Literature DB >> 23141024

Can the Gail model increase the predictive value of a positive mammogram in a European population screening setting? Results from a Spanish cohort.

A Buron1, M Vernet, M Roman, M A Checa, J M Pérez, M Sala, M Comas, C Murta-Nascimiento, X Castells, F Macià.   

Abstract

AIMS OF THE STUDY: The Gail Model (GM) is the most well-known model to assess the individual risk of breast cancer (BC). Although its discriminatory accuracy is low in the clinical context, its usefulness in the screening setting is not well known. The aim of this study is to assess the utility of the GM in a European screening program.
METHODS: Retrospective cohort study of 2200 reassessed women with information on the GM available in a BC screening program in Barcelona, Spain. The 5 year-risk of BC applying the GM right after the screening mammogram was compared first with the actual woman's risk of BC in the same screening round and second with the BC risk during the next 5 years.
RESULTS: The curves of BC Gail risk overlapped for women with and without BC, both in the same screening episode as well as 5 years afterward. Overall sensitivity and specificity in the same screening episode were 22.3 and 86.5%, respectively, and 46.2 and 72.1% 5 years afterward. ROC curves were barely over the diagonal and the concordance statistics were 0.59 and 0.61, respectively.
CONCLUSION: The GM has very low accuracy among women with a positive mammogram result, predicting BC both in the concomitant episode and 5 years later. Our results do not encourage the use of the GM in the screening context to aid the referral decision or the type of procedures after a positive mammogram or to identify women at high risk among those with a false-positive outcome.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 23141024     DOI: 10.1016/j.breast.2012.09.015

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  3 in total

1.  Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.

Authors:  Xin Wang; Yubei Huang; Lian Li; Hongji Dai; Fengju Song; Kexin Chen
Journal:  Breast Cancer Res       Date:  2018-03-13       Impact factor: 6.466

2.  An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain.

Authors:  Arantzazu Arrospide; Carles Forné; Montse Rué; Núria Torà; Javier Mar; Marisa Baré
Journal:  BMC Cancer       Date:  2013-12-10       Impact factor: 4.430

Review 3.  Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review.

Authors:  Solikhah Solikhah; Sitti Nurdjannah
Journal:  Heliyon       Date:  2020-04-22
  3 in total

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