Academic radiology · 2025
Preoperative Prediction of Occult Lymph Node Metastasis in Clinically Node-Negative Early-Stage Lung Adenocarcinoma: A Multicenter Machine Learning Study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Zhao Fengnian, Zhao Yunqing, Ye Zhaoxiang, Sun Haoran, Li Yanbo, Zhou Guiming
Аннотация
BACKGROUND: Occult lymph node metastasis (OLNM) in cN0 early-stage lung adenocarcinoma (LUAD) leads to pathological understaging and suboptimal surgical management. Current prediction tools exhibit limited robustness. This study aimed to develop and validate a machine learning model that integrates CT semantic and radiomic features for the preoperative prediction of OLNM.
METHODS: In this retrospective multicenter study, an interpretable machine learning model was developed. A cohort of 752 patients (training: n = 495; internal validation: n = 124; external validation: n = 133) underwent rigorous feature processing: ComBat harmonization for scanner variability, PCA dimensionality reduction, and LASSO regression for feature selection. Seven classifiers were optimized using SMOTE-balanced training data.
RESULTS: The XGBoost model demonstrated robust performance, achieving an ROCAUC of 0.814 (0.666-0.925) and a PR-AUC of 0.502 (0.328-0.803) in the internal validation cohort. It maintained strong generalizability in the external validation cohort, with an ROC-AUC of 0.826 (0.746-0.897) and a PR-AUC of 0.486 (0.331-0.714). The model was well-calibrated (Brier scores: 0.135 and 0.128, respectively). Risk stratification identified five clinically actionable tiers: "very-low-risk" to "very-high-risk" patients exhibited monotonically increasing rates of OLNM (internal validation: 3.7% to 40.0%; external validation: 4.1-fold increase in metastasis). SHAP analysis identified consolidation level, radiomics-derived Rad-score, and lobulation as the top three predictors.
CONCLUSION: This validated model integrates physician-interpreted semantics with data-driven radiomics, providing a non-invasive tool for personalized surgical planning. It enables tailored lymph node dissection strategies while enhancing accessibility in resource-limited settings.
DATA AVAILABILITY: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
METHODS: In this retrospective multicenter study, an interpretable machine learning model was developed. A cohort of 752 patients (training: n = 495; internal validation: n = 124; external validation: n = 133) underwent rigorous feature processing: ComBat harmonization for scanner variability, PCA dimensionality reduction, and LASSO regression for feature selection. Seven classifiers were optimized using SMOTE-balanced training data.
RESULTS: The XGBoost model demonstrated robust performance, achieving an ROCAUC of 0.814 (0.666-0.925) and a PR-AUC of 0.502 (0.328-0.803) in the internal validation cohort. It maintained strong generalizability in the external validation cohort, with an ROC-AUC of 0.826 (0.746-0.897) and a PR-AUC of 0.486 (0.331-0.714). The model was well-calibrated (Brier scores: 0.135 and 0.128, respectively). Risk stratification identified five clinically actionable tiers: "very-low-risk" to "very-high-risk" patients exhibited monotonically increasing rates of OLNM (internal validation: 3.7% to 40.0%; external validation: 4.1-fold increase in metastasis). SHAP analysis identified consolidation level, radiomics-derived Rad-score, and lobulation as the top three predictors.
CONCLUSION: This validated model integrates physician-interpreted semantics with data-driven radiomics, providing a non-invasive tool for personalized surgical planning. It enables tailored lymph node dissection strategies while enhancing accessibility in resource-limited settings.
DATA AVAILABILITY: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.