Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China; School of Physics, Dalian University of Technology, Dalian, 116023, People's Republic of China.
Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China; School of Physics, Dalian University of Technology, Dalian, 116023, People's Republic of China; State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Jun 5;314:124189. doi: 10.1016/j.saa.2024.124189. Epub 2024 Mar 25.
Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.
早期检测和术后评估对于提高肺癌患者的总体生存率至关重要。在这里，我们报告了一种将拉曼光谱与机器学习相结合的非侵入性技术，用于检测肺癌。该研究包括 88 名术后肺癌患者、73 名非手术肺癌患者和 68 名健康受试者。主要目的是探讨这些队列中血清代谢的变化。对平均拉曼光谱进行了比较分析，同时采用主成分分析进行数据可视化。随后，使用扩充数据集对卷积神经网络 (CNN) 和 Resnet 模型进行训练，从而开发出一个诊断框架。通过接收者操作特征曲线验证，CNN 模型表现出优越的性能。值得注意的是，术后患者复发的可能性增加，这强调了对连续术后监测的迫切需求。总之，拉曼光谱与基于 CNN 的分类相结合具有用于肺癌的早期检测和术后评估的潜力。
