School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
The first hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, Gansu, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Feb 15;287(Pt 1):122049. doi: 10.1016/j.saa.2022.122049. Epub 2022 Oct 28.
Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm, 1004 cm, 1449 cm, 1089-1128 cm, 1311-1367 cm and 1585-1665 cm, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.
胃癌，以胃腺癌（GAC）最为常见的组织学类型，导致相当多的死亡。为了提高 GAC 治疗后的生存率，开发一种用于 GAC 的早期检测和治疗支持的方法很重要。拉曼光谱由于其实时、无损测量，无需任何额外的试剂，是一种探测癌细胞的潜在工具。在这项研究中，我们使用拉曼光谱来检查 GAC 样本，并区分癌性胃黏膜和正常胃黏膜。两组的平均拉曼光谱在 750cm、1004cm、1449cm、1089-1128cm、1311-1367cm 和 1585-1665cm 处存在差异，这些峰分别分配给细胞色素 c、苯丙氨酸、磷脂、胶原、脂质和不饱和脂肪酸。此外，我们构建了 SENet-LSTM 模型，实现了癌性胃黏膜和正常胃黏膜的自动分类，所有预处理的拉曼光谱在 400-1800cm 范围内作为输入。实现了 96.20%的准确率。此外，通过使用掩蔽方法，我们发现了决定分类的拉曼光谱特征，并探索了分类模型的可解释性。结果与平均光谱的结论一致。所有结果都表明，结合拉曼光谱和机器学习进行癌前筛查具有潜力。
