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Updated: Apr 15, 2026

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LANTERN-XGB: An Interpretable Multi-Modal Machine Learning for Improving Clinical Decision-Making in Lung Cancer.

Davide Dalfovo1,2, Carolina Sassorossi3,4, Elisa De Paolis5,6

  • 1OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, 01067 Dresden, Germany.

International Journal of Molecular Sciences
|April 14, 2026
PubMed

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Summary
This summary is machine-generated.

This study introduces LANTERN-XGB, an interpretable AI model for non-small cell lung cancer (NSCLC) that predicts occult lymph node metastasis. The model balances predictive accuracy with clinical transparency, offering a path for AI integration in precision oncology.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
  • Current multi-modal AI models for NSCLC prediction face challenges due to their "black box" nature and data fragmentation.
  • Interpretable AI is crucial for clinical adoption in precision oncology.

Purpose of the Study:

  • To develop and validate LANTERN-XGB, a hierarchical machine learning workflow for interpretable prediction of occult lymph node metastasis (OLM) in NSCLC.
  • To generate "digital human avatars" for patient-specific decision support in precision oncology.
  • To bridge the gap between complex AI algorithms and routine clinical practice.

Main Methods:

  • Developed a multi-stage scalable tree boosting system (XGBoost) workflow named LANTERN-XGB.
Keywords:
artificial intelligencelung cancermulti-modal integrationprecision oncologyradiogenomics

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  • Utilized Shapley Additive Explanations (SHAP) for hierarchical feature selection and missing value management.
  • Integrated diverse data modalities (CT, PET, clinical) and validated on retrospective, prospective, and external datasets.

    • Main Results:

    • LANTERN-XGB identified a consensus signature for OLM prediction based on CT, PET, and clinical data interactions.
    • Achieved robust discrimination in external validation (AUC ≈ 0.77), comparable to benchmarks.
    • Demonstrated superior utility in handling diagnostic ambiguity through interpretable "local force plots".

    Conclusions:

    • LANTERN-XGB provides a validated, open-source framework balancing predictive power with clinical transparency for NSCLC.
    • The workflow enables visualization and verification of AI predictions, facilitating clinical integration.
    • Offers a pragmatic approach for deploying reliable multi-modal AI in daily medical decision-making.