Literature DB >> 27793579

Inter-reader Variability in the Use of BI-RADS Descriptors for Suspicious Findings on Diagnostic Mammography: A Multi-institution Study of 10 Academic Radiologists.

Amie Y Lee1, Dorota J Wisner2, Shadi Aminololama-Shakeri3, Vignesh A Arasu2, Stephen A Feig4, Jonathan Hargreaves3, Haydee Ojeda-Fournier5, Lawrence W Bassett6, Colin J Wells6, Jade De Guzman5, Chris I Flowers7, Joan E Campbell4, Sarah L Elson2, Hanna Retallack2, Bonnie N Joe2.   

Abstract

RATIONALE AND
OBJECTIVES: The study aimed to determine the inter-observer agreement among academic breast radiologists when using the Breast Imaging Reporting and Data System (BI-RADS) lesion descriptors for suspicious findings on diagnostic mammography.
MATERIALS AND METHODS: Ten experienced academic breast radiologists across five medical centers independently reviewed 250 de-identified diagnostic mammographic cases that were previously assessed as BI-RADS 4 or 5 with subsequent pathologic diagnosis by percutaneous or surgical biopsy. Each radiologist assessed the presence of the following suspicious mammographic findings: mass, asymmetry (one view), focal asymmetry (two views), architectural distortion, and calcifications. For any identified calcifications, the radiologist also described the morphology and distribution. Inter-observer agreement was determined with Fleiss kappa statistic. Agreement was also calculated by years of experience.
RESULTS: Of the 250 lesions, 156 (62%) were benign and 94 (38%) were malignant. Agreement among the 10 readers was strongest for recognizing the presence of calcifications (k = 0.82). There was substantial agreement among the readers for the identification of a mass (k = 0.67), whereas agreement was fair for the presence of a focal asymmetry (k = 0.21) or architectural distortion (k = 0.28). Agreement for asymmetries (one view) was slight (k = 0.09). Among the categories of calcification morphology and distribution, reader agreement was moderate (k = 0.51 and k = 0.60, respectively). Readers with more experience (10 or more years in clinical practice) did not demonstrate higher levels of agreement compared to those with less experience.
CONCLUSIONS: Strength of agreement varies widely for different types of mammographic findings, even among dedicated academic breast radiologists. More subtle findings such as asymmetries and architectural distortion demonstrated the weakest agreement. Studies that seek to evaluate the predictive value of certain mammographic features for malignancy should take into consideration the inherent interpretive variability for these findings. Copyright Â
© 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  BI-RADS; Breast Imaging; Mammography

Mesh:

Year:  2016        PMID: 27793579     DOI: 10.1016/j.acra.2016.09.010

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  13 in total

1.  Characteristics of screen-detected cancers following concordant or discordant recalls at blinded double reading in biennial digital screening mammography.

Authors:  Angela M P Coolen; Joost R C Lameijer; Adri C Voogd; Marieke W J Louwman; Luc J Strobbe; Vivianne C G Tjan-Heijnen; Lucien E M Duijm
Journal:  Eur Radiol       Date:  2018-06-25       Impact factor: 5.315

2.  Inter-rater reliability in the radiological classification of renal injuries.

Authors:  Elias J Pretorius; Amir D Zarrabi; Stephanie Griffith-Richards; Justin Harvey; Hilgard M Ackermann; Catharina M Meintjes; Willem G Cilliers; Moleen Zunza; Alexander J Szpytko; Richard D Pitcher
Journal:  World J Urol       Date:  2018-01-02       Impact factor: 4.226

3.  Nonpalpable breast lesions: impact of a second-opinion review at a breast unit on BI-RADS classification.

Authors:  Constance de Margerie-Mellon; Jean-Baptiste Debry; Axelle Dupont; Caroline Cuvier; Sylvie Giacchetti; Luis Teixeira; Marc Espié; Cédric de Bazelaire
Journal:  Eur Radiol       Date:  2021-01-18       Impact factor: 5.315

4.  Impact of artificial intelligence in breast cancer screening with mammography.

Authors:  Lan-Anh Dang; Emmanuel Chazard; Edouard Poncelet; Teodora Serb; Aniela Rusu; Xavier Pauwels; Clémence Parsy; Thibault Poclet; Hugo Cauliez; Constance Engelaere; Guillaume Ramette; Charlotte Brienne; Sofiane Dujardin; Nicolas Laurent
Journal:  Breast Cancer       Date:  2022-06-28       Impact factor: 3.307

5.  Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.

Authors:  Gopichandh Danala; Bhavika Patel; Faranak Aghaei; Morteza Heidari; Jing Li; Teresa Wu; Bin Zheng
Journal:  Ann Biomed Eng       Date:  2018-05-10       Impact factor: 3.934

6.  Are All Views with and without Displacement Maneuver Necessary in Augmentation Mammography? Putting Numbers Into Perspective.

Authors:  Lilian Soares Couto; Ruffo Freitas-Junior; Rosangela Silveira Corrêa; Macelo Vilela Lauar; Selma Pace Bauab; Linei Augusta Brolini Dellê Urban; Jorge Luiz Oliveira Cruvinel-Filho; Leonardo Ribeiro Soares; Ricardo Francalacci Savaris
Journal:  Asian Pac J Cancer Prev       Date:  2022-01-01

7.  Positive Predictive Value of Tomosynthesis-guided Biopsies of Architectural Distortions Seen on Digital Breast Tomosynthesis and without an Ultrasound Correlate.

Authors:  Gopal R Vijayaraghavan; Adrienne Newburg; Srinivasan Vedantham
Journal:  J Clin Imaging Sci       Date:  2019-11-18

8.  A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis.

Authors:  Daesung Kang; Hye Mi Gweon; Na Lae Eun; Ji Hyun Youk; Jeong-Ah Kim; Eun Ju Son
Journal:  Sci Rep       Date:  2021-12-14       Impact factor: 4.379

Review 9.  Screening of cancer predisposition syndromes.

Authors:  Haifa Al-Sarhani; Ravi V Gottumukkala; Angelo Don S Grasparil; Eric L Tung; Michael S Gee; Mary-Louise C Greer
Journal:  Pediatr Radiol       Date:  2021-04-01

10.  A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms.

Authors:  Said Boumaraf; Xiabi Liu; Chokri Ferkous; Xiaohong Ma
Journal:  Biomed Res Int       Date:  2020-05-11       Impact factor: 3.411

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