Robustness and Accuracy of Radiomics Models for Classifying IASLC Grading in Lung Adenocarcinomas: A Comprehensive Analysis of a Large Multicenter CT Database

Introduction Accurate preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grading is crucial for developing individualized management and surgical strategies in lung adenocarcinomas (LUAD). Computed tomography (CT) radiomics serves as an important imaging biomarker for classification tasks in LUAD. However, the robustness and accuracy of radiomics models remain subjects of ongoing debate. Methods In this study, we conducted an analytical comparison of two critical steps in radiomics: dimensionality reduction and feature selection, aiming to differentiate between Grade 1 and Grade 2–3 tumors according to the preoperative IASLC grading system for LUAD. 1) For dimensionality reduction, we sequentially combined the T-test, Pearson correlation, and Least Absolute Shrinkage and Selection Operator (LASSO), while considering principal component analysis (PCA) for comparison. 2) For feature selection, we utilized various machine learning (ML) techniques including Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GBC), XGBoost, Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CatBoost). The diagnostic efficacy was evaluated using receiver operating characteristic curve (ROC) and the corresponding area under the curve (AUC). Result: The AUC for PCA combined with various ML feature selection methods ranged from 0.502 to 0.719 in this classification task. In contrast, the AUC for the combined T-test, Pearson, and LASSO dimensionality reduction methods, along with various ML feature selection methods, significantly increased from 0.818 to 0.869. Among these, the LGBM achieved the highest performance, reaching an AUC of 0.869, while LR displayed the lowest performance with an AUC of 0.818. Conclusion We demonstrated that the T-test→Pearson→LASSO approach is more appropriate for radiomics feature dimensionality reduction compared to PCA. Additionally, we improved the commonly used LR feature selection method in medical research by employing the more advanced LGBM for distinguishing between Grade 1 and Grade 2–3 tumors in accordance with the preoperative IASLC grading system for LUAD.