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A radiologic-laparoscopic model to predict suboptimal (or complete and optimal) debulking surgery in advanced ovarian cancer: a pilot study

Authors Llueca A, Serra A, Delgado K, Maiocchi K, Jativa R, Gomez L, Escrig J

Received 15 December 2018

Accepted for publication 25 February 2019

Published 28 May 2019 Volume 2019:11 Pages 333—342

DOI https://doi.org/10.2147/IJWH.S198355

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 2

Editor who approved publication: Professor Elie Al-Chaer


Antoni Llueca,1–3 Anna Serra,1–3 Katty Delgado,2,4 Karina Maiocchi,2,5 Rosa Jativa,2,6 Luis Gomez,2,5 Javier Escrig2–3,5

1Department of Obstetrics and Gynecology, University General Hospital of Castellon, Castellón, Spain; 2Multidisciplinary Unit of Abdominal Pelvic Oncology Surgery (MUAPOS), University General Hospital of Castellon, Castellón, Spain; 3Department of Medicine, University Jaume I(UJI), Castellon, Spain; 4Department of Radiology, University General Hospital of Castellon, Castellón, Spain; 5Department of General Surgery, University General Hospital of Castellon, Castellón, Spain; 6Department of Anesthesiology, University General Hospital of Castellon, Castellón, Spain

Introduction: Medical models assist clinicians in making diagnostic and prognostic decisions in complex situations. In advanced ovarian cancer, medical models could help prevent unnecessary exploratory surgery. We designed two models to predict suboptimal or complete and optimal cytoreductive surgery in patients with advanced ovarian cancer.
Methods: We collected clinical, pathological, surgical, and residual tumor data from 110 patients with advanced ovarian cancer. Computed tomographic and laparoscopic data from these patients were used to determine peritoneal cancer index (PCI) and lesion size score. These data were then used to construct two-by-two contingency tables and our two predictive models. Each model included three risk score levels; the R4 model also included operative PCI, while the R3 model did not. Finally, we used the original patient data to validate the models (narrow validation).
Results: Our models predicted suboptimal or complete and optimal cytoreductive surgery with a sensitivity of 83% (R4 model) and 69% (R3 model). Our results also showed that PCI>20 was a major risk factor for unresectability.
Conclusion: Our medical models successfully predicted suboptimal or complete and optimal cytoreductive surgery in 110 patients with advanced ovarian cancer. Our models are easy to construct, based on readily available laboratory test data, simple to use clinically, and could reduce unnecessary exploratory surgery in this patient group.

Keywords: advanced ovarian cancer, medical model, peritoneal cancer index, cytoreductive surgery

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