Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization
Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization
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DOI：
10.1016/j.neunet.2021.09.006
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发表时间：
2021-09-25
期刊：
影响因子：
7.8
通讯作者：
Xu, Jian
中科院分区：
文献类型：
--
作者：
Li, Zhongqiang;Li, Zheng;Xu, Jian
胰腺癌是最致命的癌症类型，五年生存率低于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，该系统可以使用卷积神经网络（CNN）模型进行术中组织诊断，以有效区分癌性和正常胰腺组织。据我们所知，这是第一次报道的努力，诊断胰腺癌的CNN辅助自发拉曼散射与实验室开发的系统设计的术中应用。基于原始一维（1D）拉曼、二维（2D）拉曼图像以及来自2D图像的主成分分析的第一主成分（PC 1）的分类都可以实现高性能：测试灵敏度、特异性和准确性均超过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特征一直是一个挑战，这在癌症诊断的拉曼光谱应用中从未实现。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，我们确定了关键的拉曼峰，这些峰可以帮助对癌组织和非癌组织进行分类。二维拉曼PC 1比一维拉曼产生更多的胰腺癌鉴别的关键峰，因为拉曼强度被二维拉曼PC 1放大。据我们所知，在CNN辅助自发拉曼光谱用于癌症诊断领域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下的频率，发现胰腺癌组织含有更多与蛋白质含量相关的生化成分（特别是胶原蛋白），而正常胰腺组织含有更多脂质和核酸（特别是脱氧核糖核酸/核糖核酸）。总的来说，CNN模型结合拉曼光谱可以作为提取关键特征的有用工具，这些特征可以帮助区分胰腺癌和正常胰腺。(C)2021爱思唯尔有限公司版权所有。
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. (C) 2021 Elsevier Ltd. All rights reserved.