Combined Model Integrating Clinical-Imaging and Deep Learning to Discriminate Between Persistent Inflammatory and Malignant Subsolid Pulmonary Nodules.

This study aimed to develop a combined model integrating clinical, computed tomography (CT), and deep learning (DL) features to discriminate malignant subsolid nodules (M-SSNs) from persistent inflammatory subsolid nodules (PI-SSNs). A total of 573 patients with subsolid nodules (SSNs) who underwent preoperative chest CT and surgical resection at Center 1 were assigned to training and internal validation sets (ratio 4:1), and an external testing cohort comprising 199 patients from Center 2 was also enrolled. A clinical model was first developed using clinical and CT features that differed significantly between M-SSNs and PI-SSNs. Subsequently, five DL models were constructed based on 3D convolutional neural network architectures (VGG11, ResNet18, SEResNet34, Res2Net50, and DenseNet121), and the best-performing DL model was selected as the feature extractor. The extracted DL features were then combined with the selected clinical-CT features to form a comprehensive feature pool. Recursive feature elimination was applied to identify the most discriminative features. Finally, a combined model was built using the random forest algorithm on the refined feature subset. The performance of the clinical model, the optimal DL model, and the combined model was compared to determine the best-performing model. The clinical model was constructed using three features selected by the random forest algorithm: well-defined boundary, lobulation, and mixed ground-glass opacity with multiple solid components. In the external testing cohort, this model achieved an area under the curve (AUC) of 0.773, with an accuracy of 0.683, sensitivity of 0.654, specificity of 0.746, precision of 0.848, and F1-score of 0.739. Among the five DL models, the 3D Res2Net50 model performed best, attaining an AUC of 0.827 in the external test set, along with an accuracy of 0.809, sensitivity of 0.809, specificity of 0.810, precision of 0.902, and F1-score of 0.853. The combined model integrated three clinical-CT features with 20 discriminative DL features derived from the 3D Res2Net50 model. It achieved an AUC of 0.875 in the external testing cohort, with accuracy, sensitivity, specificity, precision, and F1-score values of 0.814, 0.824, 0.794, 0.896, and 0.858, respectively, and the lowest Brier score (0.146) among the three models, indicating the best probability calibration. In this cohort, the combined model showed superior performance compared to both the clinical model and the 3D Res2Net50 DL model. In a reader study, AI assistance significantly improved radiologists' majority-vote AUC from 0.783 to 0.833 (P < 0.05). The decision curve analysis results demonstrated that the combined model provided a higher standardized net benefit than the clinical model, the DL model, and both the treat-all and treat-none strategies across a wide range of threshold probabilities (all P < 0.05). The combined model based on clinical-CT and DL features is valuable in differentiating PI-SSNs from M-SSNs and contributes to optimizing surgical indications and guiding personalized treatment strategies for SSNs.