Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China

• Published in: BMC cancer Vol. 24; no. 1; pp. 711 - 13
• Main Authors: Zhang, Shaobo, Xu, Hongxia, Li, Wei, Cui, Jiuwei, Zhao, Qingchuan, Guo, Zengqing, Chen, Junqiang, Yao, Qinghua, Li, Suyi, He, Ying, Qiao, Qiuge, Feng, Yongdong, Shi, Hanping, Song, Chunhua
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 10.06.2024; BioMed Central; BMC
• Subjects: Adult; Aged; Algorithms; Analysis; Biochemistry; Biological markers; Biomarkers, Tumor; Blood platelets; Blood tests; Cancer; Cancer patients; China - epidemiology; Clinical trials; Cohort analysis; Cohort Studies; Committees; Electronic health records; Evaluation; Female; Gastric cancer; Health aspects; Humans; Inflammation; Inflammatory biomarkers; Kaplan-Meier Estimate; Learning algorithms; Leukocytes; Lifestyles; Lymphocytes; Machine Learning; Male; Medical prognosis; Medical records; Middle Aged; Mortality; Neoplasm Staging; Neutrophils; Nomograms; Oncology, Experimental; Overall survival; Patients; Physiological aspects; Prognosis; Proportional Hazards Models; Quality of life; Questionnaires; Stomach cancer; Stomach Neoplasms - diagnosis; Stomach Neoplasms - mortality; Stomach Neoplasms - pathology; Survival; Variables; China; East Asia; Inflammatory biomarkers; Prognosis; Overall survival; Gastric cancer; Machine learning
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-12483-4

Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.