PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics.
Journal
Nature communications
Journal Volume
17
Journal Issue
1
Pages
1595
ISSN
2041-1723
Date Issued
2026-02-13
Author(s)
Wu, Dan-Ni
Jen, Joey
Fajiculay, Erickson
Hsu, Min-Fen
Yeh, Jen-Chen
Sargsyan, Karen
Kupcinskas, Juozas
Skieceviciene, Jurgita
Steponaitiene, Ruta
Morkunas, Egidijus
Gedgaudiene, Greta
Hsu, Chao-Ping
Hu, Chun-Mei
Abstract
Late diagnosis and the lack of effective early detection techniques contribute to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). To address this challenge, we develop ¹H NMR-based metabolomics-AI platforms employing customized multilayer support vector machine (SVM), AutoGluon, and Tabular Foundation Model (TabPFN) frameworks. These platforms integrate serum metabolomic profiles-including small-molecule metabolites and lipoproteins-with clinical/biochemical parameters (age, CA19-9) and Activin A, derived from 902 participants (424 high-risk controls and 478 PDAC cases). Our TabPFN-based algorithm, PanMETAI, outperform state-of-the-art models. In the Taiwanese training and validation cohort, the model achieved an impressive AUC of 0.99 (95% CI: 0.98-0.99). Its robustness is further confirmed in a Lithuanian external validation cohort (n = 322), which yields an AUC of 0.93 (0.90-0.95). Notably, it identifies key signature patterns that improve early-stage (I/II) PDAC diagnosis and perform well with small sample sizes (n = 50). TabPFN-PanMETAI offers a rapid, accurate, and non-invasive tool for early PDAC detection, with strong potential for clinical application.
Type
journal article