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International Journal of Gynecological Cancer:
doi: 10.1097/IGC.0b013e31827b8844
Ovarian Cancer

Nomogram to Predict the Probability of Relapse in Patients Diagnosed With Borderline Ovarian Tumors

Obermair, Andreas MD, FRANZCOG, CGO*†; Tang, Amy MBBS, FRANZCOG, CGO*; Kondalsamy–Chennakesavan, Srinivas MBBS, MPH; Ngan, Hextan MD; Zusterzeel, Petra MD, PhD§; Quinn, Michael MBV ChB, MGO, MRCP (UK), FRANZCOG, FRCOG, CGO; Carter, Jonathan MBBS, FRACOG, CGO; Leung, Yee MBBS FRANZCOG CGO#; Janda, Monika PhD**

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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.

Copyright © 2013 by IGCS and ESGO


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