Dou Jingrui, Dawuti Wubulitalifu, Li Jintian, Zhao Hui, Zhou Run, Zhou Jing, Lin Renyong, Lü Guodong
State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China; School of Public Health, Xinjiang Medical University, Urumqi, 830054, China.
Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Talanta. 2023 Jul 1;259:124457. doi: 10.1016/j.talanta.2023.124457. Epub 2023 Mar 23.
Gallbladder cancer (GBC) is the most common malignant tumour of the biliary tract. GBC is difficult to diagnose and treat at an early stage because of the lack of effective serum markers and typical symptoms, resulting in low survival rates. This study aimed to investigate the applicability of dried serum Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms to correctly differentiate patients with GBC from patients with gallbladder disease (GBD), cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC) and healthy individuals. The differentiation between healthy individuals and GBC serum was better using principal component analysis (PCA) and linear discriminant analysis (LDA) for six spectral regions, especially in the protein (1710-1475 cm) and combined (1710-1475 + 1354-980 cm) region. However, the PCA-LDA model poorly differentiated GBC from GBD, CCA, and HCC in serum spectra. We evaluated the PCA- LDA, PCA-support vector machine (SVM), and radial basis kernel function support vector machine (RBF-SVM) models for GBC diagnosis and found that the RBF-SVM model performed the best, with 88.24-95% accuracy, 95.83% sensitivity, and 78.38-94.44% specificity in the 1710-1475 + 1354-980 cm region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.
胆囊癌（GBC）是胆道最常见的恶性肿瘤。由于缺乏有效的血清标志物和典型症状，胆囊癌在早期难以诊断和治疗，导致生存率较低。本研究旨在探讨干燥血清傅里叶变换红外（FTIR）光谱结合机器学习算法对胆囊癌患者与胆囊疾病（GBD）、胆管癌（CCA）、肝细胞癌（HCC）患者及健康个体进行正确鉴别的适用性。对于六个光谱区域，使用主成分分析（PCA）和线性判别分析（LDA）能更好地区分健康个体和胆囊癌血清，尤其是在蛋白质（1710 - 1475 cm）和组合（1710 - 1475 + 1354 - 980 cm）区域。然而，PCA - LDA模型在血清光谱中难以区分胆囊癌与胆囊疾病、胆管癌和肝细胞癌。我们评估了用于胆囊癌诊断的PCA - LDA、PCA - 支持向量机（SVM）和径向基核函数支持向量机（RBF - SVM）模型，发现RBF - SVM模型表现最佳，在1710 - 1475 + 1354 - 980 cm区域的准确率为88.24 - 95%，灵敏度为95.83%，特异性为78.38 - 94.44%。本研究表明，血清FTIR光谱结合RBF - SVM算法在胆囊癌筛查方面具有巨大的临床潜力。
