ArticleBMC medical imaging2026
The impact of chest computed tomography-defined emphysema on extrapulmonary metastases in patients with lung cancer.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients with coexisting emphysema and lung cancer present a complex clinical prognosis, yet current research evidence on this population remains limited. This study aimed to evaluate the prognostic value of CT-defined emphysema for extrapulmonary metastasis and develop a predictive machine-learning model.
methodsA retrospective analysis was conducted on patients diagnosed with lung cancer between January 2015 and December 2018 and followed up until December 2024. CT-defined emphysema was quantified using the relative lung area with attenuation ≤ − 950 Hounsfield Units (LAA%), with severity stratified as follows: ≤6% (none), > 6% and ≤ 9% (mild), and > 9% (moderate-severe). Distant metastasis-free survival (DMFS) was analyzed using Kaplan-Meier and log-rank tests. Least absolute shrinkage and selection operator (LASSO) regression identified significant predictors for extrapulmonary metastasis. An eXtreme Gradient Boosting (XGBoost) model incorporating these features was developed and internally validated via stratified 10-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). SHAP analysis and a nomogram were performed based on the selected variables.
resultsA total of 542 patients were included in the study. Lung cancer patients with or without CT-defined emphysema showed significant differences in multiple clinical and pathological characteristics (sex, age, histological subtype, smoking status, BMI, etc.). Patients with moderate-severe CT-defined emphysema (LAA% >9%) exhibited worse DMFS than those with mild or no emphysema. XGBoost model showed better metastasis predictive efficiency than other machine learning models, especially when concurrent emphysema was considered (AUC = 0.820, NRI = 0.26, P < 0.001). SHAP analysis revealed the top three contributions for predicting distant metastasis in the model were tumor size, CT-defined emphysema, and smoking status.
conclusionCT-defined emphysema severity predicts worse DMFS and improves extrapulmonary metastasis risk in lung cancer. The developed interpretable XGBoost model and nomogram provide clinically valuable tools for personalized prognosis assessment in lung cancer patients with underlying emphysema.
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