Development of a 18F-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer

Abstract

Background Axillary lymph node metastasis (ALNM) status is an important factor for the determination of the therapeutic strategies and breast cancer prognosis. In our study, we investigate whether radiomics features from 18F-fluorodeoxyglucose(18F-FDG) positron emission tomography /computed tomography (PET/CT), combined with clinical or pathological characteristics, provide a higher predictive value of ALNM. Methods A retrospective analysis was performed on 78 female patients who underwent preoperative 18F-FDG PET/CT scans at Jinhua Central Hospital from August 2015 to July 2024, with a mean age of 53.60 ± 12.49 years (range: 35–84 years). The cases were randomly divided into a training cohort (46 cases) and a testing cohort (32 cases) in a 6:4 ratio. All patients' PET/CT and clinical pathological features were analyzed, and radiomics features were extracted from the PET/CT images. Subsequently, we developed radiomics, clinical, and combined radiomics-clinical models. We also assessed the performance of these three models in predicting ALNM. The Python stats models package (version 0.13.2) was used for statistical analysis. Results For the three features radiomics model and combined model in the training cohort, the area under the curve (AUC) was 0.922 and 0.931, which were both higher than that of the traditional clinical feature model (AUC = 0.917). The AUC values for the three models in the testing cohort were 0.802, 0.821, and 0.778. For predicting ALNM across all cohorts, the radiomics model and the combined model showed clinical benefit in the decision curve analysis (DCA). Conclusion The PET/CT-based radiomics model demonstrated strong efficacy in predicting ALNM for breast cancer and has clinical application value.