A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study

Abstract

Background: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.

Methods: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data.

Results: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression.

Conclusion: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

Keywords: Gastric cancer; Immunotherapy; MSI; Radiomics signature; mRNA-seq.

Fig. 1

Patient flowchart for this study. MSI = microsatellite instability, GC = gastric cancer, PCR = polymerase chain reaction

Fig. 2

Radiomics feature selection by using the least absolute shrinkage and selection operator (LASSO) logistic regression. (a) The selection of tuning parameter (λ) in the LASSO model used 10-fold cross-validation via minimum criteria. The AUC curve was plotted versus log (λ). (b) LASSO coefficient profiles of the radiomics features. A vertical line was plotted at the optimal λ value, which resulted in 9 features with nonzero coefficients

Fig. 3

Receiver operating characteristic curves (ROC) for different models in the training (a), and validation cohorts (b); Calibration curves for the radiomics signature in the training (c), and validation cohorts (d); Radscore of different subtypes in the training (e), and validation cohorts (f). MSI-H = microsatellite instability-high, MSS = microsatellite stable

Fig. 4

Receiver operating characteristic curves (ROC) illustrating the predictive performance of the radiomics signature for immunotherapy response in the ZZU (a) and SDU cohorts (b). Radscore distribution for different immunotherapy responses in the ZZU (c) and SDU cohorts (d). Kaplan-Meier analysis of progression-free survival (PFS) and overall survival (OS) based on distinct radscore groups in the immunotherapy cohorts: (e) PFS stratified by radscore groups in the ZZU cohort; (f) OS stratified by radscore groups in the ZZU cohort; (g) PFS stratified by radscore groups in the SDU cohort; (h) OS stratified by radscore groups in the SDU cohort. CR = complete response, PR = partial response, SD = stable disease, PD = progressive disease

Fig. 5

The heatmap of the clinical- and immune-related molecular landscape. From the top to the end, there are five categories, encompassing clinical characteristics, immune cells, B7-CD28, TNF superfamily, and other immune-related molecular landscapes

Fig. 6

Immune cell infiltration (a), proportion of microsatellite instability (MSI) status (b), expression status of TNFRSF18 (c) and expression status of HHLA2 (d) in different radscore groups