Machine Learning-Based Multimodal Biomarkers Enable Accurate Diagnosis and Early Detection of Pancreatic Ductal Adenocarcinoma

Authors

Tsung-Hung Yao
Warapen Treekitkarnmongkol
Nagireddy Putluri
Deivendran Sankaran
Tristian Nguyen
Seetharaman Balasenthil
Mark W Hurd
Meng Chen
Randall E Brand
Paul D Lampe
Abu Hena M Kamal
Vasanta Putluri
Tony Y Hu
Anirban Maitra
Eugene J Koay
Ann M Killary
Subrata Sen
Suprateek Kundu

Language

English

Publication Date

1-7-2026

Journal

Scientific Reports

DOI

10.1038/s41598-025-29548-4

PMID

41495079

PMCID

PMC12780069

PubMedCentral® Posted Date

1-7-2026

PubMedCentral® Full Text Version

Post-print

Abstract

While there has been some progress on discovering clinically validated biomarkers for early detection in pancreatic ductal adenocarcinoma (PDAC), several challenges remain. Most approaches rely on single-modality biomarkers with limited sensitivity and/or specificity. Using data from a multicenter study with an age-matched cohort (n = 203 with healthy controls n = 46, pancreatitis controls n = 36, and diagnosed cases n = 121), we developed a machine learning approach integrating 2,096 microRNAs, 125 metabolites, and CA19-9. Our method performs unsupervised selection of an optimal subset of biomarkers with maximal discriminatory power for diagnosis and early detection. In training data, the selected biomarker panel achieved [Formula: see text] area under the curve (AUC) and [Formula: see text] sensitivity when controlling specificity at [Formula: see text]. The classification results under the selected multimodal panel generalize well for validation samples. The panel outperforms recently proposed microRNA-based approaches and identifies key biomarkers (such as aminobutyric acid and homovanillic acid) with high classification importance. Decision tree–based cut-offs are derived to enhance clinical interpretability, revealing the association between the low aminobutyric acid level and non-cancer status. These results highlight the superior discriminative ability and interpretability of the proposed multimodal biomarker panel, offering a promising tool for PDAC diagnosis and early detection.

Keywords

Pancreatic adenocarcinoma, Integrated multimodal analysis, Early detection, Biomarkers

Published Open-Access

yes

Recommended Citation

Yao, Tsung-Hung; Treekitkarnmongkol, Warapen; Putluri, Nagireddy; et al., "Machine Learning-Based Multimodal Biomarkers Enable Accurate Diagnosis and Early Detection of Pancreatic Ductal Adenocarcinoma" (2026). Faculty, Staff and Students Publications. 6477.
https://digitalcommons.library.tmc.edu/baylor_docs/6477