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28 August 2023 Wavelet transform and deep learning for breast cancer neoadjuvant chemotherapy efficacy prediction
Hanguang Xiao, Xiuhong Zhu, Yujia Wei, Huanqi Li, Qingling Xia
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Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272411 (2023) https://doi.org/10.1117/12.2687486
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China
Abstract
The purpose of this study was to investigate the value of wavelet features in machine learning radiomics and deep learning for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. We retrospectively analyzed 199 BC patients from the Duke-Breast-Cancer-MRI dataset. First, five machine learning classification models were constructed based on the combination of texture features and wavelet features extracted from the tumor mask region for the prediction of NAC response. Then, a deep learning model based on wavelet transform (WCNN) with a classical backbone of VGG16 was proposed to effectively predict the pCR of NAC using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans taken before NAC treatment. The prediction performance was evaluated by using the area under the receiver operating characteristic (ROC) curve (AUC). Results show that both radiomics and deep learning models with wavelet features perform much better than the models without wavelet features. In addition, the proposed WCNN model outperforms all machine learning-based radiomics and deep learning models built in this study, which indicates that the wavelet-based deep learning model has great potential to provide information for breast cancer patients planning to undergo NAC treatment.
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Hanguang Xiao, Xiuhong Zhu, Yujia Wei, Huanqi Li, and Qingling Xia "Wavelet transform and deep learning for breast cancer neoadjuvant chemotherapy efficacy prediction", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272411 (28 August 2023); https://doi.org/10.1117/12.2687486
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KEYWORDS
Deep learning
Breast cancer
Machine learning
Tumors
Magnetic resonance imaging
Wavelet transforms
Radiomics
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