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Nomogram to predict the probability of relapse in patients diagnosed with borderline ovarian tumors
Andreas Obermair1, Amy Tang, Srinivas Kondalsamy-Chennakesavan
1Queensland Centre for Gynaecological Cancer, Royal Brisbane & Women's Hospital, University of Queensland, Brisbane, Australia. Obermair@powerup.com.au
Objective:
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.
