College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830054, China.; Xinjiang Key Laboratory of Medical Animal Model Research, Urumqi, 830054, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Apr 15;291:122339. doi: 10.1016/j.saa.2023.122339. Epub 2023 Jan 12.
Cervical cancer is one of the most common cancers with a long latent period and slow onset process. Early and accurate identification of the stage of cervical cancer can significantly improve the cure rate and patient survival time. In this study, we collected 699 Raman spectral data of tissue sections from 233 different patients. We analyzed and compared the characteristics and differences of the mean Raman spectra of the seven tissues and pointed out the main differences in the biochemical composition of the seven tissues. In this study, 1D hierarchical convolutional neural network (H-CNN) is proposed by integrating the prior knowledge of hierarchical classification relations with the research of deep learning in Raman spectroscopy. H-CNN is based on CNN and is added with three network branches. Hierarchical classification is performed from coarse to fine for tissue samples of cervicitis, Low-grade Squamous Cell Carcinoma, High-grade Squamous Cell Carcinoma, Well Differentiated Squamous Cell Carcinoma, Moderately Differentiated Squamous Cell Carcinoma, Poorly Differentiated Squamous Cell Carcinoma and cervical adenocarcinoma. To evaluate the recognition performance of H-CNN, we compared it with traditional methods such as Bayesian classifier (NB), decision tree classifier (DT), support vector machine classifier (SVM) and CNN. The experimental results show that H-CNN can accurately identify different classes of tissue sections and has apparent advantages in several aspects such as recognition accuracy, stability and sensitivity compared with the other four traditional recognition methods. The classification Macro-Accuracy of H-CNN can reach 94.91%, Macro-Recall can reach 95.31%, Macro-F1 can reach 95.23%, and Macro-AUC can reach 97.35%. The hierarchical classification method proposed in this study can diagnose patients more accurately. This could lay the foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.
宫颈癌是一种潜伏期长、发病过程缓慢的最常见癌症之一。早期、准确地识别宫颈癌的分期可以显著提高治愈率和患者的生存时间。在本研究中，我们收集了 233 名不同患者的组织切片的 699 个拉曼光谱数据。我们分析和比较了七种组织的平均拉曼光谱的特征和差异，并指出了七种组织生化成分的主要差异。在这项研究中，我们通过将分层分类关系的先验知识与拉曼光谱深度学习研究相结合，提出了一维分层卷积神经网络（H-CNN）。H-CNN 基于 CNN，并添加了三个网络分支。从粗到细对宫颈炎、低级别鳞状细胞癌、高级别鳞状细胞癌、高分化鳞状细胞癌、中分化鳞状细胞癌、低分化鳞状细胞癌和宫颈腺癌的组织样本进行分层分类。为了评估 H-CNN 的识别性能，我们将其与贝叶斯分类器（NB）、决策树分类器（DT）、支持向量机分类器（SVM）和 CNN 等传统方法进行了比较。实验结果表明，H-CNN 可以准确地识别不同类型的组织切片，与其他四种传统识别方法相比，在识别准确率、稳定性和灵敏度等方面具有明显的优势。H-CNN 的分类宏准确率可达 94.91%，宏召回率可达 95.31%，宏 F1 值可达 95.23%，宏 AUC 可达 97.35%。本研究提出的分层分类方法可以更准确地诊断患者。这为进一步研究拉曼光谱作为宫颈癌的临床诊断方法奠定了基础。
