Elucidating the biological function of computed tomography (CT)-based radiomics biomarkers in serous ovarian cancer

This study aims to elucidate the biological mechanisms in the tumour microenvironment as read from images by comprehensive Gene Set Enrichment Analysis (GSEA) based on radiomic features that predict survival. Furthermore, this study enables biological interpretation of survival prediction using radiomic features, which in turn allows for optimal treatment feedback to improve clinical outcomes.

This retrospective study included serous ovarian cancer patients with pretreatment computed tomography (CT) images. Prognostic radiomic features were selected using least absolute shrinkage and selection operator (LASSO)-Cox regression and calculated as a Rad-score. Patients were classified into low- and high-risk groups (HRGs), and a survival prediction model was constructed. Model performance was evaluated with Kaplan-Meier curves, the log-rank test, and the C-index. GSEA identified gene sets associated with radiomic features linked to survival.

The Kaplan-Meier curve using the log-rank test (p<0.01) and C-index values (0.768; 95% CI: 0.694-0.842) of the predictive models showed significant differences. GSEA was performed on the low- and HRGs, and the results identified a set of genes associated with cell proliferation, including the G2M checkpoint (p=0.006, FDR=0.138), mitotic spindle (p=0.006, FDR=0.156), and E2F targets (p=0.032, FDR=0.133).

This study revealed the biological functions underlying imaging features crucial for survival prediction and introduced an innovative approach to radiogenomics. This comprehensive approach promises to provide novel insights into the tumour microenvironment and potentially contribute to advancements in ovarian cancer treatment.