Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning

Jingrui Dou; Wubulitalifu Dawuti; Jintian Li; Hui Zhao; Run Zhou; Jing Zhou; Renyong Lin; Guodong Lü

doi:10.1016/j.talanta.2023.124457

Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning

Talanta. 2023 Jul 1:259:124457. doi: 10.1016/j.talanta.2023.124457. Epub 2023 Mar 23.

Authors

Jingrui Dou¹, Wubulitalifu Dawuti¹, Jintian Li¹, Hui Zhao², Run Zhou³, Jing Zhou³, Renyong Lin⁴, Guodong Lü⁵

Affiliations

¹ 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.
² Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
³ 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; College of Pharmacy, Xinjiang Medical University, Urumqi, 830054, China.
⁴ 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. Electronic address: renyonglin@xjmu.edu.cn.
⁵ 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. Electronic address: lgd_xj@qq.com.

PMID: 36989965
DOI: 10.1016/j.talanta.2023.124457

Abstract

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^-1) and combined (1710-1475 + 1354-980 cm^-1) 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^-1 region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.

Keywords: Cholangiocarcinoma; Fourier transform infrared spectroscopy; Gallbladder carcinoma; Hepatocellular carcinoma; Machine learning; Serum.

MeSH terms

Biomarkers
Carcinoma, Hepatocellular* / diagnosis
Gallbladder Neoplasms* / diagnosis
Humans
Liver Neoplasms*
Machine Learning
Spectroscopy, Fourier Transform Infrared / methods
Support Vector Machine

Substances

Biomarkers