Abstract
PURPOSE: The use of multivariable clinical models to assess postoperative prognosis in ovarian cancer increased. All published models incorporate surgical debulking. However, postoperative CT can detect residual disease (CT-RD) in 40% of optimally resected patients. The aim of our study was to investigate the added value of incorporating CT-RD evaluation into clinical models for assessment of overall survival (OS) and progression free survival (PFS) in patients after primary cytoreductive surgery (PCS). METHODS: 212 women with PCS for advanced ovarian cancer between 01/1997 and 12/2011, and a contrast enhanced abdominal CT 1-7 weeks after surgery were included in this IRB approved retrospective study. Two radiologists blinded to clinical data, evaluated all CT for the presence of CT-RD, and Cohen's kappa assessed agreement. Cox proportional hazards regression with stepwise selection was used to develop OS and PFS models, with CT-RD incorporated afterwards. Model fit was assessed with bootstrapped Concordance Probability Estimates (CPE), accounting for over-fitting bias by correcting the initial estimate after repeated subsampling. RESULTS: Readers agreed on the majority of cases (179/212, k=0.68). For OS and PFS, CT-RD was significant after adjusting for clinical factors with a CPE 0.663 (p=0.0264) and 0.649 (p=0.0008). CT-RD was detected in 37% of patients assessed as optimally debulked (RD<1cm) and increased the risk of death (HR: 1.58, 95% CI: 1.06-2.37%). CONCLUSION: CT-RD is a significant predictor after adjusting for clinical factors for both OS and PFS. Incorporating CT-RD into the clinical model improved the prediction of OS and PFS in patients after PCS for advanced ovarian cancer.