Nomogram to predict the probability of relapse in patients diagnosed with borderline ovarian tumors

Abstract

This study aimed to develop a nomogram predicting the probability of relapse in individual patients who have surgery for borderline ovarian tumors (BOTs). Methods: This retrospective study included 801 patients with BOT diagnosed between 1985 and 2008 at 6 gynecologic cancer centers.We analyzed covariates that were associated with the risk of developing a recurrence by multivariate logistic regression. We identified a parsimonious model by backward stepwise logistic regression. The 5 most significant or clinically important variables associated with an increased risk of recurrence were included in the nomogram. The nomogram was internally validated. Results: Fifty-one patients developed a recurrence after a median observation period of 57 months. Age at diagnosis, the International Federation of Gynecology and Obstetrics stage, cell type, preoperative serum CA125, and type of surgery (radical vs fertility-sparing) were associated with an increased risk of recurrence and were used in the nomogram. Bootstrap-corrected concordance index was 0.67 and showed good calibration. Conclusions: Five factors that are commonly available to clinicians treating patients with BOT were used in the development of a nomogram to predict the risk of recurrence. The nomogram will be useful to counsel patients about risk-reduction strategies to minimize the risk of recurrence or to inform patients about a very low risk of recurrence making intensive follow-up unwarranted. © 2013 by IGCS and ESGO.