ArticleNature communications2026
PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Fast activity prediction of chemically modified siRNAs via structure-based energy calculations and inference-augmented tabular deep learning.Molecular therapy. Nucleic acids · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Specific diagnostic value of systemic immune-inflammation indices combined with nutritional and tumor markers in the stage diagnosis of pancreatic cancer: a retrospective cohort study.Frontiers in oncology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.