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Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.

Qunzhe Ding1, Chutong Lin2, Yatsu Lam3

  • 1School of Information Management, Wuhan University, Wuhan, Hubei, China.

Thoracic Cancer
|April 7, 2026
PubMed
Summary

This study developed a machine-learning model for predicting survival in stage IB lung squamous-cell carcinoma (LSCC). Results show no significant survival benefit from adjuvant chemotherapy, supporting a risk-adapted treatment approach for early-stage LSCC.

Keywords:
SHapley additive exPlanationsclinical decision supportmachine learningsquamous cell carcinoma of the lungsurvival prediction

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Area of Science:

  • Oncology
  • Machine Learning in Medicine
  • Biostatistics

Background:

  • Stage IB lung squamous-cell carcinoma (LSCC) lacks individualized survival prediction.
  • The benefit of adjuvant chemotherapy in stage IB LSCC remains debated.
  • Need for improved prognostic tools and treatment strategies in early-stage LSCC.

Purpose of the Study:

  • Develop an interpretable machine-learning model for predicting survival in stage IB LSCC.
  • Evaluate the added survival benefit of postoperative chemotherapy in stage IB LSCC.
  • Provide tools for individualized survival prediction and risk-adapted treatment decisions.

Main Methods:

  • Utilized SEER database data from 6445 stage IB LSCC patients (2000-2015).
  • Trained six machine-learning algorithms for 1-, 3-, and 5-year overall survival (OS) prediction, with external validation.
  • Employed SHAP analysis for model interpretability and propensity-score matching to assess chemotherapy benefit.

Main Results:

  • The LightGBM model demonstrated strong predictive performance (AUCs ~0.80-0.83) and generalizability.
  • Treatment modality was the key predictor; both surgery alone and surgery plus chemotherapy improved survival.
  • No significant OS difference was found between surgery alone and surgery plus chemotherapy, even in high-risk subgroups.

Conclusions:

  • The developed model provides individualized survival estimates for stage IB LSCC patients.
  • Findings support a risk-adapted, conservative adjuvant treatment strategy.
  • Results aid in integrating precision medicine and shared decision-making for early-stage LSCC management.