Literature DB >> 20863639

Impact of a CAD system in a screen-film mammography screening program: a prospective study.

S Sanchez Gómez1, M Torres Tabanera, A Vega Bolivar, M Sainz Miranda, A Baroja Mazo, M Ruiz Diaz, P Martinez Miravete, E Lag Asturiano, P Muñoz Cacho, T Delgado Macias.   

Abstract

OBJECTIVE: The purpose of our study was to perform a prospective assessment of the impact of a CAD system in a screen-film mammography screening program during a period of 3 years.
MATERIALS AND METHODS: Our study was carried out on a population of 21,855 asymptomatic women (45-65 years). Mammograms were processed in a CAD system and independently interpreted by one of six radiologists. We analyzed the following parameters: sensitivity of radiologist's interpretation (without and with CAD), detection increase, recall rate and positive predictive value of biopsy, CAD's marks, radiologist's false negatives and comparative analysis of carcinomas detected and non-detected by CAD.
RESULTS: Detection rate was 4.3‰. CAD supposed an increase of 0.1‰ in detection rate and 1% in the total number of cases (p<0.005). The impact on recall rate was not significant (0.4%) and PPV of percutaneous biopsy was unchanged by CAD (20.23%). CAD's marks were 2.7 per case and 0.7 per view. Radiologist's false negatives were 13 lesions which were initially considered as CAD's false positives.
CONCLUSIONS: CAD supposed a significant increase in detection, without modifications in recall rates and PPV of biopsy. However, better results could have been achieved if radiologists had considered actionable those cases marked by CAD but initially misinterpreted.
Copyright © 2010. Published by Elsevier Ireland Ltd.

Entities:  

Mesh:

Year:  2010        PMID: 20863639     DOI: 10.1016/j.ejrad.2010.08.031

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


  4 in total

Review 1.  Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review.

Authors:  Edward Azavedo; Sophia Zackrisson; Ingegerd Mejàre; Marianne Heibert Arnlind
Journal:  BMC Med Imaging       Date:  2012-07-24       Impact factor: 1.930

Review 2.  Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review.

Authors:  Saleem Z Ramadan
Journal:  J Healthc Eng       Date:  2020-03-12       Impact factor: 2.682

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

Review 4.  Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

Authors:  Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli
Journal:  EXCLI J       Date:  2017-02-20       Impact factor: 4.068

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.