CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors

INTRODUCTION: To explore the prognostic value of CT-based delta radiomics in predicting the prognosis of patients with stage IV gastric cancer treated with immune checkpoint inhibitors (ICI). MATERIALS AND METHODS: Forty-two patients with stage IV gastric cancer, who had received ICI monotherapy, were enrolled in this retrospective study. Baseline and first follow-up CT scans were analyzed. Intratumoral and peritumoral regions of interest (ROI) were contoured, enabling the extraction of 192 features from each ROI. The intraclass correlation coefficients were used to select features with high stability. The least absolute shrinkage and selection operator was used to select features with high weights for predicting patient prognosis. Kaplan–Meier analysis and log-rank test were performed to explore the association between features and progression free survival (PFS). Cox regression analyses were used to identify predictors for PFS. The C-index was used to assess the prediction performance of features. RESULTS: Two radiomics features of ΔVintra_ZV and postVperi_Sphericity were identified from intratumoral and peritumoral regions, respectively. The Kaplan–Meier analysis revealed significant differences in PFS between patients with low and high feature value (ΔVintra_ZV: P=0.000; postVperi_Sphericity: P=0.012), and the multivariable cox analysis demonstrated that ΔVintra_ZV was independent predictor for PFS (HR, 1.911; 95% CI: 1.163–3.142; P=0.011), with C-index of 0.705. CONCLUSIONS: Based on CT scans at baseline and first follow-up, the delta radiomics features could efficiently predict the PFS of gastric cancer patients treated with ICI therapy.