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42 Clinicopathologic predictors of early relapse in advanced epithelial ovarian cancer; development of prediction models using nationwide data
  1. S Said1,
  2. R Bretveld2,
  3. H Koffijberg3,
  4. G Sonke4,
  5. RFPM Kruitwagen5,
  6. JA de Hullu1,
  7. AM van Altena1,
  8. S Siesling2 and
  9. MA van der Aa2
  1. 1Department of Obstetrics and Gynecology, Radboud Institute for Health Sciences, Radboud university medical center, Netherlands
  2. 2Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Netherlands
  3. 3Department of Health Technology and Services Research, Technical Medical Center, University of Twente, Netherlands
  4. 4Department of Medical Oncology, The Netherlands Cancer Institute, Netherlands
  5. 5Department of Obstetrics and Gynecology, Maastricht University Medical Centre, Netherlands


Objective To identify clinicopathologic factors predictive of early relapse (i.e. a platinum-free interval (PFI) of ≤6 months) in advanced epithelial ovarian cancer (EOC) in first-line treatment, and to develop and internally validate risk prediction models for early relapse.

Methods All consecutive patients diagnosed with advanced EOC between 01-01-2008 and 31-12-2015 were identified from the Netherlands Cancer Registry. Patients who underwent debulking and platinum-based chemotherapy as initial EOC treatment were selected. Two prediction models, a pretreatment and postoperative model, were developed. Candidate predictors of early relapse were fitted into multivariable logistic regression models. Model selection was performed using backward selection (p-value<0.20). Model performance was assessed on calibration and discrimination. Internal validation was performed through bootstrapping to correct for model optimism.

Results A total of 4,557 advanced EOC patients were identified, including 3,171 late or non-relapsers and 1,302 early relapsers. Early relapsers were more likely to have FIGO stage IV, mucinous or clear cell type EOC, ascites, >1 cm residual disease, and to have undergone interval debulking. The final pretreatment model demonstrated subpar model performance (AUC=0.65 [95%-CI 0.64–0.67]). The final postoperative model based on FIGO stage, histologic subtype, presence of ascites, type of debulking, and residual disease after debulking, demonstrated good model performance (AUC=0.72 [95%-CI 0.71–0.74]). Bootstrap validation revealed minimal optimism of the final postoperative model.

Conclusion A good (postoperative) discriminative model has been developed and presented online that predicts the risk of early relapse in advanced EOC patients. Although external validation is still required, this prediction model can support treatment decision-making in daily clinical practice.

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