Literature DB >> 24768327

Recall rate of screening ultrasound with automated breast volumetric scanning (ABVS) in women with dense breasts: a first quarter experience.

Elizabeth Kagan Arleo1, Marwa Saleh2, Dana Ionescu2, Michele Drotman2, Robert J Min2, Keith Hentel2.   

Abstract

PURPOSE: The aim of this study was to determine the recall rate of screening ultrasound with automated breast volumetric scanning (ABVS) in women with dense breasts (BI-RADS density classification 3 or 4 on mammogram).
MATERIALS AND METHODS: In this retrospective cohort study, at the end of the "first quarter" (August-October 2013) of use, our practice database was searched for all ABVS examinations performed and specifically, the positive examinations (defined as abnormal/BI-RADS 0) for which patients were recalled for additional imaging evaluation with handheld ultrasound (HHUS); the latter group was reviewed with respect to final BI-RADS and pathology if relevant.
RESULTS: During the 3-month study time period, 558 ABVS studies were performed: 453 (81%) were initially BI-RADS 1 or 2 and 105 (19%) were BI-RADS 0-incomplete and recalled, corresponding with an overall recall rate of 19%; specifically, the recall rate trended down from 24.7% in August to 12.6% in October. To date, 98 of the 105 recalled women have returned for HHUS, with the resultant final BI-RADS as follows: 25/98=25% BI-RADS 1, 46/98=47% BI-RADS 2, 13/98=13% BI-RADS 3, 14/98=15% BI-RADS 4, and 0/98=0% BI-RADS 5. All biopsies performed to date of the ABVS-detected BI-RADS 4 lesions have yielded benign results, with the most common pathology being fibroadenoma.
CONCLUSION: The recall rate of screening ABVS in women with dense breasts at our institution was under 20% overall during its first quarter of use, and trended down from nearly 25% in the first month to under 13% in the third. The clinical implication is that ABVS does have a learning curve, but that is a potentially feasible way to meet the increasing demands for screening ultrasound in women with dense breasts.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  ABVS; Breast ultrasound; Dense breasts; Recall rate; Screening

Mesh:

Year:  2014        PMID: 24768327     DOI: 10.1016/j.clinimag.2014.03.012

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  7 in total

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Review 2.  Automated breast ultrasound: basic principles and emerging clinical applications.

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Journal:  Radiol Med       Date:  2017-08-28       Impact factor: 3.469

3.  Study on automatic detection and classification of breast nodule using deep convolutional neural network system.

Authors:  Feiqian Wang; Xiaotong Liu; Na Yuan; Buyue Qian; Litao Ruan; Changchang Yin; Ciping Jin
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4.  Testing a dual-modality system that combines full-field digital mammography and automated breast ultrasound.

Authors:  Christopher L Vaughan; Tania S Douglas; Qonita Said-Hartley; Roland V Baasch; James A Boonzaier; Brian C Goemans; John Harverson; Michael W Mingay; Shuaib Omar; Raphael V Smith; Nielen C Venter; Heidi S Wilson
Journal:  Clin Imaging       Date:  2015-12-03       Impact factor: 1.605

5.  Detecting Breast Cancer with a Dual-Modality Device.

Authors:  Kamila Padia; Tania S Douglas; Lydia L Cairncross; Roland V Baasch; Christopher L Vaughan
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Review 6.  Automatic breast ultrasound: state of the art and future perspectives.

Authors:  Luca Nicosia; Federica Ferrari; Anna Carla Bozzini; Antuono Latronico; Chiara Trentin; Lorenza Meneghetti; Filippo Pesapane; Maria Pizzamiglio; Nicola Balesetreri; Enrico Cassano
Journal:  Ecancermedicalscience       Date:  2020-06-23

7.  Breast ultrasound: recommendations for information to women and referring physicians by the European Society of Breast Imaging.

Authors:  Andrew Evans; Rubina M Trimboli; Alexandra Athanasiou; Corinne Balleyguier; Pascal A Baltzer; Ulrich Bick; Julia Camps Herrero; Paola Clauser; Catherine Colin; Eleanor Cornford; Eva M Fallenberg; Michael H Fuchsjaeger; Fiona J Gilbert; Thomas H Helbich; Karen Kinkel; Sylvia H Heywang-Köbrunner; Christiane K Kuhl; Ritse M Mann; Laura Martincich; Pietro Panizza; Federica Pediconi; Ruud M Pijnappel; Katja Pinker; Sophia Zackrisson; Gabor Forrai; Francesco Sardanelli
Journal:  Insights Imaging       Date:  2018-08-09
  7 in total

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