Oncology · 2025
Machine Learning Predictive Models for Survival in Gastric Cancer Patients with Diabetes: A Population-Based Cohort Study.
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Huang Junjie, Zhong Claire Chenwen, Li Zhaojun, Jiang Yu, Yang Zehuan, Yuan Jinqiu, Poon Jonathan, Dou Qi, Wong Martin C S
Аннотация
INTRODUCTION: Our study aimed to identify risk factors associated with the survival of gastric cancer patients with type 2 diabetes mellitus (T2DM) and create a risk-scoring system for predicting their survival probabilities.
METHODS: We gathered data from 1,912 individuals with both gastric cancer and T2DM from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL), spanning from 2000 to 2020. We used conventional Cox proportional hazards regression and tree-based machine learning algorithms to construct models for prognosis risk prediction. In the best-performing model, risk factors were identified using SHAP (Shapley Additive Explanations) analysis, and the AutoScore-Survival package was used to develop a risk-scoring system.
RESULTS: Our findings indicate that older age at cancer diagnosis, longer duration of T2DM, higher body mass index (BMI), central obesity, lower levels of high-density lipoprotein cholesterol, and reduced serum potassium are associated with poorer prognosis for gastric cancer in patients with T2DM. The Random Survival Forests (RSF) model exhibited the best performance, achieving an AUC of 0.870 and a concordance index of 0.78. Additionally, we developed two risk-scoring systems using predefined and tuned models, which yielded C-indices of 0.672 and 0.654, respectively, in the test set.
CONCLUSION: This study enhances our understanding of gastric cancer prognosis in patients with T2DM by identifying significant risk factors and developing risk-scoring systems. Further research is needed to elucidate the underlying mechanisms of these risk factors and to validate the risk-scoring systems in clinical settings.
METHODS: We gathered data from 1,912 individuals with both gastric cancer and T2DM from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL), spanning from 2000 to 2020. We used conventional Cox proportional hazards regression and tree-based machine learning algorithms to construct models for prognosis risk prediction. In the best-performing model, risk factors were identified using SHAP (Shapley Additive Explanations) analysis, and the AutoScore-Survival package was used to develop a risk-scoring system.
RESULTS: Our findings indicate that older age at cancer diagnosis, longer duration of T2DM, higher body mass index (BMI), central obesity, lower levels of high-density lipoprotein cholesterol, and reduced serum potassium are associated with poorer prognosis for gastric cancer in patients with T2DM. The Random Survival Forests (RSF) model exhibited the best performance, achieving an AUC of 0.870 and a concordance index of 0.78. Additionally, we developed two risk-scoring systems using predefined and tuned models, which yielded C-indices of 0.672 and 0.654, respectively, in the test set.
CONCLUSION: This study enhances our understanding of gastric cancer prognosis in patients with T2DM by identifying significant risk factors and developing risk-scoring systems. Further research is needed to elucidate the underlying mechanisms of these risk factors and to validate the risk-scoring systems in clinical settings.