Background: This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC).
Methods: This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction model in predicting axillary lymph node metastasis (ALNM) in the three cohorts.
Results: Deep learning radiomics combined with radiomics and clinical parameters was the optimal diagnostic predictor of the ALN status in the absence and presence of ALNM, with the AUC of 0.920 (95% confidence interval: 0.872 and 0.968, respectively). Additionally, this combination could also differentiate low-load ALNM [N + (1-2)] from heavy-load ALNM with ≥3 positive nodes [N + (≥3)] in the test cohort, with the AUC of 0.819 (95% confidence interval: 0.568 and 1.00, respectively).
Conclusions: Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.
Keywords: Deep learning radiomics; axillary lymph node metastasis (ALNM); breast cancer (BC); clinical parameters; radiomics.
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