Contrast-Enhanced Computed Tomography Radiomics Predicts Colony-Stimulating Factor 3 Expression and Clinical Prognosis in Ovarian Cancer

Abstract

Rationale and Objectives

To develop a radiomics model for non-invasive prediction of colony-stimulating factor 3 (CSF3) expression in ovarian cancer (OC) and evaluate its prognostic value.

Materials and Methods

We acquired clinical data, genetic information, and corresponding computed tomography (CT) scans of OC from The Cancer Genome Atlas and The Cancer Imaging Archive repositories. We assessed the prognostic significance of CSF3 and its association with clinical features through the utilization of Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, along with subgroup analysis. To explore the potential molecular mechanisms associated with CSF3 expression, we utilized gene set enrichment analysis and conducted an analysis on immune-cell infiltration. The max-relevance and min-redundancy and recursive feature elimination (RFE) algorithms were used for feature screening. The CT-based radiomics prediction model was built using support vector machine (SVM) and logistic regression (LR).

Results

The expression of CSF3 was found to be decreased in OC, and high expression of CSF3 was associated with poor overall survival. Moreover, it was noted that the expression of CSF3 exhibited a positive correlation with programmed death ligand 1 (PD-L1) and sialic acid-binding Ig-like lectin 15 (SIGLEC15). Patients with high CSF3 expression exhibited a decrease in tumor necrosis factor receptor superfamily member 7 (CD27) expression. The infiltration of neutrophils increased and CD8 +T cells decreased in CSF3 high expression group.

Conclusion

The radiomics model, which utilized both LR and SVM methods, demonstrated significant clinical applicability. The expression level of CSF3 was related to the prognosis of OC. Radiomics based on CT can serve as a novel tool for forecasting prognosis.