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Article Open Access

Development of a machine learning model for preoperative prediction of spread through air spaces in resectable 
non‑small cell lung cancer: A single‑center retrospective study

  • Authors:
    • Chong Yang
    • Guozheng Ding
    • Bicheng Zhan
    • Lanlan Xuan
    • Feifei Cheng
    • Yanguo Yang
  • View Affiliations / Copyright

    Affiliations: Department of Internal Medicine, Graduate School of Bengbu Medical University, Bengbu, Anhui 233030, P.R. China, Department of Respiratory and Critical Care Medicine, Anqing Municipal Hospital, Anqing, Anhui 246003, P.R. China, Department of Cardiothoracic Surgery, Anqing Municipal Hospital, Anqing, Anhui 246003, P.R. China, Department of Pathology, Anqing Municipal Hospital, Anqing, Anhui 246003, P.R. China
    Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 60
    |
    Published online on: December 1, 2025
       https://doi.org/10.3892/ol.2025.15413
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Abstract

Spread through air spaces (STAS) is a pathological feature associated with poor prognosis in non‑small cell lung cancer (NSCLC). However, its diagnosis currently depends exclusively on postoperative histopathological examination, limiting its utility for preoperative surgical planning. The present study aimed to develop an interpretable machine learning (ML) model using preoperative clinical and semantic CT features to predict STAS in surgically resectable NSCLC. The present study retrospectively analyzed 584 patients with pathologically confirmed NSCLC who underwent surgical resection. A total of five ML algorithms were developed using routinely available preoperative data and evaluated using repeated 5‑fold cross‑validation to ensure model robustness and mitigate overfitting. The optimal model was selected based on area under receiver operating characteristic curve (AUC). Feature importance was assessed using SHapley Additive exPlanations (SHAP) analysis for interpretability. Among the five models, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive performance (mean cross‑validated AUC=0.868 on training set; AUC=0.764 on test set). SHAP analysis identified nodule type, lobulation and smoking history as the most influential features associated with STAS. In conclusion, the present study developed a clinically interpretable XGBoost model capable of predicting STAS using readily accessible preoperative features. This model holds promise as a decision‑support tool to potentially guide personalized surgical strategies in NSCLC in the future.
View Figures

Figure 1

Model evaluation curves. (A) 5-fold
cross-validation AUC across folds on training set. (B) 5-fold
cross-validation AUC across folds on test set. (C) ROC curves in
the training set. (D) ROC curves on test set. (E) Calibration
curves on training set. (F) Calibration curves on test set. (G)
Pairwise DeLong's test P-values for AUC comparisons. Lower P-values
(blue) indicate significant differences between models. (H) Taylor
diagram summarizing correlation, standard deviation and root mean
square error between predicted and observed values. Models closer
to the reference point indicate improved overall agreement. AUC,
area under the curve; ROC, receiver operating characteristic;
XGBoost, eXtreme Gradient Boosting; SVM, support vector
machine.

Figure 2

SHAP analysis for the XGBoost model.
(A) Bar plot showing the mean absolute SHAP value of each feature,
indicating its overall importance in the model. (B) Beeswarm plot
displaying the impact and directionality of each feature on model
output across all cases. Red points represent higher feature
values, blue points indicate lower values. XGBoost, eXtreme
Gradient Boosting; SCC, squamous cell carcinoma; CYFRA21-1,
cytokeratin fragment-19; SHAP, SHapley Additive exPlanations; NSE,
neuron-specific enolase; CEA, carcinoembryonic antigen.
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Copy and paste a formatted citation
Spandidos Publications style
Yang C, Ding G, Zhan B, Xuan L, Cheng F and Yang Y: Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study. Oncol Lett 31: 60, 2026.
APA
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., & Yang, Y. (2026). Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study. Oncology Letters, 31, 60. https://doi.org/10.3892/ol.2025.15413
MLA
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., Yang, Y."Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study". Oncology Letters 31.2 (2026): 60.
Chicago
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., Yang, Y."Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study". Oncology Letters 31, no. 2 (2026): 60. https://doi.org/10.3892/ol.2025.15413
Copy and paste a formatted citation
x
Spandidos Publications style
Yang C, Ding G, Zhan B, Xuan L, Cheng F and Yang Y: Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study. Oncol Lett 31: 60, 2026.
APA
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., & Yang, Y. (2026). Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study. Oncology Letters, 31, 60. https://doi.org/10.3892/ol.2025.15413
MLA
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., Yang, Y."Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study". Oncology Letters 31.2 (2026): 60.
Chicago
Yang, C., Ding, G., Zhan, B., Xuan, L., Cheng, F., Yang, Y."Development of a machine learning model for preoperative prediction of spread through air spaces in resectable&nbsp;<br />non‑small cell lung cancer: A single‑center retrospective study". Oncology Letters 31, no. 2 (2026): 60. https://doi.org/10.3892/ol.2025.15413
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