Diagnostic Performance of Machine Learning Models Based on (18)F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

OBJECTIVES: This study aimed to evaluate the ability of (18)fluorine-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). METHODS: Data of 48 patients with SPN detected on (18)F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC). RESULTS: The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). CONCLUSION: Machine learning based on (18)F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.