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.
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.
authors.affiliation: *Department of Obstetrics and Gynaecology, Centre for Gynaecologic Oncology Amsterdam, Academic Medical Centre; †Department of Radiology, Anthony van Leeuwenhoek Hospital; and ‡Department of Radiology, Academic Medical Centre, Amsterdam, the Netherlands.
authors.forename: Marianne Jetske
authors.initials: MJ
authors.lastname: Rutten
authors.forename: Roelien
authors.initials: R
authors.lastname: van de Vrie
authors.forename: Annemarie
authors.initials: A
authors.lastname: Bruining
authors.forename: Anje M
authors.initials: AM
authors.lastname: Spijkerboer
authors.forename: Ben Willem
authors.initials: BW
authors.lastname: Mol
authors.forename: Gemma Georgette
authors.initials: GG
authors.lastname: Kenter
authors.forename: Marrije Renate
authors.initials: MR
authors.lastname: Buist
mesh_terms: D019540:Area Under Curve; D065426:Cytoreduction Surgical Procedures; D005260:Female; D006801:Humans; D009367:Neoplasm Staging; D018365:Neoplasm, Residual; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D011237:Predictive Value of Tests; D012372:ROC Curve; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome