Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

胰腺癌是最致命的癌症类型，五年生存率不到 9%。肿瘤边缘的检测在手术切除的成功中起着至关重要的作用。然而，组织病理学评估是耗时、昂贵和劳动密集型的。我们构建了一个实验室设计的手持式拉曼光谱系统，可以使用卷积神经网络 (CNN) 模型进行术中组织诊断，以有效区分癌变和正常胰腺组织。据我们所知，这是首次报道的通过 CNN 辅助的自发拉曼散射和实验室开发的专为术中应用而设计的系统来诊断胰腺癌的努力。基于原始一维（1D）拉曼、二维（2D）拉曼图像的分类，二维图像主成分分析的第一主成分（PC1）均能达到高性能：检测灵敏度、特异度、准确度均超过95%，曲线下面积接近0.99。尽管 CNN 模型在分类方面经常表现出巨大的成功，但在这些模型中可视化 CNN 特征一直具有挑战性，这在拉曼光谱在癌症诊断中的应用中从未实现过。通过研究单个拉曼区域，并通过从最大池化层中提取和可视化 CNN 特征，我们确定了有助于分类癌性和非癌性组织的关键拉曼峰。由于拉曼强度被 2D 拉曼 PC1 放大，因此 2D 拉曼 PC1 产生了比 1D 拉曼更重要的胰腺癌识别峰。据我们所知，在 CNN 辅助的自发拉曼光谱用于癌症诊断领域中首次实现了特征可视化。基于这些 CNN 特征峰及其在特定波数的频率，发现胰腺癌组织含有更多与蛋白质含量（特别是胶原蛋白）相关的生化成分，而正常胰腺组织被发现含有更多脂质和核酸（尤其是脱氧核糖核酸） /核糖核酸）。总体而言，CNN 模型与拉曼光谱相结合，可以作为提取关键特征的有用工具，有助于区分胰腺癌和正常胰腺。基于这些 CNN 特征峰及其在特定波数的频率，发现胰腺癌组织含有更多与蛋白质含量（特别是胶原蛋白）相关的生化成分，而正常胰腺组织被发现含有更多脂质和核酸（尤其是脱氧核糖核酸） /核糖核酸）。总体而言，CNN 模型与拉曼光谱相结合，可以作为提取关键特征的有用工具，有助于区分胰腺癌和正常胰腺。基于这些 CNN 特征峰及其在特定波数的频率，发现胰腺癌组织含有更多与蛋白质含量（特别是胶原蛋白）相关的生化成分，而正常胰腺组织被发现含有更多脂质和核酸（尤其是脱氧核糖核酸） /核糖核酸）。总体而言，CNN 模型与拉曼光谱相结合，可以作为提取关键特征的有用工具，有助于区分胰腺癌和正常胰腺。而正常胰腺组织被发现含有更多的脂质和核酸（特别是脱氧核糖核酸/核糖核酸）。总体而言，CNN 模型与拉曼光谱相结合，可以作为提取关键特征的有用工具，有助于区分胰腺癌和正常胰腺。而正常胰腺组织被发现含有更多的脂质和核酸（特别是脱氧核糖核酸/核糖核酸）。总体而言，CNN 模型与拉曼光谱相结合，可以作为提取关键特征的有用工具，有助于区分胰腺癌和正常胰腺。