Literature DB >> 27194129

Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center.

K G Araujo1,2, R M Jales1,2, P N Pereira1, A Yoshida1, L de Angelo Andrade3, L O Sarian1, S Derchain1.   

Abstract

OBJECTIVE: To evaluate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX model in the preoperative discrimination between benign ovarian (including tubal and para-ovarian) tumors, borderline ovarian tumors (BOT), Stage I ovarian cancer (OC), Stage II-IV OC and ovarian metastasis in a gynecological oncology center in Brazil.
METHODS: This was a diagnostic accuracy study including 131 women with an adnexal mass invited to participate between February 2014 and November 2015. Before surgery, pelvic ultrasound examination was performed and serum levels of tumor marker CA 125 were measured in all women. Adnexal masses were classified according to the IOTA ADNEX model. Histopathological diagnosis was the gold standard. Receiver-operating characteristics (ROC) curve analysis was used to determine the diagnostic accuracy of the model to classify tumors into different histological types.
RESULTS: Of 131 women, 63 (48.1%) had a benign ovarian tumor, 16 (12.2%) had a BOT, 17 (13.0%) had Stage I OC, 24 (18.3%) had Stage II-IV OC and 11 (8.4%) had ovarian metastasis. The area under the ROC curve (AUC) was 0.92 (95% CI, 0.88-0.97) for the basic discrimination between benign vs malignant tumors using the IOTA ADNEX model. Performance was high for the discrimination between benign vs Stage II-IV OC, BOT vs Stage II-IV OC and Stage I OC vs Stage II-IV OC, with AUCs of 0.99, 0.97 and 0.94, respectively. Performance was poor for the differentiation between BOT vs Stage I OC and between Stage I OC vs ovarian metastasis with AUCs of 0.64.
CONCLUSION: The majority of adnexal masses in our study were classified correctly using the IOTA ADNEX model. On the basis of our findings, we would expect the model to aid in the management of women with an adnexal mass presenting to a gynecological oncology center.
Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  adnexal masses; ovarian cancer; ovarian neoplasm; preoperative; ultrasonography

Mesh:

Year:  2017        PMID: 27194129     DOI: 10.1002/uog.15963

Source DB:  PubMed          Journal:  Ultrasound Obstet Gynecol        ISSN: 0960-7692            Impact factor:   7.299


  12 in total

1.  Response to letter to the editor concerning validation of IOTA ADNEX model.

Authors:  Sebastian Szubert; Andrzej Wojtowicz; Patryk Zywica
Journal:  Gynecol Oncol Rep       Date:  2016-11-01

2.  Validation of the Performance of International Ovarian Tumor Analysis (IOTA) Methods in the Diagnosis of Early Stage Ovarian Cancer in a Non-Screening Population.

Authors:  Wouter Froyman; Laure Wynants; Chiara Landolfo; Tom Bourne; Lil Valentin; Antonia Testa; Povilas Sladkevicius; Dorella Franchi; Daniela Fischerova; Luca Savelli; Ben Van Calster; Dirk Timmerman
Journal:  Diagnostics (Basel)       Date:  2017-06-02

3.  Validation of IOTA-ADNEX Model in Discriminating Characteristics of Adnexal Masses: A Comparison with Subjective Assessment.

Authors:  Soo Young Jeong; Byung Kwan Park; Yoo Young Lee; Tae-Joong Kim
Journal:  J Clin Med       Date:  2020-06-26       Impact factor: 4.241

4.  Diagnostic value of the gynecology imaging reporting and data system (GI-RADS) with the ovarian malignancy marker CA-125 in preoperative adnexal tumor assessment.

Authors:  Michal Migda; Migda Bartosz; Marian S Migda; Marcin Kierszk; Gieryn Katarzyna; Marek Maleńczyk
Journal:  J Ovarian Res       Date:  2018-11-03       Impact factor: 4.234

5.  Estimating the risk of malignancy of adnexal masses: validation of the ADNEX model in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China.

Authors:  Ping He; Jing-Jing Wang; Wei Duan; Chao Song; Yu Yang; Qing-Qing Wu
Journal:  J Ovarian Res       Date:  2021-12-02       Impact factor: 4.234

6.  Evaluation of the Diagnostic Value of the Ultrasound ADNEX Model for Benign and Malignant Ovarian Tumors.

Authors:  Xiao-Shan Peng; Yue Ma; Ling-Ling Wang; Hai-Xia Li; Xiu-Lan Zheng; Ying Liu
Journal:  Int J Gen Med       Date:  2021-09-16

Review 7.  ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors.

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

8.  Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study.

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

9.  Diagnostic Accuracy of the ADNEX Model for Ovarian Cancer at the 15% Cut-Off Value: A Systematic Review and Meta-Analysis.

Authors:  Xiaotong Huang; Ziwei Wang; Meiqin Zhang; Hong Luo
Journal:  Front Oncol       Date:  2021-06-17       Impact factor: 6.244

10.  Comparison of the Diagnostic Performances of Ultrasound-Based Models for Predicting Malignancy in Patients With Adnexal Masses.

Authors:  Le Qian; Qinwen Du; Meijiao Jiang; Fei Yuan; Hui Chen; Weiwei Feng
Journal:  Front Oncol       Date:  2021-06-01       Impact factor: 6.244

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