Nomogram development for predicting ovarian tumor malignancy using inflammatory biomarker and CA-125

Abstract

AbstractGlobal challenges in ovarian cancer underscore the need for cost-effective screening. This study aims to assess the role of pretreatment Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte-Ratio (LMR), Platelet-to-Lymphocyte Ratio (PLR), and CA-125 in distinguishing benign and malignant ovarian tumors, while also constructing nomogram models for distinguish benign and malignant ovarian tumor using inflammatory biomarkers and CA-125. This is a retrospective study of 206 ovarian tumor patients. We conducted bivariate analysis to compare mean values of CA-125, LMR, NLR, and PLR with histopathology results. Multiple regression logistic analysis was then employed to establish predictive models for malignancy. NLR, PLR, and CA-125 exhibited statistically higher levels in malignant ovarian tumors compared to benign ones (5.56 ± 4.8 vs. 2.9 ± 2.58, 278.12 ± 165.2 vs. 180.64 ± 89.95, 537.2 ± 1621.47 vs. 110.08 ± 393.05, respectively), while lower LMR was associated with malignant tumors compared to benign (3.2 ± 1.6 vs. 4.24 ± 1.78, p = 0.0001). Multiple logistic regression analysis revealed that both PLR and CA125 emerged as independent risk factors for malignancy in ovarian tumors (P(z) 0.03 and 0.01, respectively). Utilizing the outcomes of multiple regression logistic analysis, a nomogram was constructed to enhance malignancy prediction in ovarian tumors. In conclusion, our study emphasizes the significance of NLR, PLR, CA-125, and LMR in diagnosing ovarian tumors. PLR and CA-125 emerged as independent risk factors for distinguishing between benign and malignant tumors. The nomogram model offers a practical way to enhance diagnostic precision.