Literature DB >> 32051067

Comparison of Call-Back Rates between Digital Mammography and Digital Breast Tomosynthesis.

Anthony M Scott, Madison G Lashley, Nicholas B Drury, Paul S Dale.   

Abstract

The effect of mammographic screening on the natural history and evolution of breast cancer treatment cannot be overstated; however, despite intensive and resource consuming screening, advanced breast cancer is still diagnosed frequently. The development of three-dimensional mammography or digital breast tomosynthesis (DBT) has already demonstrated greater sensitivity in the diagnosis of breast pathology and effectiveness in identifying early breast cancers. In addition to being a more sensitive screening tool, other studies indicate DBT has a lower call-back rate when compared with traditional DM. This study compares call-back rates between these two screening tools. A single institution, retrospective review was conducted of almost 20,000 patient records who underwent digital mammography or DBT in the years 2016 to 2018. These charts were analyzed for documentation of imaging type, Breast Imaging Reporting and Data System 0 status, call-back status, and type of further imaging that was required. Charts for 19,863 patients were reviewed, 17,899 digital mammography examinations were conducted compared with 11,331 DBT examinations resulting in 1,066 and 689 Breast Imaging Reporting and Data System 0 studies, respectively. Of the DM call-backs, 82.08 per cent were recommended for additional radiographic imaging and 17.82 per cent for ultrasound imaging. In the DBT group, only 39.77 per cent of call-backs were recommended for additional radiographic imaging and 60.09 per cent for ultrasound imaging. Our data suggest that DBT results in less call-back for additional mammographic images as compared with digital mammography. DBT may offer benefits over DM, including less imaging before biopsy, less time before biopsy, quicker diagnosis, and improved patient satisfaction.

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Year:  2019        PMID: 32051067

Source DB:  PubMed          Journal:  Am Surg        ISSN: 0003-1348            Impact factor:   0.688


  1 in total

1.  Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.

Authors:  Nicolle Vigil; Madeline Barry; Arya Amini; Moulay Akhloufi; Xavier P V Maldague; Lan Ma; Lei Ren; Bardia Yousefi
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

  1 in total

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