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Prognostic value of combined lymphocyte-to-monocyte ratio and cancer antigen 724 in patients with proximal gastric cancer residing in extremely cold regions

Xiqing Zhu, Dali Li, Shanshan Liang, Huaxing Wu, Haibin Song

2025, 5(3): 170-179. doi: 10.1515/fzm-2025-0020

Keywords: proximal gastric cancer, cold regions, lymphocyte-to-monocyte ratio, carbohydrate antigen 724, prognostic evaluation

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Background This study aimed to evaluate the prognostic value of the lymphocyte-to-monocyte ratio (LMR) and cancer antigen 724 (CA724) in patients with proximal gastric cancer residing in cold climate regions. Methods A retrospective analysis was conducted on 313 patients diagnosed with proximal gastric cancer in cold climate regions between 2014 and 2017. Preoperative hematological markers, including LMR and CA724, were assessed. Receiver operating characteristic (ROC) curves were used to determine optimal cutoff values, which were then combined to form the LMR + CA724 score. Statistical analyses included Kaplan-Meier survival curves, log-rank tests, and Cox proportional hazards regression. Results A high preoperative LMR + CA724 score was significantly associated with older age, advanced pTNM stage, vascular invasion, and elevated levels of NMPVR, NMR, and AAR. The LMR + CA724 score demonstrated a higher area under the curve (AUC) compared to LMR or CA724 alone. Multivariate analysis identified pTNM stage, Borrmann type, histological type, and the LMR + CA724 score as independent prognostic factors for overall survival (OS). A nomogram incorporating these four variables achieved an AUC of 0.817, indicating strong predictive performance. Conclusion The LMR + CA724 score is a reliable and independent prognostic indicator for patients with proximal gastric cancer in cold climate regions. Its integration into clinical practice may support treatment planning and long-term management by enhancing personalized care. Further prospective studies are warranted to validate these findings in broader and more diverse patient populations.

Machine learning-based prediction of 5-year survival in diffuse-type gastric cancer patients from Harbin

Yongle Zhang, Cong Wang, Jiale Fan, Hongyu Gao, Xiqing Zhu, Haibin Song

2026, 6(1): 40-48. doi: 10.1515/fzm-2026-0004

Keywords: gastric cancer, XGBoost, cold region

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Objective Globally, over 1.1 million new cases of gastric cancer (GC) were diagnosed in 2020, with approximately 800, 000 related deaths. GC exhibits significant regional variability, particularly in extremely cold regions, where unique climate conditions and lifestyle factors may impact disease progression and prognosis. This study aimed to predict the 5-year all-cause mortality of patients with diffuse gastric cancer (DGC) living in such regions using multiple machine learning algorithms. Methods We retrospectively analyzed 249 DGC cases and developed six machine learning models—extreme gradient boosting (XGBoost), logistic regression, decision tree, support vector machine, k-nearest neighbors, and random forest. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), precision-recall curve, F1 score, and Brier score. Results The XGBoost model achieved the highest F1 scores (0.830 and 0.781, respectively) and the second-best Brier score (0.172). Conclusion This study highlights the potential of machine learning approaches to enhance prognostic assessment in GC. Although limited by single-center data and the absence of multi-center external validation, the results offer valuable insights that may inform future research and guide risk-stratified management strategies in extremely cold regions.

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