TY - JOUR AB - Objective: The aim of this study was to construct a prognostic index that predicts risk ofrelapse in women who have completed first-line treatment for ovarian cancer (OC).Methods: A database of OC cases from 2000 to 2010 was interrogated for InternationalFederation of Gynecology and Obstetrics stage, grade and histological subtype of cancer,preoperative and posttreatment CA-125 level, presence or absence of residual disease aftercytoreductive surgery and on postchemotherapy computed tomography scan, and time toprogression and death. The strongest predictors of relapse were included into an algorithm,the Risk of Ovarian Cancer Relapse (ROVAR) score.Results: Three hundred fifty-four cases of OC were analyzed to generate the ROVARscore. Factors selected were preoperative serum CA-125, International Federation ofGynecology and Obstetrics stage and grade of cancer, and presence of residual disease atposttreatment computed tomography scan. In the validation data set, the ROVAR score had asensitivity and specificity of 94% and 61%, respectively. The concordance index for thevalidation data set was 0.91 (95% confidence interval, 0.85-0.96). The score allows patientstratification into low (G0.33), intermediate (0.34Y0.67), and high (90.67) probability ofrelapse.Conclusions: The ROVAR score stratifies patients according to their risk of relapsefollowing first-line treatment for OC. This can broadly facilitate the appropriate tailoring ofposttreatment care and support. AU - Rizzuto,I AU - Stavraka,C AU - Chatterjee,J AU - Borley,J AU - Hopkins,TG AU - Gabra,H AU - Ghaem-Maghami,S AU - Huson,L AU - Blagden,SP DO - 10.1097/IGC.0000000000000361 EP - 422 PY - 2015/// SN - 1525-1438 SP - 416 TI - Risk of Ovarian Cancer Relapse Score A Prognostic Algorithm to Predict Relapse Following Treatment for Advanced Ovarian Cancer T2 - International Journal of Gynecological Cancer UR - http://dx.doi.org/10.1097/IGC.0000000000000361 UR - http://hdl.handle.net/10044/1/28876 VL - 25 ER -