Literature DB >> 29064756

Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study.

Richard A Benedikt1, Justin E Boatsman1, Cynthia A Swann1, Aaron D Kirkpatrick1, Alicia Y Toledano2.   

Abstract

OBJECTIVE: Digital breast tomosynthesis (DBT) is more accurate than full-field digital mammography alone but requires a longer reading time. A radiologist reader study evaluated the use of concurrent computer-aided detection (CAD) to shorten the reading time while maintaining interpretation performance.
MATERIALS AND METHODS: A CAD system was developed to detect suspicious soft-tissue densities in DBT planes. Abnormalities are extracted from the plane in which they are detected and blended into the corresponding synthetic image. The study used an enriched sample of 240 DBT cases with 68 malignancies in 61 patients. Twenty radiologists retrospectively reviewed all 240 cases in a multireader multicase crossover design to compare reading time and performance with and without CAD. The performance of CAD alone was also evaluated.
RESULTS: Reading time improved by 29.2% with CAD (95% CI, 21.1-36.5%; p < 0.01). Reader performance, measured by ROC AUC, was noninferior with CAD (p < 0.01). The mean AUC increased from 0.841 without to 0.850 with CAD (95% CI, -0.012 to 0.030). Mean sensitivity increased from 0.847 without to 0.871 with CAD (difference 95% CI, -0.005 to 0.055), showing a 0.033 increase in sensitivity for cases with soft-tissue densities (95% CI, -0.002 to 0.068). Mean specificity decreased from 0.527 without to 0.509 with CAD (difference 95% CI, -0.041 to 0.005), and mean recall rate for noncancers slightly increased from 0.474 without to 0.492 with CAD (difference 95% CI, -0.006 to 0.041).
CONCLUSION: Concurrent use of CAD with DBT resulted in 29.2% faster reading time, while maintaining reader interpretation performance.

Entities:  

Keywords:  breast cancer; computer-assisted diagnosis; diagnostic imaging; digital breast tomosynthesis; time studies

Mesh:

Year:  2017        PMID: 29064756     DOI: 10.2214/AJR.17.18185

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  13 in total

Review 1.  Digital Breast Tomosynthesis: Concepts and Clinical Practice.

Authors:  Alice Chong; Susan P Weinstein; Elizabeth S McDonald; Emily F Conant
Journal:  Radiology       Date:  2019-05-14       Impact factor: 11.105

2.  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

Review 3.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

Review 4.  CAD and AI for breast cancer-recent development and challenges.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

5.  Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting.

Authors:  Takayoshi Uematsu; Kazuaki Nakashima; Taiyo Leopoldo Harada; Hatsuko Nasu; Tatsuya Igarashi
Journal:  Breast Cancer       Date:  2022-08-24       Impact factor: 3.307

6.  Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

Authors:  Emily F Conant; Alicia Y Toledano; Senthil Periaswamy; Sergei V Fotin; Jonathan Go; Justin E Boatsman; Jeffrey W Hoffmeister
Journal:  Radiol Artif Intell       Date:  2019-07-31

Review 7.  A review on methods for diagnosis of breast cancer cells and tissues.

Authors:  Ziyu He; Zhu Chen; Miduo Tan; Sauli Elingarami; Yuan Liu; Taotao Li; Yan Deng; Nongyue He; Song Li; Juan Fu; Wen Li
Journal:  Cell Prolif       Date:  2020-06-12       Impact factor: 6.831

Review 8.  Synthesized Mammography: Clinical Evidence, Appearance, and Implementation.

Authors:  Melissa A Durand
Journal:  Diagnostics (Basel)       Date:  2018-04-04

9.  CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Authors:  Marcus A Badgeley; Manway Liu; Benjamin S Glicksberg; Mark Shervey; John Zech; Khader Shameer; Joseph Lehar; Eric K Oermann; Michael V McConnell; Thomas M Snyder; Joel T Dudley
Journal:  Bioinformatics       Date:  2019-05-01       Impact factor: 6.931

10.  Interpretation time for screening mammography as a function of the number of computer-aided detection marks.

Authors:  Tayler M Schwartz; Stephen L Hillis; Radhika Sridharan; Olga Lukyanchenko; William Geiser; Gary J Whitman; Wei Wei; Tamara Miner Haygood
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-03
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