Comparison of radiomics handcrafted features and deep features in <sup>18</sup>F-FDG PET/CT for differentiating benign and malignant persistent pulmonary ground-glass nodules.
Authors
Affiliations (6)
Affiliations (6)
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.
- Clinical Translational Institute for Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, 213003, China.
- Department of Nuclear Medicine, the Third Affiliated Hospital of Nanjing Medical University, Changzhou, 213003, China. [email protected].
- Department of Nuclear Medicine, Changzhou Cancer Hospital, Changzhou, 213003, China. [email protected].
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. [email protected].
- Clinical Translational Institute for Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, 213003, China. [email protected].
Abstract
The differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative value of radiomics handcrafted features and deep features derived from <sup>18</sup>F-FDG PET/CT in this setting requires further comparison. This study aimed to develop and validate diagnostic models using radiomics handcrafted features and deep features extracted from <sup>18</sup>F-FDG PET/CT for differentiating benign and malignant persistent pulmonary GGNs. Data from 173 patients (184 GGNs) across three PET/CT centers were retrospectively analyzed. Patients underwent <sup>18</sup>F-FDG PET/CT and breath-hold chest CT, with diagnoses confirmed by pathology or follow-up. Models were developed using clinical features, handcrafted features, and deep features extracted via pretrained convolutional neural networks (VGG19 and ResNet50). The SUTAH dataset was used for model training and validation, and CZ2PH and CZCH datasets were used as external test sets. Single-modality and dual-modality diagnostic models were developed based on clinical/conventional imaging features, radiomics handcrafted features, and deep features. Deep features were extracted from PET and CT images using the pretrained convolutional neural networks VGG19 and ResNet50. Model performance was evaluated using the AUC and its 95% CI, while decision curve analysis (DCA) and the Brier score were used to assess net benefit and probability prediction error, respectively. In the internal validation set, the CT_HF and PET_HF models achieved AUCs of 0.889 (95% CI: 0.756-0.964) and 0.903 (95% CI: 0.774-0.972), respectively, which were numerically higher than that of the reference model (AUC = 0.864, 95% CI: 0.725-0.949). In the external test set, the PET_ResNet50/CT_VGG19 combined model achieved the highest AUC of 0.927 (95% CI: 0.802-0.984), compared with 0.824 (95% CI: 0.676-0.924) for the reference model and 0.884 (95% CI: 0.747-0.962) for PET_ResNet50; however, the differences among the three models were not statistically significant (P = 0.101-0.464). DCA showed that this combined model had a relatively high net benefit when the threshold probability exceeded 0.5, and its Brier score in the external test set was 0.122, which was comparable to that of the reference model. The integrated ¹⁸F-FDG PET/CT model with PET_ResNet50 and CT_VGG19 deep features achieved favorable discrimination and modest net clinical benefit in the external test set. However, its probability calibration and generalizability await validation on larger multicenter datasets due to insufficient benign nodules in the external test set.