Title
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.
Year
DOI
Venue
2020
10.1117/12.2566425
MEDICAL IMAGING 2020: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING
Keywords
DocType
Volume
radiomics, cyst fluid, quantitative image analysis, intraductal papillary mucinous neoplasms, pancreas
Conference
11315
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
20