Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/90762
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Type: Journal article
Title: Predicting surgical outcome in patients with international federation of gynecology and obstetrics stage III or IV ovarian cancer using computed tomography: a systematic review of prediction models
Author: Rutten, M.
Van De Vrie, R.
Bruining, A.
Spijkerboer, A.
Mol, B.
Kenter, G.
Buist, M.
Citation: International Journal of Gynecological Cancer, 2015; 25(3):407-415
Publisher: Lippincott Williams and Wilkins
Issue Date: 2015
ISSN: 1048-891X
1525-1438
Statement of
Responsibility: 
Marianne Jetske Rutten, Roelien van de Vrie, Annemarie Bruining, Anje M. Spijkerboer, Ben Willem Mol, Gemma Georgette Kenter, Marrije Renate Buist
Abstract: OBJECTIVE: Maximal cytoreduction to no residual disease is an important predictor of prognosis in patients with advanced-stage epithelial ovarian cancer. Preoperative prediction of outcome of surgery should guide treatment decisions, for example, primary debulking or neoadjuvant chemotherapy followed by interval debulking surgery. The objective of this study was to systematically review studies evaluating computed tomography imaging based models predicting the amount of residual tumor after cytoreductive surgery for advanced-stage epithelial ovarian cancer. METHODS: We systematically searched the literature for studies investigating multivariable models that predicted the amount of residual disease after cytoreductive surgery in advanced-stage epithelial ovarian cancer using computed tomography imaging. Detected studies were scored for quality and classified as model derivation or validation studies. We summarized their performance in terms of discrimination when possible. RESULTS: We identified 11 studies that described 13 models. The 4 models that were externally validated all had a poor discriminative capacity (sensitivity, 15%-79%; specificity, 32%-64%). The only internal validated model had an area under the receiver operating characteristic curve of 0.67. Peritoneal thickening, mesenterial and diaphragm disease, and ascites were most often used as predictors in the final models. We did not find studies that assessed the impact of prediction model on outcomes. CONCLUSIONS: Currently, there are no external validated studies with a good predictive performance for residual disease. Studies of better quality are needed, especially studies that focus on predicting any residual disease after surgery.
Keywords: Computed tomography; Ovarian carcinoma; Prediction model; Residual disease.
Rights: (C) 2015 by the International Gynecologic Cancer Society and the European Society of Gynaecological Oncology.
DOI: 10.1097/IGC.0000000000000368
Published version: http://dx.doi.org/10.1097/igc.0000000000000368
Appears in Collections:Aurora harvest 7
Obstetrics and Gynaecology publications

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