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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Neighborhood matters: Predicting lung cancer screening adherence with explainable AI.

Kun-Han Lu1, Yi Xiao2, Aamna Akhtar3

  • 1Department of Applied AI and Data Science, City of Hope, Duarte, CA, USA; Division of Mathematics for Cancer Evolution and Early Detection, Beckman Research Institute, City of Hope, Duarte, CA, USA.

Lung Cancer (Amsterdam, Netherlands)
|December 22, 2025
PubMed
Summary

Lung cancer screening (LCS) adherence is predicted by social determinants of health (SDOH). Neighborhood factors like poverty are key predictors of non-adherence, informing targeted interventions for high-risk individuals.

Keywords:
Lung cancer screening adherenceMachine learningPredictive ModelingSocial determinants of health

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Area of Science:

  • Public Health
  • Machine Learning
  • Health Disparities

Background:

  • Lung cancer screening (LCS) adherence is crucial for early detection in high-risk populations.
  • Social determinants of health (SDOH) significantly impact healthcare access and adherence.
  • Predictive modeling can identify individuals at risk for LCS non-adherence.

Purpose of the Study:

  • To develop a predictive model for lung cancer screening adherence using SDOH data.
  • To identify key individual and neighborhood-level factors associated with LCS non-adherence.
  • To improve risk stratification for individuals unlikely to complete annual LCS follow-up scans.

Main Methods:

  • Recruited 188 high-risk, minoritized individuals for low-dose computed tomography (LDCT) screening.
  • Collected demographic, tobacco use, social needs, and risk perception data via surveys.
  • Utilized XGBoost classifier with SHAP analysis to predict LCS adherence based on individual and geocoded neighborhood SDOH metrics.

Main Results:

  • The study cohort comprised diverse minoritized groups, with a 66% LCS non-adherence rate.
  • The predictive model achieved strong performance (AUROC 0.81, AUPRC 0.90).
  • Neighborhood SDOH factors (e.g., school proficiency, poverty) were more predictive of non-adherence than individual factors.

Conclusions:

  • Machine learning accurately predicts LCS non-adherence using SDOH.
  • Community-level characteristics are vital for informing LCS adherence interventions.
  • Regionally tailored strategies can improve LCS adherence in high-risk populations.