Literature DB >> 26534858

A comparison between digital breast tomosynthesis and full-field digital mammography for the detection of breast cancers.

Woo Jung Choi1, Hak Hee Kim2, Sun Young Lee1, Eun Young Chae1, Hee Jung Shin1, Joo Hee Cha1, Byung Ho Son3, Sei Hyun Ahn3, Young-Wook Choi4.   

Abstract

PURPOSE: To evaluate interobserver agreement in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) in terms of both lesion detection and characterization, and to evaluate the cancer detection rate of standard two-view FFDM compared to various combinations of DBT.
MATERIALS AND METHODS: Thirty-five women (mean age 59.7; range 50-80 years) with 37 breast cancers who underwent both two-view DBT and two-view FFDM were included. DBT images were obtained using an investigational prototype. We performed interobserver agreement analyses using kappa (k) statistics. The cancer detection rate of various combinations of DBT compared to standard two-view FFDM was estimated using a generalized estimation equation.
RESULTS: There was fair to moderate agreement on detectability (k = 0.59-0.62) in both views of FFDM and DBT, while fair to substantial agreement was found for lesion location (k = 0.52-0.84) and fair to moderate agreement for lesion type (k = 0.46-0.70) and BI-RADS final assessment (k = 0.48-0.69). In generalized estimation equations, standard two-view FFDM was inferior to any combination of DBT. The detection rate ratio was significantly higher in the combined four views of DBT and FFDM compared to standard FFDM (p < 0.046).
CONCLUSION: Our study showed good agreement in lesion detection and characterization between FFDM and DBT images. Our findings also demonstrated that combining DBT and FFDM is superior in detecting cancer compared to standard FFDM.

Entities:  

Keywords:  Breast; Comparison; Digital breast tomosynthesis; Full-field digital mammography

Mesh:

Year:  2015        PMID: 26534858     DOI: 10.1007/s12282-015-0656-1

Source DB:  PubMed          Journal:  Breast Cancer        ISSN: 1340-6868            Impact factor:   4.239


  4 in total

1.  Decrease in interpretation time for both novice and experienced readers using a concurrent computer-aided detection system for digital breast tomosynthesis.

Authors:  Eun Young Chae; Hak Hee Kim; Ji-Wook Jeong; Seung-Hoon Chae; Sooyeul Lee; Young-Wook Choi
Journal:  Eur Radiol       Date:  2018-12-13       Impact factor: 5.315

2.  Visualization of Breast Microcalcifications on Digital Breast Tomosynthesis and 2-Dimensional Digital Mammography Using Specimens.

Authors:  Jieun Byun; Jee Eun Lee; Eun Suk Cha; Jin Chung; Jeoung Hyun Kim
Journal:  Breast Cancer (Auckl)       Date:  2017-04-12

3.  Diagnostic Performance of Digital Breast Tomosynthesis for Breast Suspicious Calcifications From Various Populations: A Comparison With Full-field Digital Mammography.

Authors:  Juntao Li; Hengwei Zhang; Hui Jiang; Xuhui Guo; Yinli Zhang; Dan Qi; Jitian Guan; Zhenzhen Liu; Erxi Wu; Suxia Luo
Journal:  Comput Struct Biotechnol J       Date:  2018-12-20       Impact factor: 7.271

4.  Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network.

Authors:  Bingbing Xiao; Haotian Sun; You Meng; Yunsong Peng; Xiaodong Yang; Shuangqing Chen; Zhuangzhi Yan; Jian Zheng
Journal:  Biomed Eng Online       Date:  2021-07-28       Impact factor: 2.819

  4 in total

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