Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research.

OBJECTIVE

To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation.

METHODS

We conducted a retrospective observational study enrolling 1496 EC patients having received complete staging surgery from Qilu Hospital of Shandong University from 2012 to 2018. These patients were randomly divided into two cohorts: training cohort (n = 1046) and validation cohort (n = 448). A nomogram prediction model was developed based on univariate, least absolute shrinkage and selection operator (Lasso), and multivariate logistic regression. Then, the nomogram model's performance was evaluated in discrimination, calibration, and clinical utility three aspects.

RESULTS

Parametrium invasion, lymph node metastasis, and oviduct metastasis were finally contained in the nomogram prediction model. The AUC of the model in the training cohort was 0.85 compared with 0.72 in the validation cohort. It also behaved well in calibration and had good clinical utility. With a threshold probability of 20% ~ 80%, the nomogram increased the net benefit by 0 ~ 13.6 per 100 patients than surgery for all patients upon validation.

CONCLUSIONS

We develop a nomogram with good performances for predicting ovarian metastasis in EC patients, which may help clinicians identify candidate patients appropriate for ovarian preservation in premenopausal EC patients.