Abstract
This study aimed to find optimal biomarker combinations (panels) by using logistic regression for the purpose of comparing the accuracy of distinguishing cancerous from benign tumors at the earlier (Stages I and II) and advanced stages (Stages III and IV) of ovarian cancer. Data samples were extracted from 120 patients with benign ovarian cysts and 65 patients with ovarian cancer. The concentrations of 21 urine biomarkers specific to ovarian cancer were obtained using Luminex and used in the study. Data samples were divided into early-stage and advantage-stage sample groups, and 2–3 biomarker combinations with the best area under the curve (AUC) values were selected for each stage using logistic regression to identify the optimal combinations for the accurate diagnosis of ovarian cancer. Additionally, AUC, sensitivity, specificity, diagnostic accuracy, and positive and negative predictive values of all of the selected biomarker combinations were compared. Among the combinations of 2 biomarkers, the best performing combination showed AUC values of 85.83% and 97.98% for the early and advanced stages, respectively; the same for the 3-biomarker combinations was 92.77% and 98.74%, respectively. These results confirm that in ovarian cancer diagnosis, biomarkers are more effective in early-stage detection than in advanced-stage detection.
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