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LAMPAD: An Integrated Circulating Tumor DNA-Based Model for Predicting Potential Cure in Patients With Resected

Jia-Tao Zhang1, Ke-Zhong Chen2, Xuan Gao3

  • 1Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China; Chinese Thoracic Oncology Group Young Investigators Committee (CTONG-Young), Guangzhou, Guangdong, People's Republic of China.

Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
|April 19, 2026
PubMed
Summary

The LAMPAD model improves non-small cell lung cancer (NSCLC) prognosis by analyzing circulating tumor DNA (ctDNA) from two blood draws. This approach accurately identifies truly disease-free patients, enhancing treatment decisions.

Keywords:
Circulating tumor DNADisease-free survivalLandmark detectionMolecular residual diseaseNon–small cell lung cancer

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Area of Science:

  • Oncology
  • Molecular Diagnostics
  • Bioinformatics

Background:

  • Postoperative molecular residual disease (MRD) assessment in non-small cell lung cancer (NSCLC) often uses circulating tumor DNA (ctDNA).
  • Single-timepoint ctDNA analysis has a high false-negative rate (~70%), limiting its utility for guiding adjuvant therapy decisions within the narrow postoperative window.
  • Accurate identification of MRD-negative patients is crucial for determining eligibility for curative treatment.

Purpose of the Study:

  • To develop and validate a novel model (LAMPAD) for improved prognostic stratification of NSCLC patients after surgery.
  • To refine the identification of truly disease-free patients among those with undetectable MRD at the standard postoperative timepoint.
  • To enhance the utility of ctDNA analysis for guiding adjuvant therapy and patient management in NSCLC.

Main Methods:

  • Introduction of LAMPAD, an XGBoost-Cox model integrating preoperative and postoperative ctDNA quantification.
  • Utilizing Shapley Additive Explanations (SHAP) to identify key features: baseline ctDNA, TNM stage, landmark cfDNA, baseline cfDNA, and baseline ctDNA status.
  • Training and validation on 163 stage I-III NSCLC patients with landmark undetectable MRD, across fixed-panel and personalized ctDNA-MRD approaches.

Main Results:

  • The LAMPAD model significantly stratified patients into low-risk (2-year DFS: 97.8%) and high-risk (2-year DFS: 71.6%) groups (HR=0.11, p<0.001).
  • Consistent performance observed in validation cohorts (pooled 2-year DFS: 94.3% vs 72.4%; HR=0.18, p<0.001).
  • Preoperative ctDNA analysis and methylation analysis of cfDNA (immune- and lung-derived) provided significant prognostic value, suggesting immune system involvement.

Conclusions:

  • The LAMPAD model surpasses single-timepoint postoperative ctDNA detection in accuracy.
  • It reliably identifies truly MRD-negative NSCLC patients, distinguishing them from those with residual disease.
  • LAMPAD offers a more dependable prognostic tool for identifying low-risk NSCLC patients with a higher likelihood of cure.