Development and Internal Validation of a Prognostic Model to Predict Recurrence Free Survival in Patients with Adult Granulosa Cell Tumors of the Ovary

Objective: Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model.

Methods: We performed a multicenter retrospective cohort study of patients with a GCT. Demographic, clinical and pathological information were considered as potential predictors. Univariable and multivariable analyses were performed using a Cox proportional hazards model. Using backward stepwise selection we identified the combination of predictors that best predicted recurrence free survival. Discrimination (c-statistic) and calibration were used to assess model performance. The model was internally validated using bootstrapping techniques to correct for overfitting. To increase clinical applicability of the model we developed a nomogram to allow individual prediction of recurrence free survival.

Results: We identified 127 patients with a GCT (median follow-up time was 131 months (IQR 70-215)). Recurrence of GCT occurred in 81 out of 127 patients (64%). The following four variables jointly best predicted recurrence free survival; clinical stage, Body Mass Index (BMI), tumor diameter and mitotic index. The model had a c-statistic of 0.73 (95% CI 0.66-0.80) and showed accurate calibration.

Conclusions: Recurrence free survival in patients with an adult GCT of the ovary can be accurately predicted by a combination of BMI, clinical stage, tumor diameter and mitotic index. The introduced nomogram could facilitate in counseling patients and may help to guide patients and caregivers in joint decisions on post-treatment surveillance.