To construct an individualized chemotherapy response scoring system using artificial intelligence technology to provide decision support for precision treatment of gastric cancer patients.

A retrospective analysis was performed on 11 478 patients diagnosed with gastric cancer and treated with radical surgery in the SEER database between 2000 and 2021. The DeepSurv neural network model was used to integrate the clinicopathological characteristics of patients and establish a prognostic prediction model. Survival analysis was applied to evaluate the predictive performance of the model, and a permutation-based method was used to quantify the importance of each input feature for the model’s predictive results.

Patients with high model risk scores showed significantly better prognosis than those with low scores (HR=6.19, 95% CI: 5.83-6.58, Log-Rank P<0.01). The patient group following the model’s chemotherapy recommendations (n=7 367) had significantly better prognosis than those not following the recommendations (n=4 111) (HR=0.46, 95% CI: 0.44-0.49, Log-Rank P<0.01). Variable importance analysis showed that the proportion of positive lymph nodes, T stage, and age were the three most important factors affecting prognosis and chemotherapy recommendations. The proportion of chemotherapy recommended by the model increased with the progression of tumor stage, while it also increased with patient age.

The DeepSurv model constructed based on the SEER database can not only accurately predict the prognosis of gastric cancer patients but also provide valuable guidance for precision chemotherapy decision-making.