Literature DB >> 25193778

Single reading with computer-aided detection performed by selected radiologists in a breast cancer screening program.

Xavier Bargalló1, Gorane Santamaría2, Montse Del Amo2, Pedro Arguis2, José Ríos3, Jaume Grau4, Marta Burrel2, Enrique Cores2, Martín Velasco2.   

Abstract

OBJECTIVES: To assess the impact of shifting from a standard double reading plus arbitration protocol to a single reading by experienced radiologists assisted by computer-aided detection (CAD) in a breast cancer screening program.
METHODS: This was a prospective study approved by the ethics committee. Data from 21,321 consecutive screening mammograms in incident rounds (2010-2012) were read following a single reading plus CAD protocol and compared with data from 47,462 consecutive screening mammograms in incident rounds (2004-2010) that were interpreted following a double reading plus arbitration protocol. For the single reading, radiologists were selected on the basis of the appraisement of their previous performance.
RESULTS: Period 2010-2012 vs. period 2004-2010: Cancer detection rate (CDR): 6.1‰ (95% confidence interval: 5.1-7.2) vs. 5.25‰; Recall rate (RR): 7.02% (95% confidence interval: 6.7-7.4) vs. 7.24% (selected readers before arbitration) and vs. 3.94 (all readers after arbitration); Predictive positive value of recall: 8.69% vs. 13.32%. Average size of invasive cancers: 14.6±9.5mm vs. 14.3±9.5mm. Stage: 0 (22.3/26.1%); I (59.2/50.8%); II (19.2/17.1%); III (3.1/3.3%); IV (0/1.9%). Specialized breast radiologists performed better than general radiologists.
CONCLUSIONS: The cancer detection rate of the screening program improved using a single reading protocol by experienced radiologists assisted by CAD, at the cost of a moderate increase of the recall rate mainly related to the lack of arbitration.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Breast; Breast neoplasms; Breast screening; Computer-assisted diagnosis; Mammography

Mesh:

Year:  2014        PMID: 25193778     DOI: 10.1016/j.ejrad.2014.08.010

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.

Authors:  Alyssa T Watanabe; Vivian Lim; Hoanh X Vu; Richard Chim; Eric Weise; Jenna Liu; William G Bradley; Christopher E Comstock
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

2.  Breast Cancer Diagnostic Efficacy in a Developing South-East Asian Country

Authors:  Rhianna L Jackson; Callan R Double; Hayden J Munro; Jessica Lynch; Kriscia A Tapia; Phuong Dung Trieu; Maram Alakhras; Aarthi Ganesan; Thuan Doan Do; Baolin Pauline Soh; Patrick C Brennan; Puslednik Puslednik
Journal:  Asian Pac J Cancer Prev       Date:  2019-03-26

3.  Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Authors:  Baptiste Vasey; Stephan Ursprung; Benjamin Beddoe; Elliott H Taylor; Neale Marlow; Nicole Bilbro; Peter Watkinson; Peter McCulloch
Journal:  JAMA Netw Open       Date:  2021-03-01

4.  Cost-Effectiveness of Double Reading versus Single Reading of Mammograms in a Breast Cancer Screening Programme.

Authors:  Margarita Posso; Misericòrdia Carles; Montserrat Rué; Teresa Puig; Xavier Bonfill
Journal:  PLoS One       Date:  2016-07-26       Impact factor: 3.240

5.  Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets.

Authors:  Daiju Ueda; Akira Yamamoto; Naoyoshi Onoda; Tsutomu Takashima; Satoru Noda; Shinichiro Kashiwagi; Tamami Morisaki; Shinya Fukumoto; Masatsugu Shiba; Mina Morimura; Taro Shimono; Ken Kageyama; Hiroyuki Tatekawa; Kazuki Murai; Takashi Honjo; Akitoshi Shimazaki; Daijiro Kabata; Yukio Miki
Journal:  PLoS One       Date:  2022-03-24       Impact factor: 3.240

  5 in total

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