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Volume 6 Issue 1
Jan.  2026
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Article Navigation >  Frigid Zone Medicine  > 2026  >  6(1): 40-48
Yongle Zhang, Cong Wang, Jiale Fan, Hongyu Gao, Xiqing Zhu, Haibin Song. Machine learning-based prediction of 5-year survival in diffuse-type gastric cancer patients from Harbin[J]. Frigid Zone Medicine, 2026, 6(1): 40-48. doi: 10.1515/fzm-2026-0004
Citation: Yongle Zhang, Cong Wang, Jiale Fan, Hongyu Gao, Xiqing Zhu, Haibin Song. Machine learning-based prediction of 5-year survival in diffuse-type gastric cancer patients from Harbin[J]. Frigid Zone Medicine, 2026, 6(1): 40-48. doi: 10.1515/fzm-2026-0004
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Machine learning-based prediction of 5-year survival in diffuse-type gastric cancer patients from Harbin

doi: 10.1515/fzm-2026-0004

  • Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150000, China

Funds:

the Fund for Independent Innovation of Hypoglycemic Drugs from Basic Research to Clinical Application 070500020373

Heilongjiang Provincial Natural Science Foundation PL2025H169

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    Abstract
  •   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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