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

Jiazheng Li; Zifan Chen; Yang Chen; Jie Zhao; Meng He; Xiaoting Li; Li Zhang; Bin Dong; Xiaotian Zhang; Lei Tang; Lin Shen

doi:10.3389/fonc.2022.1059874

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

Front Oncol. 2023 Jan 4:12:1059874. doi: 10.3389/fonc.2022.1059874. eCollection 2022.

Authors

Jiazheng Li¹, Zifan Chen², Yang Chen³, Jie Zhao⁴, Meng He¹, Xiaoting Li¹, Li Zhang², Bin Dong⁵, Xiaotian Zhang³, Lei Tang¹, Lin Shen³

Affiliations

¹ Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
² Center for Data Science, Peking University, Beijing, China.
³ Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
⁴ National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China.
⁵ Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing, China.

Abstract

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.

Keywords: computed tomography; gastric cancer; immunotherapy; prognosis; radiomics.