Literature DB >> 20016188

Ultrasound experience substantially impacts on diagnostic performance and confidence when adnexal masses are classified using pattern recognition.

Caroline Van Holsbeke1, Anneleen Daemen, Joseph Yazbek, Tom K Holland, Tom Bourne, Tinne Mesens, Lore Lannoo, Anne-Sophie Boes, Annelies Joos, Arne Van De Vijver, Nele Roggen, Bart de Moor, Eric de Jonge, Antonia C Testa, Lil Valentin, Davor Jurkovic, Dirk Timmerman.   

Abstract

AIM: To determine how accurately and confidently examiners with different levels of ultrasound experience can classify adnexal masses as benign or malignant and suggest a specific histological diagnosis when evaluating ultrasound images using pattern recognition.
METHODS: Ultrasound images of selected adnexal masses were evaluated by 3 expert sonologists, 2 senior and 4 junior trainees. They were instructed to classify the masses using pattern recognition as benign or malignant, to state the level of confidence with which this classification was made and to suggest a specific histological diagnosis. Sensitivity, specificity, accuracy and positive and negative likelihood ratios (LR+ and LR-) with regard to malignancy were calculated. The area under the receiver operating characteristic curve (AUC) of pattern recognition was calculated by using six levels of diagnostic confidence.
RESULTS: 166 masses were examined, of which 42% were malignant. Sensitivity with regard to malignancy ranged from 80 to 86% for the experts, was 70 and 84% for the 2 senior trainees and ranged from 70 to 86% for the junior trainees. The specificity of the experts ranged from 79 to 91%, was 77 and 89% for the senior trainees and ranged from 59 to 83% for the junior trainees. The experts were uncertain about their diagnosis in 4-13% of the cases, the senior trainees in 15-20% and the junior trainees in 67-100% of the cases. The AUCs ranged from 0.861 to 0.922 for the experts, were 0.842 and 0.855 for the senior trainees, and ranged from 0.726 to 0.795 for the junior trainees. The experts suggested a correct specific histological diagnosis in 69-77% of the cases. All 6 trainees did so significantly less often (22-42% of the cases).
CONCLUSION: Expert sonologists can accurately classify adnexal masses as benign or malignant and can successfully predict the specific histological diagnosis in many cases. Whilst less experienced operators perform reasonably well when predicting the benign or malignant nature of the mass, they do so with a very low level of diagnostic confidence and are unable to state the likely histology of a mass in most cases. Copyright 2009 S. Karger AG, Basel.

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Year:  2009        PMID: 20016188     DOI: 10.1159/000265012

Source DB:  PubMed          Journal:  Gynecol Obstet Invest        ISSN: 0378-7346            Impact factor:   2.031


  7 in total

Review 1.  Ovarian Adnexal Reporting Data System (O-RADS) for Classifying Adnexal Masses: A Systematic Review and Meta-Analysis.

Authors:  Julio Vara; Nabil Manzour; Enrique Chacón; Ana López-Picazo; Marta Linares; Maria Ángela Pascual; Stefano Guerriero; Juan Luis Alcázar
Journal:  Cancers (Basel)       Date:  2022-06-27       Impact factor: 6.575

2.  Management of the Adnexal Mass: Considerations for the Family Medicine Physician.

Authors:  Brian Bullock; Lisa Larkin; Lauren Turker; Kate Stampler
Journal:  Front Med (Lausanne)       Date:  2022-07-05

3.  Ultrasound of ovarian dermoids - sonographic findings of a dermoid cyst in a 41-year-old woman with an elevated serum hCG.

Authors:  Lauren Kite; Talat Uppal
Journal:  Australas J Ultrasound Med       Date:  2015-12-31

4.  An adolescent with an asymptomatic adnexal cyst: To worry or not to worry? Medical versus surgical management options.

Authors:  Vincenzo De Sanctis; Ashraf T Soliman; Heba Elsedfy; Nada A Soliman; Rania Elalaily; Salvatore Di Maio; Alaa Y Ahmed; Giuseppe Millimaggi
Journal:  Acta Biomed       Date:  2017-08-23

5.  Assessment of the diagnostic value of using serum CA125 and GI-RADS system in the evaluation of adnexal masses.

Authors:  Heng Zheng; Yan Tie; Xi Wang; Yang Yang; Xiawei Wei; Xia Zhao
Journal:  Medicine (Baltimore)       Date:  2019-02       Impact factor: 1.817

6.  Association between the sonographer's experience and diagnostic performance of IOTA simple rules.

Authors:  Chun-Ping Ning; Xiaoli Ji; Hong-Qiao Wang; Xiao-Ying Du; Hai-Tao Niu; Shi-Bao Fang
Journal:  World J Surg Oncol       Date:  2018-09-05       Impact factor: 2.754

7.  Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

Authors:  F Christiansen; E L Epstein; E Smedberg; M Åkerlund; K Smith; E Epstein
Journal:  Ultrasound Obstet Gynecol       Date:  2021-01       Impact factor: 7.299

  7 in total

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