School of Pharmaceutical Sciences and Institute of Materia Medica & Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, 830017, China.
College of science, China agriculture university, Beijing, 100094, China.
Comput Methods Programs Biomed. 2024 Oct;255:108361. doi: 10.1016/j.cmpb.2024.108361. Epub 2024 Aug 3.
Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model.
Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information.
Optimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks.
Compared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions.
Raman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.
拉曼光谱已成为一种有效的非侵入性乳腺癌分析技术。然而，当前的拉曼预测模型未能涵盖所有乳腺癌的分子亚型，并且缺乏模型的可视化。
使用拉曼光谱结合卷积神经网络（CNN）构建现有的已知乳腺癌分子亚型的预测模型，并通过可视化策略选择关键峰，以达到挖掘特定生物标志物信息的目的。
利用麻雀搜索算法（SSA）优化网络参数，对 CNN 中的多个参数进行优化，提高模型的预测性能。为避免结果的偶然性，通过蒙特卡罗抽样生成多组数据来训练模型，从而提高结果的可信度。基于模型的准确预测，使用梯度加权类激活映射（Grad-CAM）对贡献分类的光谱区域进行可视化，实现特征峰的可视化目标。
与其他算法相比，优化后的 CNN 可以获得最高的准确性和最低的标准误差。并且使用全谱和指纹区域（在 2%以内）的结果没有显著差异，这表明指纹区域在分类亚型方面提供了最大的贡献。基于指纹区域的分类结果，各种亚型的模型性能如下：CNN（95.34%±2.18%）>SVM（94.90%±1.88%）>PLS-DA（94.52%±2.22%）>KNN（80.00%±5.27%）。Grad-CAM 可视化的关键特征与 IHC 信息吻合良好，允许在空间位置上更明显地区分亚型。
拉曼光谱结合 CNN 可实现乳腺癌分子亚型的准确快速识别。所提出的可视化策略可以从生物化学信息和空间位置上得到证明，表明该策略可用于未来生物标志物的挖掘。
