Hybrid Deep Learning–Machine Learning Fusion of Clinical, Radiomic and Deep Learning Features for Preoperative Differentiation of Solitary Pulmonary Mucinous Adenocarcinoma

Objectives: To develop and validate a hybrid deep learning–machine learning (DL-ML) fusion model for noninvasive preoperative differentiation of solitary pulmonary mucinous adenocarcinoma (SPMA). Methods: A total of 200 patients with pathologically confirmed lung adenocarcinoma, including 37 SPMA cases, treated at Tianjin Union Medical Center between 2018 and 2025 were retrospectively enrolled. Patients were randomly assigned to a training cohort (n = 140) and a test cohort (n = 60). Clinical characteristics, radiomic features, and deep learning features extracted via ResNet50 were integrated. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and multiple machine learning classifiers were compared. Model performance was assessed using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve analysis (CCA). Results: LASSO regression identified 10 optimal features, comprising 8 radiomic and 2 deep learning features. The random forest classifier yielded the best performance. The hybrid DL-ML fusion model yielded the highest AUC of 0.982 in the training cohort and 0.878 in the test cohort, significantly outperforming the clinical model (Clinic), radiomic model (Rad), deep transfer learning (DTL) model, deep learning–radiomics (DLR) model. In the test cohort, the hybrid DL-ML fusion model achieved an AUC of 0.878, which was significantly higher than that of the clinical model (0.755; p < 0.05). DCA and CCA confirmed favorable clinical utility and calibration. Conclusions: The hybrid DL-ML fusion model enables accurate, noninvasive preoperative differentiation of SPMA. It outperforms conventional clinical assessment and single-modality imaging models, with promising potential for noninvasive preoperative differential diagnosis.