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Multicenter Study
. 2024 Jun 10;24(1):711.
doi: 10.1186/s12885-024-12483-4.

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

Shaobo Zhang  1 , Hongxia Xu  2 , Wei Li  3 , Jiuwei Cui  3 , Qingchuan Zhao  4 , Zengqing Guo  5 , Junqiang Chen  6 , Qinghua Yao  7 , Suyi Li  8 , Ying He  9 , Qiuge Qiao  10 , Yongdong Feng  11 , Hanping Shi  12   13   14 , Chunhua Song  15   16   17
Affiliations
Multicenter Study

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

Shaobo Zhang et al. BMC Cancer. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Gastric cancer; Inflammatory biomarkers; Machine learning; Overall survival; Prognosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow of patients inclusion
Fig. 2
Fig. 2
Model performance in the data (AUCs were compared using DeLong’s test). (A) Performance of the full ML models. (B) Performance of the simplified ML models after feature selection. (C) Comparison of the ADA and RF in training set. (D) Comparison of the ADA and RF in validation set. (E) Comparison of the full ADA and simplified ADA
Fig. 3
Fig. 3
The nomogram for overall survival prediction in GC patients
Fig. 4
Fig. 4
Nomogram Model performance in total patients (AUCs were compared using DeLong’s test). (A) Comparison of 1-year prognostic ROC for Risk Score calculated by Nomogram Model, Cox Model and TNM Model in total patients. (B) Comparison of 3-year prognostic ROC for Risk Score calculated by Nomogram Model, Cox Model and TNM Model in total patients. (C) Comparison of 5-year prognostic ROC for Risk Score calculated by Nomogram Model, Cox Model and TNM Model in total patients. (D) The Kaplan-Meier survival curves of Risk Score calculated by Cox Model in total patients
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References

    1. Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP. The Eighth Edition AJCC Cancer staging Manual: continuing to build a bridge from a population-based to a more personalized approach to cancer staging. CA Cancer J Clin. 2017;67(2):93–9. doi: 10.3322/caac.21388. - DOI - PubMed
    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. doi: 10.3322/caac.21660. - DOI - PubMed
    1. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48. doi: 10.1016/S0140-6736(20)31288-5. - DOI - PubMed
    1. Karimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev. 2014;23(5):700–13. doi: 10.1158/1055-9965.EPI-13-1057. - DOI - PMC - PubMed
    1. Thrift AP, El-Serag HB. Burden of gastric Cancer. Clin Gastroenterol Hepatol. 2020;18(3):534–42. doi: 10.1016/j.cgh.2019.07.045. - DOI - PMC - PubMed

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