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

Marianne Jetske Rutten, Roelien Van De Vrie, Annemarie Bruining, Anje M. Spijkerboer, Ben Willem Mol, Gemma Georgette Kenter, Marrije Renate Buist

Research output: Contribution to journalReview article

33 Citations (Scopus)

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.

LanguageEnglish
Pages407-415
Number of pages9
JournalInternational Journal of Gynecological Cancer
Volume25
Issue number3
DOIs
Publication statusPublished - 10 Mar 2015

Keywords

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

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynaecology

Cite this

Rutten, Marianne Jetske ; Van De Vrie, Roelien ; Bruining, Annemarie ; Spijkerboer, Anje M. ; Mol, Ben Willem ; Kenter, Gemma Georgette ; Buist, Marrije Renate. / 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. In: International Journal of Gynecological Cancer. 2015 ; Vol. 25, No. 3. pp. 407-415.
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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. / Rutten, Marianne Jetske; Van De Vrie, Roelien; Bruining, Annemarie; Spijkerboer, Anje M.; Mol, Ben Willem; Kenter, Gemma Georgette; Buist, Marrije Renate.

In: International Journal of Gynecological Cancer, Vol. 25, No. 3, 10.03.2015, p. 407-415.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Predicting surgical outcome in patients with international federation of gynecology and obstetrics stage III or IV ovarian cancer using computed tomography

T2 - International Journal of Gynecological Cancer

AU - Rutten, Marianne Jetske

AU - Van De Vrie, Roelien

AU - Bruining, Annemarie

AU - Spijkerboer, Anje M.

AU - Mol, Ben Willem

AU - Kenter, Gemma Georgette

AU - Buist, Marrije Renate

PY - 2015/3/10

Y1 - 2015/3/10

N2 - 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.

AB - 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.

KW - Computed tomography

KW - Ovarian carcinoma

KW - Prediction model

KW - Residual disease

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