Identification of prognostic factors and construction of nomogram to predict cancer-specific survival for patients with ovarian granulosa cell tumors

Abstract

Background: Ovarian granulosa cell tumors (OGCTs) feature low incidence, indolent growth and late recurrence. Treatment for recurrent OGCTs is challenging. Methods: The present study was designed to explore the prognostic factors and establish a nomogram to predict cancer-specific survival (CSS) for OGCTs patients. Enrolled in the study were 1459 eligible patients in the Surveillance, Epidemiology, and End Results (SEER) database, who were randomized to the training (n = 1021) or testing set (n = 438) at a ratio of 7:3. Univariate and multivariate Cox regression analyses were employed to screen the prognostic factors. The predictors were determined by using the Least absolute shrinkage and selection operator (LASSO) regression analysis. The model was constructed via the Cox proportional hazards risk regression analysis. The performance and clinical value of the nomograms was assessed with C-index, calibration plots, and decision curve analysis. Results: Age, pTNM stage, tumor size, surgery of the primary tumor, surgery of regional lymph nodes (LNs), residual disease after surgery, and chemotherapy were considered as significant predictive factors for CSS in OGCTs patients. After screening, the prognostic factors except surgery of regional LNs and chemotherapy were employed to build the nomogram. With desirable discrimination and calibration, the nomogram was more powerful in predicting CSS than the American Joint Committee on Cancer staging system in clinical use. Conclusion: This novel prognostic nomogram, which comprises a stationary nomogram and a web-based calculator, offers convenience for clinicians in personalized decision-making including optimal treatment plans and prognosis assessments for OGCTs patients.