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Background <p>Liver metastasis at the time of pancreatic cancer diagnosis plays a critical role in treatment planning owing to its strong association with poor prognosis. However, they often remain undetected because of the limited sensitivity of conventional imaging and biomarkers. Previous studies have primarily focused on postoperative liver metastasis or relied on complex nonroutine variables (e.g., liquid biopsy and radiomics), which limit scalability and real-world applicability. To address this unmet need, we applied an machine learning (ML) approach chosen for its interpretability, developing a simple, real-time prediction model that uses only routine clinical data available at diagnosis.</p> Methods <p>We retrospectively enrolled 2,657 patients with pancreatic cancer from a tertiary centre to develop the Liver Metastasis in Pancreatic Cancer (LiMPC) model. The model was trained using 21 routinely available clinical variables and compared across four ML algorithms. The best performing model (extreme gradient boosting) was calibrated using isotonic regression and externally validated in five independent hospitals (<i>n</i> = 272). Model performance was evaluated using AUROC, sensitivity, specificity, negative predictive value, positive predictive value, and calibration plots. Clinical utility was assessed with decision curve analysis, and feature contributions were interpreted using SHapley Additive exPlanations (SHAP).</p> Results <p>The fine-tuned LiMPC model achieved strong external validation performance (AUROC = 0.78, sensitivity = 0.81, specificity = 0.55) with robust calibration and consistent clinical net benefit. SHAP interpretation identified CA19-9, CEA, GGT, and age as key predictors, consistent with established biomarkers of advanced disease. In the subgroup analysis, the model achieved particularly strong discrimination in older (AUROC = 0.82) and male (AUROC = 0.82) patients, suggesting demographic influences on metastatic risk. In supplementary analyses, baseline predictors remained consistent among patients who later developed liver metastasis, reinforcing the model’s biological plausibility and clinical relevance.</p> Conclusions <p>LiMPC is an externally validated, interpretable tool for liver metastasis risk stratification using routinely collected clinical data. As a hypothesis-generating tool, it demonstrates how simple clinical variables can provide decision support when imaging results are&#xa0;inconclusive, offering a practical framework for future prospective validation and clinical implementation.</p>

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Early prediction of liver metastasis in pancreatic cancer using routine clinical data: an externally validated machine learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Yeo Gyeong Ko,
  • See Young Lee,
  • Won Kyu Lee,
  • Ji Hoon Park,
  • Sang Hoon Lee,
  • Kyung In Shin,
  • Jiyoung Keum,
  • Jee Hoon Kim,
  • Jung Hyun Jo,
  • Sung Ill Jang,
  • Jae Hee Cho,
  • Galam Leem,
  • Moon Jae Chung,
  • Jeong Youp Park,
  • Seungmin Bang,
  • Seung Woo Park,
  • Seung Up Kim,
  • Hee Seung Lee

摘要

Background

Liver metastasis at the time of pancreatic cancer diagnosis plays a critical role in treatment planning owing to its strong association with poor prognosis. However, they often remain undetected because of the limited sensitivity of conventional imaging and biomarkers. Previous studies have primarily focused on postoperative liver metastasis or relied on complex nonroutine variables (e.g., liquid biopsy and radiomics), which limit scalability and real-world applicability. To address this unmet need, we applied an machine learning (ML) approach chosen for its interpretability, developing a simple, real-time prediction model that uses only routine clinical data available at diagnosis.

Methods

We retrospectively enrolled 2,657 patients with pancreatic cancer from a tertiary centre to develop the Liver Metastasis in Pancreatic Cancer (LiMPC) model. The model was trained using 21 routinely available clinical variables and compared across four ML algorithms. The best performing model (extreme gradient boosting) was calibrated using isotonic regression and externally validated in five independent hospitals (n = 272). Model performance was evaluated using AUROC, sensitivity, specificity, negative predictive value, positive predictive value, and calibration plots. Clinical utility was assessed with decision curve analysis, and feature contributions were interpreted using SHapley Additive exPlanations (SHAP).

Results

The fine-tuned LiMPC model achieved strong external validation performance (AUROC = 0.78, sensitivity = 0.81, specificity = 0.55) with robust calibration and consistent clinical net benefit. SHAP interpretation identified CA19-9, CEA, GGT, and age as key predictors, consistent with established biomarkers of advanced disease. In the subgroup analysis, the model achieved particularly strong discrimination in older (AUROC = 0.82) and male (AUROC = 0.82) patients, suggesting demographic influences on metastatic risk. In supplementary analyses, baseline predictors remained consistent among patients who later developed liver metastasis, reinforcing the model’s biological plausibility and clinical relevance.

Conclusions

LiMPC is an externally validated, interpretable tool for liver metastasis risk stratification using routinely collected clinical data. As a hypothesis-generating tool, it demonstrates how simple clinical variables can provide decision support when imaging results are inconclusive, offering a practical framework for future prospective validation and clinical implementation.