Zhang Ya, Li Zheng, Li Zhongqiang, Wang Huaizhi, Regmi Dinkar, Zhang Jian, Feng Jiming, Yao Shaomian, Xu Jian
Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Biol Proced Online. 2024 Sep 12;26(1):28. doi: 10.1186/s12575-024-00255-0.
Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.
Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.
Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.
乳腺癌对全球女性的健康构成重大风险，在美国，每年约有30%的女性被诊断出患有乳腺癌。手术过程中区分癌性乳腺组织和非癌性乳腺组织对于肿瘤的彻底切除至关重要。
我们的研究创新性地将机器学习技术（随机森林（RF）、支持向量机（SVM）和卷积神经网络（CNN））与拉曼光谱相结合，以简化和加速小鼠正常和晚期癌性乳腺组织的区分。这些模型的分类准确率分别为：随机森林94.47%、支持向量机96.76%、卷积神经网络97.58%。据我们所知，本研究是首次比较这三种机器学习技术基于拉曼光谱对乳腺癌组织进行分类的有效性。此外，我们创新性地识别出了有助于小鼠癌性和非癌性组织分子特征的特定光谱峰。
因此，我们的机器学习与拉曼光谱相结合的方法为乳腺癌提供了一种非侵入性、快速的诊断工具，在术中应用方面具有广阔前景。
