Multi-Biomarker Panel Prediction Model for Diagnosis of Pancreatic Cancer: a retrospective and multi-center cohort study

Abstract Background Early diagnosis is paramount in increasing the survival rate of pancreatic ductal adenocarcinoma (PDAC). However, effective early diagnostic tools are lacking at present. The current study aimed to develop a prediction model using a multi-marker panel (LRG1, TTR, and CA19-9) as a diagnostic screening tool for PDAC. Methods A large multi-center cohort of 1,991 samples were collected from January 2011 to September 2019, of which 609 are normal (NL), 145 are other cancer (OC; colorectal, thyroid, and breast cancer), 314 are pancreatic benign disease (PB), and 923 are PDAC. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers, LRG1, TTR, and CA 19 − 9. Using a logistic regression (LR) model trained on training data set, the predicted values for PDACs were obtained, and the result was classified into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and biomarkers TTR, CA 19 − 9, and LRG1. Results Participants were categorized into four groups as NL (n = 609, 30.6%), OC (n = 145, 7.3%), PB (n = 314, 15.7%), and PDAC (n = 923, 46.4%). The NL, OC, and PB groups were clubbed into the non-PDAC group (n = 1068, 53.6%). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. Conclusions This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing PDAC from normal and benign pancreatic disease states, as well as patients with other cancers. The study indicates that the introduced multi-marker panel prediction model for PDAC diagnosis can help guide medical decisions for patients, including patients with early stage PDAC or with normal levels of CA 19 − 9.