Zhang Qiyi, Lin Yuxiang, Lin Duo, Lin Xueliang, Liu Miaomiao, Tao Hong, Wu Jinxun, Wang Tingyin, Wang Chuan, Feng Shangyuan
Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China.
Talanta. 2024 Aug 1;275:126136. doi: 10.1016/j.talanta.2024.126136. Epub 2024 Apr 27.
Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.
早期检测乳腺癌及其分子亚型对于指导临床治疗和提高生存率至关重要。目前乳腺癌的诊断方法具有侵入性、耗时且复杂。在这项工作中，开发了一种将表面增强拉曼光谱（SERS）技术与特征选择和深度学习算法相结合的光学检测方法，用于识别血清成分并建立诊断模型，旨在高效、准确地对乳腺癌进行无创筛查。首先，获得了来自乳腺癌（BC）、乳腺良性疾病（BBD）患者和健康对照（HC）的高质量血清SERS光谱。进行卡方检验以排除混杂因素，提高研究的可靠性。然后，使用LightGBM（LGB）算法作为基础模型来保留有用特征，显著提高分类性能。通过反向传播训练DNN算法，调整神经元之间的权重和偏差以提高网络的预测能力。与传统机器学习算法相比，该方法为乳腺癌分类提供了更准确的信息，BC和BBD的分类准确率为91.38%，BC、BBD和HC的分类准确率为96.40%。此外，在评估BC患者的分子亚型时，HR+/HR-的准确率可达90.11%，HER2+/HER2-的准确率可达88.89%。这些结果表明，血清SERS与强大的LGB-DNN算法相结合将为临床乳腺癌筛查提供一种补充方法。
