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<p>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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(&gt;95\%\)</EquationSource> </InlineEquation> area under the curve (AUC) and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\sim 90\%\)</EquationSource> </InlineEquation> sensitivity when controlling specificity at <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(90\%\)</EquationSource> </InlineEquation>. 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.</p>

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Machine learning-based multimodal biomarkers enable accurate diagnosis and early detection of pancreatic ductal adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • 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

摘要

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 \(>95\%\) area under the curve (AUC) and \(\sim 90\%\) sensitivity when controlling specificity at \(90\%\) . 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.