A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment

Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell's c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732-0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.