CT-based radiomics and intratumoral heterogeneity for predicting benign and malignant lesions in solid pulmonary...

Lung cancer remains one of the leading causes of cancer-related deaths worldwide. This study utilized clinical risk factors along with intratumoral radiomics, peritumoral radiomics, and intratumoral subregional features extracted from computed tomography (CT) lung-window images for individual and integrated modeling to classify solid pulmonary nodules and identify the optimal model, thereby improving diagnostic accuracy while minimizing unnecessary invasive procedures.

CT images of 230 pathologically confirmed solitary solid pulmonary nodules were retrospectively collected from two hospitals. Among the 167 patients from the first hospital, 20% (n=34) served as the test set, while the remaining 80% (n=133) were used as the training and development set for 5-fold cross-validation, while data from the second hospital (n=63) served as an external test set. Intratumoral and peritumoral regions of interest (ROIs) were delineated on lung window images, and relevant radiomics features were extracted. Multiple machine learning algorithms-including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Support Vector Classifier (Linear SVC) etc.-were employed to construct predictive models for distinguishing benign from malignant solid pulmonary nodules.

A triple-feature model (intratumoral, peritumoral, clinical) achieved superior diagnostic performance [area under the receiver operating characteristic curve (AUC): training 0.932, 95% confidence interval (CI): 0.897-0.960; test 0.833, 95% CI: 0.773-0.890; external test 0.741, 95% CI: 0.618-0.864] with high sensitivity/specificity. The intratumoral-peritumoral dual-modality model showed optimal cross-center robustness external test, AUC =0.808 (95% CI: 0.700-0.922). Habitat imaging revealed heterogeneity, AUC =0.750 (95% CI: 0.676-0.825). Decision curve analysis confirmed the triple-model's clinical utility. SHAP identified age, gender, and key radiomics (e.g., gradient_firstorder_Skewness_Intra) as top predictors. Multi-center test confirmed generalizability, positioning this integrated framework as a robust tool to reduce invasive procedures in pulmonary nodule management.

The multi-combination models developed in this study enhance the diagnostic accuracy for distinguishing benign from malignant solid pulmonary nodules, with the triple-feature model demonstrating the highest diagnostic performance. This approach has the potential to spare patients from unnecessary invasive procedures and strengthen clinical decision-making in the management of pulmonary nodules.