Literature DB >> 19863409

Computer-aided detection in digital mammography: false-positive marks and their reproducibility in negative mammograms.

Seung Ja Kim1, Woo Kyung Moon, Min Hyun Seong, Nariya Cho, Jung Min Chang.   

Abstract

BACKGROUND: There are relatively few studies reporting the frequency of false-positive computer-aided detection (CAD) marks and their reproducibility in normal cases.
PURPOSE: To evaluate retrospectively the false-positive mark rate of a CAD system and the reproducibility of false-positive marks in two sets of negative digital mammograms.
MATERIAL AND METHODS: Two sets of negative digital mammograms were obtained in 360 women (mean age 57 years, range 30-76 years) with an approximate interval of 1 year (mean time 343.7 days), and a CAD system was applied. False-positive CAD marks and the reproducibility were determined.
RESULTS: Of the 360 patients, 252 (70.0%) and 240 (66.7%) patients had 1-7 CAD marks on the initial and second mammograms, respectively. The false-positive CAD mark rate was 1.5 (1.1 for masses and 0.4 for calcifications) and 1.4 (1.0 for masses and 0.4 for calcifications) per examination in the initial and second mammograms, respectively. The reproducibility of the false-positive CAD marks was 12.0% for both mass (81/680) and microcalcification (33/278) marks.
CONCLUSION: False-positive CAD marks were seen in approximately 70% of normal cases. However, the reproducibility was very low. Radiologists must be familiar with the findings of false-positive CAD marks, since they are very common and can increase the recall rate in screening.

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Year:  2009        PMID: 19863409     DOI: 10.3109/02841850903216700

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  3 in total

1.  CADe system integrated within the electronic health record.

Authors:  Noelia Vállez; Gloria Bueno; Óscar Déniz; María del Milagro Fernández; Carlos Pastor; Miguel Ángel Rienda; Pablo Esteve; María Arias
Journal:  Biomed Res Int       Date:  2013-09-17       Impact factor: 3.411

2.  Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis.

Authors:  Kiwook Kim; Sungwon Kim; Young Han Lee; Seung Hyun Lee; Hye Sun Lee; Sungjun Kim
Journal:  Sci Rep       Date:  2018-09-03       Impact factor: 4.379

3.  Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign.

Authors:  Kendra A Batchelder; Aaron B Tanenbaum; Seth Albert; Lyne Guimond; Pierre Kestener; Alain Arneodo; Andre Khalil
Journal:  PLoS One       Date:  2014-09-15       Impact factor: 3.240

  3 in total

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