CT-based radiomics prediction of CXCL13 expression in ovarian cancer

Background: Ovarian cancer, the most common malignancy in the female reproductive system, and patients tend to be at middle and advanced clinical stages when diagnosed. Therefore, early detection and early diagnosis have important clinical significance for the treatment of ovarian cancer patients. CXCL13, a chemokine with the ligands CXCR3 and CXCR5, is involved in the tumor metastasis process.

Purpose: This study aimed to predict mRNA expression of CXCL13 in ovarian cancer tissues noninvasively.

Methods: Medical imaging data and transcriptomic sequencing data of the 343 ovarian cancer patients were downloaded from the TCIA and TCGA databases, respectively. Seventy-six radiomics features were extracted from the CT data. Seven features were selected for model construction by using logistic regression. Accuracy, specificity, sensitivity, positive predictive value, and negative predictive value were used to evaluate the radiomics model.

Results: High CXCL13 expression was found to be a significant protective factor for OS [HR (95% CI) = 0.755 (0.622-0.916), p = 0.004]. There was a significant positive correlation between CXCL13 and the degree of eosinophil infiltration. A calibration curve and the Hosmer-Lemeshow goodness-of-fit test showed that the prediction probability of the radiomics prediction model for high expression of CXCL13 was consistent with the true value. The AUC value of the nomogram model's ability to predict OS (12 months) was 0.758. The calibration plot and DCA both showed high clinical applicability for the nomogram model.

Conclusion: CXCL13 is a candidate predictive biomarker for OC and correlates with the degree of plasma cell and eosinophil infiltration.