Imperial College London


Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Honorary Clinical Senior Lecturer







Institute of Reproductive and Developmental BiologyHammersmith Campus






BibTex format

author = {Rizzuto, I and Stavraka, C and Chatterjee, J and Borley, J and Hopkins, TG and Gabra, H and Ghaem-Maghami, S and Huson, L and Blagden, SP},
doi = {10.1097/IGC.0000000000000361},
journal = {International Journal of Gynecological Cancer},
pages = {416--422},
title = {Risk of Ovarian Cancer Relapse Score A Prognostic Algorithm to Predict Relapse Following Treatment for Advanced Ovarian Cancer},
url = {},
volume = {25},
year = {2015}

RIS format (EndNote, RefMan)

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 -
UR -
VL - 25
ER -