Literature DB >> 30547203

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

Eun Young Chae1, Hak Hee Kim2, Ji-Wook Jeong3, Seung-Hoon Chae3, Sooyeul Lee3, Young-Wook Choi4.   

Abstract

OBJECTIVES: To compare the diagnostic performance and interpretation time of digital breast tomosynthesis (DBT) for both novice and experienced readers with and without using a computer-aided detection (CAD) system for concurrent read.
METHODS: CAD system was developed for concurrent read in DBT interpretation. In this observer performance study, we used an enriched sample of 100 DBT cases including 70 with and 30 without breast cancers. Image interpretation was performed by four radiologists with different experience levels (two experienced and two novice). Each reader completed two reading sessions (at a minimum 2-month interval), once with and once without CAD. Three different rating scales were used to record each reader's interpretation. Reader performance with and without CAD was reported and compared for each radiologist. Reading time for each case was also recorded.
RESULTS: Average area under the receiver operating characteristic curve values for BI-RADS scale on using CAD were 0.778 and 0.776 without using CAD, demonstrating no statistically significant differences. Results were consistent when the probability of malignancy and percentage probability of malignancy scales were used. Reading times per case were 72.07 s and 62.03 s (SD, 37.54 s vs 34.38 s) without and with CAD, respectively. The average difference in reading time on using CAD was a statistically significant decrease of 10.04 ± 1.85 s, providing 14% decrease in time. The time-reducing effect was consistently observed in both novice and experienced readers.
CONCLUSION: DBT combined with CAD reduced interpretation time without diagnostic performance loss to novice and experienced readers. KEY POINTS: • The use of a concurrent DBT-CAD system shortened interpretation time. • The shortened interpretation time with DBT-CAD did not come at a cost to diagnostic performance to novice or experienced readers. • The concurrent DBT-CAD system improved the efficiency of DBT interpretation.

Entities:  

Keywords:  Breast cancer; Computer-assisted diagnosis; Digital breast tomosynthesis

Mesh:

Year:  2018        PMID: 30547203     DOI: 10.1007/s00330-018-5886-0

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  23 in total

1.  Digital Breast Tomosynthesis: State of the Art.

Authors:  Srinivasan Vedantham; Andrew Karellas; Gopal R Vijayaraghavan; Daniel B Kopans
Journal:  Radiology       Date:  2015-12       Impact factor: 11.105

2.  Does Reader Performance with Digital Breast Tomosynthesis Vary according to Experience with Two-dimensional Mammography?

Authors:  Lorraine Tucker; Fiona J Gilbert; Susan M Astley; Amanda Dibden; Archana Seth; Juliet Morel; Sara Bundred; Janet Litherland; Herman Klassen; Gerald Lip; Hema Purushothaman; Hilary M Dobson; Linda McClure; Philippa Skippage; Katherine Stoner; Caroline Kissin; Ursula Beetles; Yit Yoong Lim; Emma Hurley; Jane Goligher; Rumana Rahim; Tanja J Gagliardi; Tamara Suaris; Stephen W Duffy
Journal:  Radiology       Date:  2017-03-13       Impact factor: 11.105

3.  Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial.

Authors:  Elizabeth A Rafferty; Jeong Mi Park; Liane E Philpotts; Steven P Poplack; Jules H Sumkin; Elkan F Halpern; Loren T Niklason
Journal:  Radiology       Date:  2012-11-20       Impact factor: 11.105

4.  Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program.

Authors:  Anne Marie McCarthy; Despina Kontos; Marie Synnestvedt; Kay See Tan; Daniel F Heitjan; Mitchell Schnall; Emily F Conant
Journal:  J Natl Cancer Inst       Date:  2014-10-13       Impact factor: 13.506

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

Authors:  Richard A Benedikt; Justin E Boatsman; Cynthia A Swann; Aaron D Kirkpatrick; Alicia Y Toledano
Journal:  AJR Am J Roentgenol       Date:  2017-10-24       Impact factor: 3.959

6.  Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography.

Authors:  Steven P Poplack; Tor D Tosteson; Christine A Kogel; Helene M Nagy
Journal:  AJR Am J Roentgenol       Date:  2007-09       Impact factor: 3.959

7.  Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD).

Authors:  Corinne Balleyguier; Julia Arfi-Rouche; Laurent Levy; Patrick R Toubiana; Franck Cohen-Scali; Alicia Y Toledano; Bruno Boyer
Journal:  Eur J Radiol       Date:  2017-10-24       Impact factor: 3.528

8.  Detection and characterization of breast lesions in a selective diagnostic population: diagnostic accuracy study for comparison between one-view digital breast tomosynthesis and two-view full-field digital mammography.

Authors:  Eun Young Chae; Hak Hee Kim; Joo Hee Cha; Hee Jung Shin; Woo Jung Choi
Journal:  Br J Radiol       Date:  2016-04-13       Impact factor: 3.039

9.  Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study.

Authors:  Kristina Lång; Ingvar Andersson; Aldana Rosso; Anders Tingberg; Pontus Timberg; Sophia Zackrisson
Journal:  Eur Radiol       Date:  2015-05-01       Impact factor: 5.315

10.  Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration.

Authors:  Per Skaane; Andriy I Bandos; Randi Gullien; Ellen B Eben; Ulrika Ekseth; Unni Haakenaasen; Mina Izadi; Ingvild N Jebsen; Gunnar Jahr; Mona Krager; Solveig Hofvind
Journal:  Eur Radiol       Date:  2013-04-04       Impact factor: 5.315

View more
  5 in total

Review 1.  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

2.  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 3.  [Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective].

Authors:  Ki Hwan Kim; Sang Hyup Lee
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2021-01-31

4.  Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs.

Authors:  Ana M Mota; Matthew J Clarkson; Pedro Almeida; Nuno Matela
Journal:  J Imaging       Date:  2022-08-29

5.  Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.

Authors:  Suzanne L van Winkel; Alejandro Rodríguez-Ruiz; Linda Appelman; Albert Gubern-Mérida; Nico Karssemeijer; Jonas Teuwen; Alexander J T Wanders; Ioannis Sechopoulos; Ritse M Mann
Journal:  Eur Radiol       Date:  2021-05-04       Impact factor: 5.315

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.