A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions

Abstract

This paper contributes to the burgeoning field of surgical data science. Specifically, multi-modal 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 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. We extracted radiomic features from CT scans and combined this with cyst-fluid markers. The cyst fluid model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Radiomic analysis of routinely acquired CT scans combined with cyst fluid inflammatory markers provides accurate prediction of risk of pancreatic cancer progression.