Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k1= 2845 cm−1and k2= 2930 cm−1. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images.
受激拉曼组织学（SRH）利用组织样本中生物分子对光子的受激拉曼散射效应生成组织学图像，通过后续病理分析可在无需切片染色的情况下实现术中评估。本研究旨在探讨基于深度学习技术对口腔鳞状细胞癌（OSCC）进行分类及非恶性组织亚型分型的可行性，并比较该分类器在SRS图像与SRH图像中的性能表现。实验在波数k1=2845 cm−1和k2=2930 cm−1处测量拉曼位移，将SRS图像转换为形似传统H&E染色冰冻切片的SRH图像。通过对8例OSCC患者80份组织样本的图像进行6种组织类型的标注，基于VGG19架构的卷积神经网络在64幅SRS图像（及对应SRH图像）上完成训练，并在16幅图像上进行测试。结果显示：间质组织的平衡准确率为0.90（SRH图像为0.87）、F1分数0.91（SRH图像0.91）；脂肪组织0.98（0.96）；鳞状上皮0.90（0.87）；肌肉组织0.92（0.76）；腺体组织0.87（0.90）；肿瘤组织0.88（0.87）。本研究证实了深度学习技术可直接应用于SRS与SRH图像的术中组织类型识别。
AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology
肿瘤(癌症)患者之家