Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Feb 15;287(Pt 1):121990. doi: 10.1016/j.saa.2022.121990. Epub 2022 Oct 22.
Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.
近红外（NIR）光谱具有深穿透能力，可以根据 X-H 基团的振动，快速、特异性地对生物组织的成分进行特征化。深度学习已被证明有助于快速、自动识别组织癌变。在这项研究中，使用自制的近红外探头进行了近红外光谱检测，以原位探索乳腺癌化过程中分子成分的变化，其中在癌变和癌旁区域的不同位置高效地采集了漫射近红外光谱。基于一维卷积神经网络（1D-CNN）建立了乳腺癌-癌旁区分模型。通过优化神经网络的结构，实现了 94.67%的高分类准确率、95.33%的召回率/灵敏度、94.00%的特异性、94.08%的精度和 0.9470 的 F1 分数，显示出比 K-最近邻（KNN，88.34%、98.21%、76.11%、83.59%、0.9031）和 Fisher 判别分析（FDA，90.00%、96.43%、81.82%、87.10%、0.9153）方法更好的区分能力和可靠性。实验结果表明，1D-CNN 的应用可以区分癌性和癌旁性乳腺组织，为临床定位、诊断和治疗乳腺癌提供了一种智能方法。
