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

• Published in: Journal of medical imaging (Bellingham, Wash.) Vol. 7; no. 3; p. 031507
• Main Authors: Harrington, Kate A, Williams, Travis L, Lawrence, Sharon A, Chakraborty, Jayasree, Al Efishat, Mohammad A, Attiyeh, Marc A, Askan, Gokce, Chou, Yuting, Pulvirenti, Alessandra, McIntyre, Caitlin A, Gonen, Mithat, Basturk, Olca, Balachandran, Vinod P, Kingham, T. Peter, D’Angelica, Michael I, Jarnagin, William R, Drebin, Jeffrey A, Do, Richard K, Allen, Peter J, Simpson, Amber L
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.05.2020
• Subjects: Special Section on Interventional and Surgical Data Science; radiomics; intraductal papillary mucinous neoplasms; cyst fluid; pancreas; quantitative image analysis
• ISSN: 2329-4302; 2329-4310
• DOI: 10.1117/1.JMI.7.3.031507

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 describing radiomic patterns predictive of risk were extracted from scans. The CFIM model and QI model were combined into a single predictive model. An additional model was created with tumor-associated neutrophils (TANs) assessed by a pathologist at the time of resection. Results: Thirty-three patients were analyzed (7 high risk and 26 low risk). The CFIM model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Combining the CFIM, QI, and TAN models further increased performance to an AUC of 0.98. Conclusions: Quantitative analysis of routinely acquired CT scans combined with CFIMs provides accurate prediction of risk of pancreatic cancer progression. Although a larger cohort is needed for validation, this model represents a promising tool for preoperative assessment of IPMN.