Deep learning-powered multi-parametric ultrasound for classifying metastatic versus reactive axillary lymph nodes.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 1St St. SW, Rochester, MN, 55905, USA.
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, 200 1St St. SW, Rochester, MN, 55905, USA.
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, 200 1St St. SW, Rochester, MN, 55905, USA.
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 1St St. SW, Rochester, MN, 55905, USA. [email protected].
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, 200 1St St. SW, Rochester, MN, 55905, USA. [email protected].
Abstract
To propose a multi-parametric ultrasound imaging-based deep learning method for accurately classifying metastatic and non-metastatic axillary lymph nodes in breast cancer patients. The proposed method integrates the conventional ultrasound B-mode imaging with shear wave elastography and color Doppler images of 174 patients to train a transfer learning-based network comprising pretrained MobileNetv2 with a custom shallow head consisting of a convolutional neural network with mixed pooling, weighted sum mixed pooling and squeeze-and-excite attention mechanisms for the first time in the context of ALN classification. The proposed method was evaluated using five-fold cross-validation, achieving a mean classification accuracy of 0.91, specificity of 0.91, sensitivity of 0.93, F1 score of 0.93, area under the precision-recall curve of 0.94, and a cross-validated AUC (cvAUC) of 0.92. A network ablation study confirmed the robustness of the model, with relatively narrow 95% confidence intervals (CIs) for cvAUC. Comparative analysis showed that the proposed network (Acc: 0.91) outperformed state-of-the-art deep learning models (Acc: 0.67-0.88) for ALN classification and exhibited narrower CIs, highlighting its relative stability. Additionally, results demonstrated that multi-parametric imaging significantly enhanced classification performance, reducing the 95% CI width by nearly half compared to uni-parametric data, further supporting the method's robustness and reliability. The integration of multi-parametric ultrasound imaging with deep learning network can remarkably improve the classification of metastatic and non-metastatic ALNs in breast cancer patients.