Background and objectives: ultrasound is considered to be the primary tool for preoperative assessment of ovarian masses; however, the discrimination of borderline ovarian tumours (BOTs) is challenging, and depends highly on the experience of the sonographer. The Assessment of Different NEoplasias in the adneXa (ADNEX) model is considered to be a valuable diagnostic tool for preoperative assessment of ovarian masses; however, its performance for BOTs has not been widely studied, due to the low prevalence of these tumours. The aim of this study was to evaluate the performance of ADNEX model for preoperative diagnosis of BOTs. Methods: retrospective analysis of preoperative ultrasound datasets of patients diagnosed with BOTs on the final histology after performed surgery was done at a tertiary oncogynaecology centre during the period of 2012-2018. Results: 85 patients were included in the study. The performance of ADNEX model based on absolute risk (AR) improved with the selection of a more inclusive cut-off value, varying from 47 (60.3%) correctly classified cases of BOTs, with the selected cut-off of 20%, up to 67 (85.9%) correctly classified cases of BOTs with the cut-off value of 3%. When relative risk (RR) was used to classify the tumours, 59 (75.6%) cases were identified correctly. Forty (70.2%) cases of serous and 16 (72.7%) cases of mucinous BOTs were identified when AR with a 10% cut-off value was applied, compared to 44 (77.2%) and 15 (68.2%) cases of serous and mucinous BOTs, correctly classified by RR. The addition of Ca125 improved the performance of ADNEX model for all BOTs in general, and for different subtypes of BOTs. However, the differences were insignificant. Conclusions: The International Ovarian Tumour Analysis (IOTA) ADNEX model performs well in discriminating BOTs from other ovarian tumours irrespective of the subtype. The calculation based on RR or AR with the cut-off value of at least 10% should be used when evaluating for BOTs.
Background and objectives: ultrasound is considered to be the primary tool for preoperative assessment of ovarian masses; however, the discrimination of borderline ovarian tumours (BOTs) is challenging, and depends highly on the experience of the sonographer. The Assessment of Different NEoplasias in the adneXa (ADNEX) model is considered to be a valuable diagnostic tool for preoperative assessment of ovarian masses; however, its performance for BOTs has not been widely studied, due to the low prevalence of these tumours. The aim of this study was to evaluate the performance of ADNEX model for preoperative diagnosis of BOTs. Methods: retrospective analysis of preoperative ultrasound datasets of patients diagnosed with BOTs on the final histology after performed surgery was done at a tertiary oncogynaecology centre during the period of 2012-2018. Results: 85 patients were included in the study. The performance of ADNEX model based on absolute risk (AR) improved with the selection of a more inclusive cut-off value, varying from 47 (60.3%) correctly classified cases of BOTs, with the selected cut-off of 20%, up to 67 (85.9%) correctly classified cases of BOTs with the cut-off value of 3%. When relative risk (RR) was used to classify the tumours, 59 (75.6%) cases were identified correctly. Forty (70.2%) cases of serous and 16 (72.7%) cases of mucinous BOTs were identified when AR with a 10% cut-off value was applied, compared to 44 (77.2%) and 15 (68.2%) cases of serous and mucinous BOTs, correctly classified by RR. The addition of Ca125 improved the performance of ADNEX model for all BOTs in general, and for different subtypes of BOTs. However, the differences were insignificant. Conclusions: The International Ovarian Tumour Analysis (IOTA) ADNEX model performs well in discriminating BOTs from other ovarian tumours irrespective of the subtype. The calculation based on RR or AR with the cut-off value of at least 10% should be used when evaluating for BOTs.
Authors: D Timmerman; P Schwärzler; W P Collins; F Claerhout; M Coenen; F Amant; I Vergote; T H Bourne Journal: Ultrasound Obstet Gynecol Date: 1999-01 Impact factor: 7.299
Authors: Christina Fotopoulou; Jalid Sehouli; Nina Ewald-Riegler; Nikolaus de Gregorio; Alexander Reuss; Rolf Richter; Sven Mahner; Friedrich Kommoss; Barbara Schmalfeldt; Tanja Fehm; Lars Hanker; Pauline Wimberger; Ulrich Canzler; Jacobus Pfisterer; Stefan Kommoss; Steffen Hauptmann; Andreas du Bois Journal: Int J Gynecol Cancer Date: 2015-09 Impact factor: 3.437
Authors: E Fruscella; A C Testa; G Ferrandina; F De Smet; C Van Holsbeke; G Scambia; G F Zannoni; M Ludovisi; R Achten; F Amant; I Vergote; D Timmerman Journal: Ultrasound Obstet Gynecol Date: 2005-11 Impact factor: 7.299
Authors: N Colombo; C Sessa; A du Bois; J Ledermann; W G McCluggage; I McNeish; P Morice; S Pignata; I Ray-Coquard; I Vergote; T Baert; I Belaroussi; A Dashora; S Olbrecht; F Planchamp; D Querleu Journal: Ann Oncol Date: 2019-05-01 Impact factor: 32.976
Authors: F Moro; C Baima Poma; G F Zannoni; A Vidal Urbinati; T Pasciuto; M Ludovisi; M C Moruzzi; S Carinelli; D Franchi; G Scambia; A C Testa Journal: Ultrasound Obstet Gynecol Date: 2017-11-02 Impact factor: 7.299
Authors: Ben Van Calster; Kirsten Van Hoorde; Lil Valentin; Antonia C Testa; Daniela Fischerova; Caroline Van Holsbeke; Luca Savelli; Dorella Franchi; Elisabeth Epstein; Jeroen Kaijser; Vanya Van Belle; Artur Czekierdowski; Stefano Guerriero; Robert Fruscio; Chiara Lanzani; Felice Scala; Tom Bourne; Dirk Timmerman Journal: BMJ Date: 2014-10-15
Authors: Artur Czekierdowski; Norbert Stachowicz; Agata Smoleń; Tomasz Kluz; Tomasz Łoziński; Andrzej Miturski; Janusz Kraczkowski Journal: Diagnostics (Basel) Date: 2021-02-28