Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-μm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 μm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm⁻¹ (Phenylalanine), 869 cm⁻¹ (Proline, CC stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm⁻¹ (CH₃/CH₂ twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery.

手术切除和了解肿瘤发展过程需要更好地了解乳腺肿瘤微环境。拉曼光谱是一种很有前途的工具，可以帮助揭示疾病的分子基础，并为诊断和治疗评估提供可量化的分子信息。在这项工作中，使用 250 μm 方形测量网格完整绘制了 88 个冷冻乳腺组织切片，包括 44 个正常切片和 44 个肿瘤切片。每个组织内的两个或多个较小的感兴趣区域还使用 25 μm 平方步长进行映射。开发了一种深度学习算法——卷积神经网络（CNN）来区分个体内和多个组织切片的组织病理学特征。癌性乳腺组织与正常乳腺组织的区分准确度为 90%，敏感性为 88.8%，特异性为 90.8%，接受者操作曲线下面积 (AUROC) 为 0.96。对模型有重大贡献的特征被识别出来并用于生成组织切片的 RGB 图像。对于拉曼图上的每个网格点（像素），颜色被分配给频率为 1002 cm ^-1 （苯丙氨酸）、869 cm ^-1 （脯氨酸、C 羟脯氨酸-胶原指定的C伸缩，氨基酸脯氨酸、缬氨酸和多糖的单键伸缩振动和1309cm ^-1 (脂质的CH ₃ /CH ₂扭转或弯曲模式)。拉曼图像清楚地与苏木精和伊红染色的组织切片相关联，并允许正常脂肪、结缔组织和肿瘤之间的边界清晰可见。 我们证明了这种简单的成像技术可以对拉曼图像进行高分辨率、简单的分子解释。拉曼光谱可快速、无标记地对微观特征进行高精度成像。该方法可作为实验室工具应用，并可协助保乳手术期间的术中组织评估。