Multiinstitutional Validation Study of Cyst Fluid Protein Biomarkers in Patients With Cystic Lesions of the Pancreas.

OBJECTIVE: Prospective evaluation of 2 clinical-molecular models in patients with unknown pathology who underwent endoscopic ultrasound with fine-needle aspiration (EUS-FNA) for a cystic lesion of the pancreas. SUMMARY OF BACKGROUND DATA: Preoperative prediction of histologic subtype (mucinous vs nonmucinous) and grade of dysplasia in patients with pancreatic cystic neoplasms is challenging. Our group has previously published 2 clinical-molecular nomograms for intraductal papillary mucinous neoplasms (IPMN) that incorporated both clinical/radiographic features and cyst fluid protein markers (sFASL, CA72-4, MMP9, IL-4). METHODS: This multiinstitutional study enrolled patients who underwent EUS-FNA for a cystic lesion of the pancreas. Treatment recommendations regarding resection were based on standard clinical, radiographic, and endoscopic features. Predicted probabilities of high-risk IPMN (high-grade dysplasia/invasive cancer) were calculated using the previously developed clinical-molecular nomograms. RESULTS: Cyst fluid was obtained from 100 patients who underwent diagnostic EUS-FNA. Within this group there were 35 patients who underwent resection, and 65 were monitored radiographically. Within the group that underwent resection, 26 had low-risk IPMN or benign non-IPMN lesions, and 9 had high-risk IPMN. Within the surveillance group, no patient progressed to resection or developed cancer after a median follow-up of 12months (range: 0.5-38). Using the clinical/radiographic nomogram alone, 2 out of 9 patients with high-risk IPMN had a predicted probability >0.5. In the clinical-molecular models, 6 of 9 patients in model 1, and 6 of 9 in model 2, had scores >0.5. CONCLUSIONS: This prospective study of patients with unknown cyst pathology further demonstrates the importance of cyst fluid protein analysis in the preoperative identification of patients with high-risk IPMN. Longer follow-up is necessary to determine if this model will be useful in clinical practice.

Multi-institutional Validation Study of Pancreatic Cyst Fluid Protein Analysis for Prediction of High-risk Intraductal Papillary Mucinous Neoplasms of the Pancreas.

OBJECTIVE: Preliminary work by our group suggested that proteins within the pancreatic cyst fluid (CF) may discriminate degree of IPMN dysplasia. We sought to externally validate these markers and determine whether their inclusion in a preoperative clinical nomogram could increase diagnostic accuracy. SUMMARY BACKGROUND DATA: IPMN is the most common radiographically identifiable precursor to pancreatic cancer; however, the timing and frequency of its malignant progression are unknown, and there are currently no reliable preoperative tests that can determine the grade of dysplasia in IPMN. METHODS: Clinical and radiographic data, as well as CF samples, were obtained from 149 patients who underwent resection for IPMN at 1 of 3 institutions. High-risk disease was defined as the presence of high-grade dysplasia or invasive carcinoma. Multianalyte bead array analysis (Luminex) of CF was performed for 4 protein markers that were previously associated with high-risk disease. Logistic regression models were fit on training data, with and without adjustment for a previously developed clinical nomogram and validated with an external testing set. The models incorporating clinical risk score were presented graphically as nomograms. RESULTS: Within the group of 149 resected patients, 89 (60%) had low-risk disease, and 60 (40%) had high-risk disease. All 4 CF markers (MMP9, CA72-4, sFASL, and IL-4) were overexpressed in patients with high-risk IPMN (P < 0.05). Two predictive models based on preselected combinations of CF markers had concordance indices of 0.76 (Model-1) and 0.80 (Model-2). Integration of each CF marker model into a previously described clinical nomogram leads to increased discrimination compared with either the CF models or nomogram alone (c-indices of 0.84 and 0.83, respectively). CONCLUSIONS: This multi-institutional study validated 2 CF protein marker models for preoperative identification of high-risk IPMN. When combined with a clinical nomogram, the ability to predict high-grade dysplasia was even stronger.

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 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.