Objectives: Breast cancer is one of the most common malignant tumors among women worldwide, and accurate assessment of axillary lymph node metastasis (ALNM) is crucial for determining treatment strategies. Compared to conventional ultrasound, contrast-enhanced ultrasound (CEUS) can observe blood perfusion and microcirculation changes in primary breast tumors, making it a more ideal diagnostic method for ALNM. Methods: To address the issues that CEUS video sequences require a high level of diagnostic experience from clinicians, and the process is time-consuming and labor-intensive, making it challenging to generate large datasets for deep learning models, we proposed a method for predicting breast cancer ALNM that combines pre-trained fine-tuning with contrastive learning. First, within a text-video contrastive learning framework, we fine-tuned pre-trained weights from a large general dataset using a small-scale proprietary dataset. Second, during the fine-tuning phase, we employed random prompt optimization to specifically adjust the text encoder according to the characteristics of breast CEUS videos, and optimized the extracted text and video representations through an adaptive fine-tuning optimizer to better fit the current data distribution. Results: Experimental results demonstrated that our method achieved a sensitivity of 0.792 and a specificity of 0.8. Conclusions: The study demonstrates that the proposed method effectively leverages CEUS to aid in ALNM diagnosis, highlighting its potential to improve the accuracy of early breast cancer screening and to facilitate the development of more personalized treatment plans for patients.
Huang et al. (Mon,) studied this question.
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