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Applications of machine learning models in the prediction of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma
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Huanhuan Liu1a, Ye Chen1b, Lu Zhou2, Hongliang Qi1b, *
Modern Instruments & Medical Treatment | 2025, 31(1) : 37 - 42
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Modern Instruments & Medical Treatment | 2025, 31(1): 37-42
Applications of machine learning models in the prediction of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma
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Huanhuan Liu1a, Ye Chen1b, Lu Zhou2, Hongliang Qi1b, *
Affiliations
  • 1a.Department of Radiotherapy, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
  • 1b.Department of Medical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
  • 2.Department of Radiology, Xiangtan Central Hospital, Xiangtan 411100, China
Published: 2025-02-10 doi: 10.11876/mimt202501007
Outline
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Objective

This study aims to explore the clinical features and CT imaging manifestations of spread through air spaces (STAS) in stage IA subsolid lung adenocarcinoma and to construct a predictive model based on machine learning algorithms.

Methods

We retrospectively analyzed patients with stage IA subsolid lung adenocarcinoma admitted to Xiangtan Central Hospital from January 2021 to June 2023. Patients were classified into STAS(+) and STAS(-) groups based on postoperative pathological results. We employed five machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (Xgboost), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF)—to develop a predictive model for STAS in stage IA subsolid lung adenocarcinoma.

Results

Among the 106 patients with stage IA subsolid lung adenocarcinoma, 16 (15%) were diagnosed with STAS. After comparing the five machine learning algorithms, Xgboost demonstrated the highest diagnostic accuracy, achieving an area under the curve (AUC) of 0.969 (95% CI: 0.915-0.993). The Xgboost algorithm identified several key predictors of STAS in stage IA subsolid lung adenocarcinoma: higher proportion of solid component, CT value of the solid component, lesion size, degree of blurring in the surrounding ground-glass opacity (GGO) border, and volume of the solid component.

Conclusion

Through the analysis of clinical and imaging data, the Xgboost algorithm can effectively predict STAS in stage IA subsolid lung adenocarcinoma, providing valuable insights for clinical decision-making.

Stage IA subsolid lung adenocarcinoma  /  Spread through air spaces  /  Machine learning  /  Predictive model
刘欢欢, 陈业, 周露, 齐宏亮. 基于机器学习对亚实性IA期肺腺癌气道内播散的预测价值. 现代仪器与医疗, 2025 , 31 (1) : 37 -42 . DOI: 10.11876/mimt202501007
Huanhuan Liu, Ye Chen, Lu Zhou, Hongliang Qi. Applications of machine learning models in the prediction of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma[J]. Modern Instruments & Medical Treatment, 2025 , 31 (1) : 37 -42 . DOI: 10.11876/mimt202501007
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Year 2025 volume 31 Issue 1
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doi: 10.11876/mimt202501007
  • Online Date：2026-10-23
  • Published：2025-02-10
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    1a.Department of Radiotherapy, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
    1b.Department of Medical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
    2.Department of Radiology, Xiangtan Central Hospital, Xiangtan 411100, China
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