Objective This study aimed to assess the diagnostic value of 18 F-FDG PET/CT radiomics in distinguishing adenocarcinoma from inflammatory lesions in pulmonary solitary solid nodules (solid pulmonary nodules). Methods A total of 222 patients with Solid pulmonary nodules were retrospectively analyzed and randomly divided into two groups: a training set ( n = 155) and a validation set ( n = 67). Radiomic features were extracted from positron emission tomography/computed tomography (PET/CT) images, and optimal features were selected from the training set. Three model groups were created (CT, PET, and PET+CT) using six machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC). Results A total of eleven, nine, and fourteen optimal features were identified for the CT, PET, and PET+CT groups, respectively. In the validation set, the Area Under the Curve (AUC) values for the CT models ranged from 0.731 to 0.831, for the PET models from 0.746 to 0.810, and for the PET+CT models from 0.800 to 0.847. Among these, the PET+CT model developed using the Random Forest (RF) classifier demonstrated the best diagnostic performance, with an AUC of 0.847, sensitivity of 0.804, and specificity of 0.821. Decision curve analysis (DCA) confirmed that the model has favorable clinical utility, while calibration curves showed a good agreement between predicted and observed outcomes. Conclusion The PET+CT radiomics models outperformed the single-modality models in distinguishing Solid pulmonary nodules adenocarcinoma from inflammatory lesions. Overall, the RF-based PET+CT model achieved the highest diagnostic efficacy and indicates promising potential for clinical application.
Liao et al. (Mon,) studied this question.