Use of machine learning with two-dimensional synthetic mammography for axillary lymph node metastasis prediction in breast cancer: a preliminary study

BACKGROUND: As of 2020, breast cancer is the most common type of cancer and the fifth most common cause of cancer-related deaths worldwide. The non-invasive prediction of axillary lymph node (ALN) metastasis using two-dimensional synthetic mammography (SM) generated from digital breast tomosynthesis (DBT) could help mitigate complications related to sentinel lymph node biopsy or dissection. Thus, this study aimed to investigate the possibility of predicting ALN metastasis using radiomic analysis of SM images. METHODS: Seventy-seven patients diagnosed with breast cancer using full-field digital mammography (FFDM) and DBT were included in the study. Radiomic features were calculated using segmented mass lesions. The ALN prediction models were constructed based on a logistic regression model. Parameters such as the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: The FFDM model yielded an AUC value of 0.738 [95% confidence interval (CI): 0.608–0.867], with sensitivity, specificity, PPV, and NPV of 0.826, 0.630, 0.488, and 0.894, respectively. The SM model yielded an AUC value of 0.742 (95% CI: 0.613–0.871), with sensitivity, specificity, PPV, and NPV of 0.783, 0.630, 0.474, and 0.871, respectively. No significant differences were observed between the two models. CONCLUSIONS: The ALN prediction model using radiomic features extracted from SM images demonstrated the possibility of enhancing the accuracy of diagnostic imaging when utilised together with traditional imaging techniques.