Literature DB >> 30807927

Double reading of automated breast ultrasound with digital mammography or digital breast tomosynthesis for breast cancer screening.

Janie M Lee1, Savannah C Partridge2, Geraldine J Liao2, Daniel S Hippe2, Adrienne E Kim2, Christoph I Lee2, Habib Rahbar2, John R Scheel2, Constance D Lehman3.   

Abstract

PURPOSE: To evaluate the impact of double reading automated breast ultrasound (ABUS) when added to full field digital mammography (FFDM) or digital breast tomosynthesis (DBT) for breast cancer screening.
METHODS: From April 2014 to June 2015, 124 women with dense breasts and intermediate to high breast cancer risk were recruited for screening with FFDM, DBT, and ABUS. Readers used FFDM and DBT in clinical practice and received ABUS training prior to study initiation. FFDM or DBT were first interpreted alone by two independent readers and then with ABUS. All recalled women underwent diagnostic workup with at least one year of follow-up. Recall rates were compared using the sign test; differences in outcomes were evaluated using Fisher's exact test.
RESULTS: Of 121 women with complete follow-up, all had family (35.5%) or personal (20.7%) history of breast cancer, or both (43.8%). Twenty-four women (19.8%) were recalled by at least one modality. Recalls increased from 5.0% to 13.2% (p = 0.002) when ABUS was added to FFDM and from 3.3% to 10.7% (p = 0.004) when ABUS was added to DBT. Findings recalled by both readers were more likely to result in a recommendation for short term follow-up imaging or tissue biopsy compared to findings recalled by only one reader (100% vs. 42.1%, p = 0.041). The cancer detection rate was 8.3 per 1000 screens (1/121); mode of detection: FFDM and DBT.
CONCLUSIONS: Adding ABUS significantly increased the recall rate of both FFDM and DBT screening. Double reading of ABUS during early phase adoption may reduce false positive recalls.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Automated breast ultrasound; Breast cancer screening; Digital breast tomosynthesis; Full field digital mammography

Mesh:

Year:  2019        PMID: 30807927     DOI: 10.1016/j.clinimag.2019.01.019

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


  4 in total

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Authors:  R Jared Weinfurtner; Melissa Anne Mallory; Dayana Bermudez
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2.  DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Authors:  Xiaobo Lai; Weiji Yang; Ruipeng Li
Journal:  Comput Math Methods Med       Date:  2020-01-28       Impact factor: 2.238

3.  Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.

Authors:  Eduardo Fleury; Karem Marcomini
Journal:  Eur Radiol Exp       Date:  2019-08-05

4.  Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer.

Authors:  Hongxu Zhang; Haiwang Liu; Lihui Ma; Jianping Liu; Dawei Hu
Journal:  J Healthc Eng       Date:  2021-09-17       Impact factor: 2.682

  4 in total

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