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Updated: Mar 29, 2026
Quantitation of Intra-peritoneal Ovarian Cancer Metastasis
Published on: July 18, 2016
Prediction of incomplete primary debulking surgery in patients with advanced ovarian cancer: An external validation
Iris J G Rutten1, Rafli van de Laar1, Roy F P M Kruitwagen1
1Department of Obstetrics and Gynecology, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
Objective:
To test the ability of three prospectively developed computed tomography (CT) models to predict incomplete primary debulking surgery in patients with advanced (International Federation of Gynecology and Obstetrics stages III-IV) ovarian cancer.
Methods:
Three prediction models to predict incomplete surgery (any tumor residual >1cm in diameter) previously published by Ferrandina (models A and B) and by Gerestein were applied to a validation cohort consisting of 151 patients with advanced epithelial ovarian cancer. All patients were treated with primary debulking surgery in the Eastern part of the Netherlands between 2000 and 2009 and data were retrospectively collected. Three individual readers evaluated the radiographic parameters and gave a subjective assessment. Using the predicted probabilities from the models, the area under the curve (AUC) was calculated which represents the discriminative ability of the model.
Results:
The AUC of the Ferrandina models was 0.56, 0.59 and 0.59 in model A, and 0.55, 0.60 and 0.59 in model B for readers 1, 2 and 3, respectively. The AUC of Gerestein's model was 0.69, 0.61 and 0.69 for readers 1, 2 and 3, respectively. AUC values of 0.69 and 0.63 for reader 1 and 3 were found for subjective assessment.
Conclusions:
Models to predict incomplete surgery in advanced ovarian cancer have limited predictive ability and their reproducibility is questionable. Subjective assessment seems as successful as applying predictive models. Present prediction models are not reliable enough to be used in clinical decision-making and should be interpreted with caution.
