Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms

Purpose: Our paper contributes to the burgeoning field of surgical data science. Specifically, multimodal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine previously defined individual models of radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. Approach: Retrospective analysis of prospectively acquired cyst fluid and CT scans was undertaken for this study. A predictive model combining clinical features with a cyst fluid inflammatory marker (CFIM) was applied to patient data. Quantitative imaging (QI) features describi...