Lin Runrui, Peng Bowen, Li Lintao, He Xiaoliang, Yan Huan, Tian Chao, Luo Huaichao, Yin Gang
School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
Front Oncol. 2023 Oct 26;13:1258436. doi: 10.3389/fonc.2023.1258436. eCollection 2023.
This study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals.
In this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method.
The results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups.
In conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.
本研究旨在评估使用通用拉曼光谱作为乳腺癌筛查方法的可行性。目标是开发一种机器学习模型，利用拉曼光谱检测乳腺癌患者、良性病例和健康受试者的血清样本，并以穿刺活检作为比较的金标准。目的是探索拉曼光谱在乳腺癌、良性病变和健康个体鉴别诊断中的价值。
在本研究中，共收集了333名参与者的血清样本。其中，有129例肿瘤病例（病理诊断为乳腺癌并标记为癌症），91例良性病变病例（病理诊断为良性并标记为良性），以及113例健康对照（标记为正常）。收集了每组血清样本的拉曼光谱。为了对正常、良性和癌症样本组进行分类，使用了主成分分析（PCA）结合支持向量机（SVM）。使用交叉验证方法评估SVM模型。
研究结果显示，正常组与肿瘤/良性组血清样本的平均拉曼光谱存在显著差异。尽管癌症组和良性组的平均拉曼光谱显示出轻微差异，但SVM模型在对癌症、良性和正常组进行分类时达到了高达98%的显著预测准确率。
总之，这项探索性研究证明了通用拉曼光谱作为乳腺癌临床辅助诊断和快速筛查工具的巨大潜力。
