Breast cancer early detection and molecular subtype prediction by combination of Raman spectroscopy with deep learning.

Breast cancer is one of the most common tumors in women, and early screening can significantly reduce mortality rates. Meanwhile, accurately identifying HER2-positive and HER2-negative subtypes of breast cancer is critical for helping doctors determine treatment options and prognosis strategies for patients. The goal of this study was to develop a computationally efficient, end-to-end model capable of both breast cancer detection and molecular typing prediction without the need for complex feature engineering. This study collected serum samples from 541 volunteers, including HER2-positive, HER2-negative, ductal carcinoma in situ (DCIS) patients, and healthy individuals. After sample collection, Raman spectra were obtained using a Raman spectrometer with a 532 nm excitation wavelength. Based on an efficient channel attention mechanism and convolutional neural networks, a classification model was developed to facilitate breast cancer detection and molecular subtyping. The proposed model significantly reduced the number of parameters and increased training speed. On an unknown test set, the model achieved an accuracy of 94.5 % and an AUC of 0.952, outperforming traditional models and algorithms. Specifically, the model achieved an accuracy of 98.1 % for BC, DCIS, and healthy volunteers, and 89.5 % for HER2-positive and HER2-negative cases. Additionally, the model's effectiveness was validated using different datasets, yielding satisfactory results. Raman spectroscopy, combined with our proposed attention mechanism-based convolutional neural network, effectively enables early screening and molecular subtype prediction of breast cancer. These findings offer new possibilities for rapid, non-invasive, and low-cost early screening and molecular subtype prediction of breast cancer.