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Serum N-Glycan profiling for the diagnosis of pancreatic cancer: A Machine Learning Approach

biochemistry
gastroenterology-and-hepatology
oncology

Abstract

Purpose: To establish a predictive model by combining the results of laboratory examinations and the N-Glycan profiling can differentiate among healthy control (HC), pancreatic benign disease (PB), and pancreatic cancer (PC).

Methods: A total of 458 individuals, including PC, PB, and age-matched HC, were recruited from Jul 2015 to November 2019 in the First Affiliated Hospital with Nanjing Medical University. Age, Gender, CA19-9, CEA, and six biochemical indicators were recorded. Seven random forest machine learning models were developed to identify HC, PB, and PC, and the diagnostic performances were evaluated by accuracy, precision, and recall.

Results: Among those models, Model-gly-tm (which incorporates N-glycan and tumor markers) is relatively simple yet exhibits good predictive performance, achieving 86.60% and 79.08% accuracy in the training and testing sets, respectively. When distinguishing between PC and non-PC, the sensitivity and specificity in the training and testing groups are 93.79%, 91.25%, and 87.10%, 81.32%. Especially in individuals with negative CA19-9, this model can further diagnose 60.0% and 53.3% of PC patients in the training and testing datasets.

Conclusions: The serum N-glycan profile is a promising biomarker for PC. The N-glycan and tumor markers model is a valuable supplement to the serologic markers already in use.

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