Prediction Model for Preoperative Diagnosis of Ovarian Cancer Using Tumor Markers, CBC, and LFT

The preoperative diagnosis of ovarian cancer (OC) was developed based on risk factor
groups using secondary data. Binary and multiple logistic regression and its operating characteristic
curve were used to analyze the data of risk factor groups for tumor markers, complete blood count
(CBC), and liver function tests (LFT), respectively, and to explore potential predictors for each risk
factor group. The data of 202 patients with ovarian cancer were analyzed in this research. As
the tumor markers group, menopausal status, human epididymal protein 4, and cancer antigen
19-9 were included as the derivation of the preoperative diagnosis index. For the CBC group,
menopausal status, lymphocyte count, and basophil cell ratio were used as predictors. Menopausal
status, albumin, alkaline phosphatase, and indirect bilirubin were used as predictors for the LFT
group. The area under the receiver operating characteristic curve (AUROC) for tumor markers,
CBC, and LFT were 0.89 (95% CI, 0.845–0.935; sensitivity = 0.776, specificity = 0.919), 0.813 (95% CI,
0.755–0.871; sensitivity = 0.741, specificity = 0.767), and 0.81 (95% CI, 0.751–0.868; sensitivity = 0.664,
specificity = 0.837), respectively.