Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer-Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.
Keywords: deep learning; lymph node metastasis; nomogram; radiomics; ultrasound.