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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
  1. Marianne Jetske Rutten, MD*,
  2. Roelien van de Vrie, MD*,
  3. Annemarie Bruining, MD,
  4. Anje M. Spijkerboer, MD, PhD,
  5. Ben Willem Mol, MD, PhD*,
  6. Gemma Georgette Kenter, MD, PhD* and
  7. Marrije Renate Buist, MD, PhD*
  1. *Department of Obstetrics and Gynaecology, Centre for Gynaecologic Oncology Amsterdam, Academic Medical Centre;
  2. Department of Radiology, Anthony van Leeuwenhoek Hospital; and
  3. Department of Radiology, Academic Medical Centre, Amsterdam, the Netherlands.
  1. Address correspondence and reprint requests to Marianne Jetske Rutten, MD, Centre for Gynaecologic Oncology Amsterdam, Meibergdreef 9 1105 AZ, Amsterdam, the Netherlands. E-mail: m.j.rutten@amc.uva.nl.

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.

  • Computed tomography
  • Ovarian carcinoma
  • Prediction model
  • Residual disease

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Footnotes

  • No financial support was provided for this work.

  • The authors declare no conflicts of interest.

  • Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.ijgc.net).

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