Clinical-Radiomics Model Enhancing Prediction of Occult Nodal Metastasis in cT1a-bN0M0-stage Lung Adenocarcinoma: A Multi-center Study

Purpose To construct radiomics models for predicting occult nodal metastasis (ONM) in cT1a-bN0M0-stage lung adenocarcinoma (LUAD) and evaluate the multi-center diagnostic performance of models. Methods 1672 patients from six hospitals were collected including training set (n = 687), test set (n = 297) and external validation set (n = 688). Generalized linear model (GLM), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM) and the Clinical-Radiomics (Clinic-Rad) models were constructed and validated to predict ONM. Diagnostic performance was quantified by the area under receiver operative characteristic curve (AUC), and compared using De-Long test. Correlations of radiomics features with pathological characteristics were evaluated by Mantel-test. Results Compared to GLM-, SVM-, RF- and GBM-models, the Clinic-Rad model integrating clinical predictors and Radscore received superior diagnostic efficacy in validation set (0.813 ± 0.019 versus 0.790 ± 0.021, 0.761 ± 0.023, 0.708 ± 0.026, 0.769 ± 0.022; all P < 0.001), although no statistical differences in test set (0.834 ± 0.023 versus 0.827 ± 0.024, 0.829 ± 0.025, 0.838 ± 0.023, 0.826 ± 0.024; all P > 0.05). The pooled sensitivity, specificity, accuracy of the Clinic-Rad model was 77.2–75.8%, 72.0–72.7%, 72.7–74.4%. Besides, it was well predictive in solid- and subsolid-appearance LUAD respectively, with pooled AUC values of 0.802–0.820 and 0.797–0.917. Furthermore, radiomics models significantly outperformed clinical predictors including solid-component diameter, consolidation-to-tumor ratio, CEA level and the combined diagnosis (AUC values: versus 0.669–0.678, 0.542–0.600, 0.571–0.613 and 0.683–0.724; all P < 0.001). The Mantel-test demonstrated 88.9%(n = 16/18) of selected radiomics features, Radscore and predicted ONM possibilities were correlated with poorly-differentiated, lymph-vessel invasion, visceral pleura invasion. Conclusions Radiomics features are useful to predict ONM in cT1a-bN0M0-stage LUAD and the Clinic-Rad model shows the best diagnostic performance.