OBJECTIVES: To analyse the diagnostic accuracy and to establish a predictive score based on diffusion-weighted magnetic resonance imaging (DWMRI) compared to exploratory laparotomy (EL) for predicting suboptimal cytoreductive surgery for different intra-abdominal sites of implants in patients with ovarian cancer. METHODS: Thirty-four patients with advanced ovarian carcinoma were studied. Preoperative DWMRI of the abdomen and pelvis was performed. DWMRI findings were compared with EL. Ten anatomical sites were selected for inclusion in the score. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy for suboptimal cytoreduction were calculated for both DWMRI and EL. Receiver operating characteristic (ROC) curve analysis was used to assess the ability to predict suboptimal cytoreduction. RESULTS: Using predictive score, ROC curves were generated with an area under the curve of 0.938 for DWMRI and 0.947 for EL (P < 0.0001). For DWMRI, a score ≥6 had the highest overall accuracy at 91.1 % and identified patients with unnecessary EL with a sensitivity of 75 %. For EL, a score ≥4 had the highest overall accuracy at 88.2 % and was able to identify patients with unnecessary EL with a sensitivity of 87.5 %. CONCLUSIONS: DWMRI is an emerging technique that may be useful to predict suboptimal cytoreduction in ovarian cancer. KEY POINTS: • DWMRI is increasingly used in ovarian cancer. • DWMRI is an accurate technique for depicting intra-abdominal sites of implants • DWMRI is useful for predicting optimal cytoreductive surgical outcome. • We report a high predictive value similar to exploratory laparotomy.
OBJECTIVES: To analyse the diagnostic accuracy and to establish a predictive score based on diffusion-weighted magnetic resonance imaging (DWMRI) compared to exploratory laparotomy (EL) for predicting suboptimal cytoreductive surgery for different intra-abdominal sites of implants in patients with ovarian cancer. METHODS: Thirty-four patients with advanced ovarian carcinoma were studied. Preoperative DWMRI of the abdomen and pelvis was performed. DWMRI findings were compared with EL. Ten anatomical sites were selected for inclusion in the score. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy for suboptimal cytoreduction were calculated for both DWMRI and EL. Receiver operating characteristic (ROC) curve analysis was used to assess the ability to predict suboptimal cytoreduction. RESULTS: Using predictive score, ROC curves were generated with an area under the curve of 0.938 for DWMRI and 0.947 for EL (P < 0.0001). For DWMRI, a score ≥6 had the highest overall accuracy at 91.1 % and identified patients with unnecessary EL with a sensitivity of 75 %. For EL, a score ≥4 had the highest overall accuracy at 88.2 % and was able to identify patients with unnecessary EL with a sensitivity of 87.5 %. CONCLUSIONS: DWMRI is an emerging technique that may be useful to predict suboptimal cytoreduction in ovarian cancer. KEY POINTS: • DWMRI is increasingly used in ovarian cancer. • DWMRI is an accurate technique for depicting intra-abdominal sites of implants • DWMRI is useful for predicting optimal cytoreductive surgical outcome. • We report a high predictive value similar to exploratory laparotomy.
Authors: Anna Fagotti; Gabriella Ferrandina; Francesco Fanfani; Giorgia Garganese; Giuseppe Vizzielli; Vito Carone; Maria Giovanna Salerno; Giovanni Scambia Journal: Am J Obstet Gynecol Date: 2008-09-17 Impact factor: 8.661
Authors: June Y Hou; Michael G Kelly; Herbert Yu; Jessica N McAlpine; Masoud Azodi; Thomas J Rutherford; Peter E Schwartz Journal: Gynecol Oncol Date: 2007-01-18 Impact factor: 5.482
Authors: Katrijn L M Michielsen; Ignace Vergote; Raphaëla Dresen; Katya Op de Beeck; Ragna Vanslembrouck; Frédéric Amant; Karin Leunen; Philippe Moerman; Steffen Fieuws; Frederik De Keyzer; Vincent Vandecaveye Journal: Br J Radiol Date: 2016-09-21 Impact factor: 3.039
Authors: Sileny N Han; Frédéric Amant; Katrijn Michielsen; Frederik De Keyzer; Steffen Fieuws; Kristel Van Calsteren; Raphaëla C Dresen; Mina Mhallem Gziri; Vincent Vandecaveye Journal: Eur Radiol Date: 2017-12-07 Impact factor: 5.315
Authors: Katrijn Michielsen; Ignace Vergote; Katya Op de Beeck; Frederic Amant; Karin Leunen; Philippe Moerman; Christophe Deroose; Geert Souverijns; Steven Dymarkowski; Frederik De Keyzer; Vincent Vandecaveye Journal: Eur Radiol Date: 2013-12-11 Impact factor: 5.315
Authors: Dirk Timmerman; François Planchamp; Tom Bourne; Chiara Landolfo; Andreas du Bois; Luis Chiva; David Cibula; Nicole Concin; Daniela Fischerova; Wouter Froyman; Guillermo Gallardo Madueño; Birthe Lemley; Annika Loft; Liliana Mereu; Philippe Morice; Denis Querleu; Antonia Carla Testa; Ignace Vergote; Vincent Vandecaveye; Giovanni Scambia; Christina Fotopoulou Journal: Int J Gynecol Cancer Date: 2021-06-10 Impact factor: 3.437