Epidemiology and nomogram for predicting the cancer-specific survival of ovarian granulosa cell tumor: A seer...

ovarian granulosa cell tumor (OGCT) is a kind of infrequent ovarian malignant tumor with limited epidemiological data available. we established a predictive nomograph to verify the clinical prognosis.

1005 diagnosed with ovarian granulosa cell tumor (OGCT) were extracted from Surveillance, Epidemiology, and End Results (SEER) public database from 2000-2018. Kaplan-Meier analysis was applied to distinguish risk factors, univariate and multivariate Cox analyses were used to determine the independent prognostic factors for cancer-specific survival (CSS) of OGCT patients. The obtained prognostic variables were combined to construct a nomogram model for predicting CSS in OGCT patients.

Model performance was detected and evaluated with ROC curves and calibration plots. Data collected from 1005 patients were divided into two groups: training cohort(n=703,70%) and validation cohort(n=302,30%). The multivariate Cox model identified five covariates including age, marital status, AJCC stages, surgery and chemotherapy as independent interfering factors of CSS. The nomogram has shown a promising and excellent accuracy in evaluating 3 -, 5 -, 8-year CSS in OGCT patients. In terms of the CSS of the training cohort, the AUC values of the 3 -, 5 -, 8-year ROC curves were 0.819,0.8,0.819, while in terms of the CSS of the validation cohort, the AUC values of the validation cohort were 0.822,0.84,0.823, respectively. All the calibration curves showed pleasant consistency between predicted and actual survival rates. The nomogram model established in the study can improve the veracity of prognosis prediction, thereby improving the accuracy of individualized survival risk assessment, and providing targeted and constructive recommendations for specific treatment options.

Age, advanced clinical stage, widower and without surgery therapy are independent risk factors for poor prognosis and the nomogram we constructed can help clinicians efficiently recognize high-risk OGCT patients to guide targeted therapies and improve their outcomes.