Sebastian Szubert1, Andrzej Wojtowicz2, Rafal Moszynski3, Patryk Zywica2, Krzysztof Dyczkowski2, Anna Stachowiak2, Stefan Sajdak3, Dariusz Szpurek3, Juan Luis Alcazar4. 1. Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland. Electronic address: szuberts@o2.pl. 2. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznan, Poland. 3. Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland. 4. Department of Obstetrics and Gynecology, Clinica Universidad de Navarra, University of Navarra School of Medicine, Pamplona, Spain.
Abstract
OBJECTIVES: The external, two-center validation of the IOTA ADNEX model for differential diagnosis of adnexal tumors. METHODS: A total of 204 patients with adnexal masses (134 benign and 70 malignant) treated at the Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland (Center I), and 123 patients (89 benign and 34 malignant) from the Department of Obstetrics and Gynecology, Clinica Universidad de Navarra, University of Navarra School of Medicine, Pamplona, Spain (Center II), were enrolled into the study. RESULTS: ADNEX achieved high accuracy in discriminating between malignant and benign ovarian tumors in both centers (79.9% and 81.3% in Centers I and II, respectively). Multiclass accuracy was substantially lower than in binary classification (malignant vs. benign): 64.2% and 74.0% in Centers I and II, respectively. Sensitivity and specificity for the diagnosis of specific tumor types in Center I were as follows: benign tumors - 72.4% and 94.3%; borderline tumors - 33.3% and 87.0%, stage I ovarian cancers - 00.0% and 91.8%; stage II-IV ovarian cancers - 68.2% and 83.1%; and metastatic tumors - 00.0% and 99.5%. Sensitivity and specificity in Center II were as follows: benign tumors - 75.3% and 97.1%; borderline tumors - 50.0% and 88.2%, stage I ovarian cancers - 40.0% and 97.5%; stage II-IV ovarian cancers - 95.0% and 88.3%; and metastatic tumors - 20.0% and 98.3%. CONCLUSIONS: ADNEX is characterized by very high accuracy in differentiating between malignant and benign adnexal tumors. However, prediction of ovarian tumor types could be more accurate.
OBJECTIVES: The external, two-center validation of the IOTA ADNEX model for differential diagnosis of adnexal tumors. METHODS: A total of 204 patients with adnexal masses (134 benign and 70 malignant) treated at the Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland (Center I), and 123 patients (89 benign and 34 malignant) from the Department of Obstetrics and Gynecology, Clinica Universidad de Navarra, University of Navarra School of Medicine, Pamplona, Spain (Center II), were enrolled into the study. RESULTS: ADNEX achieved high accuracy in discriminating between malignant and benign ovarian tumors in both centers (79.9% and 81.3% in Centers I and II, respectively). Multiclass accuracy was substantially lower than in binary classification (malignant vs. benign): 64.2% and 74.0% in Centers I and II, respectively. Sensitivity and specificity for the diagnosis of specific tumor types in Center I were as follows: benign tumors - 72.4% and 94.3%; borderline tumors - 33.3% and 87.0%, stage I ovarian cancers - 00.0% and 91.8%; stage II-IV ovarian cancers - 68.2% and 83.1%; and metastatic tumors - 00.0% and 99.5%. Sensitivity and specificity in Center II were as follows: benign tumors - 75.3% and 97.1%; borderline tumors - 50.0% and 88.2%, stage I ovarian cancers - 40.0% and 97.5%; stage II-IV ovarian cancers - 95.0% and 88.3%; and metastatic tumors - 20.0% and 98.3%. CONCLUSIONS: ADNEX is characterized by very high accuracy in differentiating between malignant and benign adnexal tumors. However, prediction of ovarian tumor types could be more accurate.
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
Authors: Ben Van Calster; Lil Valentin; Wouter Froyman; Chiara Landolfo; Jolien Ceusters; Antonia C Testa; Laure Wynants; Povilas Sladkevicius; Caroline Van Holsbeke; Ekaterini Domali; Robert Fruscio; Elisabeth Epstein; Dorella Franchi; Marek J Kudla; Valentina Chiappa; Juan L Alcazar; Francesco P G Leone; Francesca Buonomo; Maria Elisabetta Coccia; Stefano Guerriero; Nandita Deo; Ligita Jokubkiene; Luca Savelli; Daniela Fischerová; Artur Czekierdowski; Jeroen Kaijser; An Coosemans; Giovanni Scambia; Ignace Vergote; Tom Bourne; Dirk Timmerman Journal: BMJ Date: 2020-07-30