Ann Surg Treat Res.  2021 Mar;100(3):144-153. 10.4174/astr.2021.100.3.144.

Diagnostic model for pancreatic cancer using a multi-biomarker panel

Affiliations
  • 1Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
  • 2Bio-MAX/N-Bio Institute, Seoul National University, Seoul, Korea
  • 3Department of Statistics, Seoul National University, Seoul, Korea
  • 4Department of Applied Mathematics, Sejong University, Seoul, Korea
  • 5Data Labs, AI Center, SK Telecom, Seoul, Korea
  • 6Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  • 7Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  • 8Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, Korea
  • 9Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
  • 10Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Korea
  • 11Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
  • 12Center for Liver Cancer, National Cancer Center, Seoul, Korea

Abstract

Purpose
Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.
Methods
Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups).
Results
The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value.
Conclusion
This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Keyword

Biomarkers; Enzyme-linked immunosorbent assay; Pancreatic intraductal neoplasms
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