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| Research on the Diagnosis of Breast Cancer Molecular Subtypes Based on Convolutional Neural Networks with Multimodal Ultrasound Features |
| DENG Lizhen, WU Li |
| The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha 410005 |
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Abstract Objective This study aimed to investigate the application of convolutional neural networks (CNN) based on multimodal ultrasound features in the diagnosis of molecular subtypes of breast cancer, and to assess its potential in improving diagnostic performance. Methods By integrating features from conventional ultrasound, strain elastography, and shear wave elastography, CNN models were built using different combinations of ultrasound modalities. The CNN models were trained and validated through 5-fold cross-validation, and model performance was assessed using accuracy, recall, F1 score, and area under the curve (AUC) metrics, along with subtype prediction confusion matrix analysis. Results A total of 90 cases were collected. The CNN model based on three types of ultrasound modalities achieved satisfactory performance in terms of accuracy, recall, F1 score, and AUC indicators for the classification of molecular subtypes of breast cancer (0.914, 0.923, 0.91, and 0.942, respectively), which was significantly higher than the CNN models based on a single ultrasound modality or any two combined ultrasound modalities. Confusion matrix analysis showed that the CNN model based on three types of ultrasound modalities had the highest predictive accuracy for each subtype. Conclusion The CNN model based on multimodal ultrasound features has great potential in the diagnosis of molecular subtypes of breast cancer and can provide strong support for the precise diagnosis and treatment of breast cancer.
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Received: 26 July 2023
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2024, Vol. 21 
