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<p>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 (<i>n</i> = 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 (<i>n</i> = 50). TabPFN-PanMETAI offers a rapid, accurate, and non-invasive tool for early PDAC detection, with strong potential for clinical application.</p>

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PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月13日

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  • Dan-Ni Wu,
  • Joey Jen,
  • Erickson Fajiculay,
  • Min-Fen Hsu,
  • Ming-Chu Chang,
  • Jen-Chen Yeh,
  • Karen Sargsyan,
  • Juozas Kupcinskas,
  • Jurgita Skieceviciene,
  • Ruta Steponaitiene,
  • Egidijus Morkunas,
  • Greta Gedgaudiene,
  • Chao-Ping Hsu,
  • Yu-Ting Chang,
  • Chun-Mei Hu

摘要

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.