radiomics and machine learning-derived CT-biomarkers in patient stratification and therapy response assessment in pancreatic ductal adenocarcinoma

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Pancreatic ductal adenocarcinoma (PDAC) ranges among the deadliest cancers, reflected by a devastating 5-year survival rate of only 9%. Moreover, the incidence of PDAC is rising and it is projected to take over second place among cancer-related deaths in the United States by the year 2030. A particular hallmark of PDAC is the high degree of genomic, transcriptomic and metabolic heterogeneity.The grim prognosis and the characteristic intra- and intertumoral heterogeneity underline the necessity for a more precise patient stratification and therapy response assessment to improve survival in PDAC. However, routine cross-sectional imaging methods are of limited value with regard to therapy response assessment and patient stratification in PDAC. Artificial intelligence based algorithms could help to overcome this issue and pave the path towards individualized precision oncology. However, previous studies on this field in PDAC most importantly lack of sufficient cohort sizes, standardization and availability of histopathologic features. Herein, we aim to develop a comprehensive stratification model for patients with primary resectable PDAC, combining state of the art radiomics and high-end deep learning procedures in the so far published largest multi-institutional and multi-national patient cohort. Moreover, we aim to unravel the potential of novel comprehensive imaging-derived biomarkers, including radiogenomic feature maps, for prediction of response assessment in patients with advanced PDAC included in the prospective COMPASS trial. This trial includes > 330 patients with advanced PDAC. All patients received extensive histopathological and genomic work-up (e.g. whole genomic sequencing and RNA sequencing). We will match state-of-the art machine learning and radiomics approaches with mutational and transcriptional features in order to develop novel imaging-derived biomarkers and radiogenomic feature maps for advanced PDAC. With our proposed research projects, we aim to develop advanced non-invasive imaging-derived biomarkers for a better patient stratification and therapy response assessment in patients with primary resectable and advanced PDAC, respectively.