Preoperative Prediction of Occult Lymph Node Metastasis in Clinically Node-Negative Early-Stage Lung Adenocarcinoma: A Multicenter Machine Learning Study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Ultrasound, Tianjin Medical University General Hospital, Anshan Road, Heping District, Tianjin 300052, China (F.Z., G.Z.). Electronic address: [email protected].
- Department of Radiology, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing Road, Heping District, Tianjin 300052, China (Y.Z.). Electronic address: [email protected].
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center, Key Laboratory of Cancer Prevention and Therapy, Huanhuxi Road, Hexi District, Tianjin 300060, China (Z.Y.). Electronic address: [email protected].
- Department of Radiology, Tianjin Medical University General Hospital, Anshan Road, Heping District, Tianjin 300052, China (H.S.). Electronic address: [email protected].
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huanhuxi Road, Hexi District, Tianjin 300060, China (Y.L.). Electronic address: [email protected].
- Department of Ultrasound, Tianjin Medical University General Hospital, Anshan Road, Heping District, Tianjin 300052, China (F.Z., G.Z.). Electronic address: [email protected].
Abstract
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. 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. 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. 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. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.