Introduction
The aim of the study is to investigate the risk factors for developing lymph node metastases (LNM) in cases diagnosed as a presumed early-stage ovarian carcinoma (OC).
Methodology
Information of patients who had been diagnosed as OC in 2018 was obtained from the SEER database. We enrolled 104 OC patients in General Hospital of Northern Theatre Command for external validation. A logistic regression was conducted to determine the independent predictors for LNM, which were used for establishing a nomogram. In order to evaluate the reliability of nomogram, we applied a receiver operating characteristic curve (ROC) analysis, calibration curves and plotted decision curves.
Results
We found that age(≥70, OR=0.544, p=0.022), histology type (Mucinous carcinoma, OR=0.390, p=0.001; Endometrioid carcinoma, OR=7.946, p=0.053; Others, OR=2.400, p=0.040), histology grade (Grade II, OR=2.423, p=0.028; Grade III, OR=1.982, p=0.152; Grade IV, OR=1.594, p=0.063) and preoperative serum CA125 level (positive, OR=2.236, p=0.001) were all significant predictors of LNM. The AUC of the model training cohort, internal validation cohort, and external validation cohort were 0.78, 0.79 and 0.76 respectively. The calibration curves showed that the predicted outcome fitted well to the observed outcome in the training cohort (p=0.825) internal validation cohort (p=0.503), and external validation cohort (p=0.108). The decision curves showed the nomogram had more benefits than the All or None scheme if the threshold probability is >50% and <100% in training cohort and internal validation cohort, >30% and <90% in the external validation cohort.
Conclusion
The multivariate logistic regression showed that age, histology type, histology grade and preoperative serum CA125 level were all significant predictors of LNM. The nomogram established using the above variables had great performance for clinical applying.