Literature DB >> 35059595

The Value of Tyrer-Cuzick Versus Gail Risk Modeling in Predicting Benefit from Screening MRI in Breast Cancer.

Anthanasios Sevdalis1, Xiaoyan Deng2, Dipankar Bandyopadhyay2, Kandace P McGuire1.   

Abstract

OBJECTIVE: Breast cancer is the most commonly diagnosed malignancy in US women. Risk assessment tools such as the Gail and Tyrer-Cuzick (TC) models calculate risk for breast cancer based on modifiable and non-modifiable factors in order to guide screening and prevention for high-risk patients. Screening with magnetic resonance imaging (MRI) in addition to mammography is recommended in high-risk patients (>20% lifetime risk on TC or other familial based models). Currently, no published data indicate these recommendations improve cancer detection.
MATERIALS AND METHODS: With the aim to determine what percentage lifetime risk (LR%) is associated with a statistically significant increase in cancer detection, the Virginia Commonwealth University (VCU) breast imaging database was reviewed to identify patients who received screening MRI.
RESULTS: The receiver operating characteristics (ROC) curves for the Gail and TC models and the rate of cancer detection correlated to 20% LR% were calculated. The Gail model was considered the control model as it is NOT considered a validated screening tool for MRI. TC is not more accurate than Gail when predicting benefit of breast MRI screening. (area under the curve (AUC): 0.6841, 0.6543 respectively, p = 0.828). Univariate analysis failed to demonstrate a statistically significant relationship between the Gail or TC LR % and diagnosis of breast cancer when using 20% as the cutoff for high-risk classification (p = 1.0, 0.369 respectively). Neither the TC nor the Gail risk calculators demonstrated a significant correlation between risk and the likelihood of diagnosis of breast cancer when screened with MRI.
CONCLUSION: Larger cohort studies are necessary to determine the risk percentage most predictive of a breast cancer diagnosis using MRI as screening. ©Copyright 2022 by Turkish Federation of Breast Diseases Associations.

Entities:  

Keywords:  Breast cancer; MRI; breast cancer screening; risk factors

Year:  2021        PMID: 35059595      PMCID: PMC8734526          DOI: 10.4274/ejbh.galenos.2021.2021-8-2

Source DB:  PubMed          Journal:  Eur J Breast Health


  29 in total

1.  American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography.

Authors:  Debbie Saslow; Carla Boetes; Wylie Burke; Steven Harms; Martin O Leach; Constance D Lehman; Elizabeth Morris; Etta Pisano; Mitchell Schnall; Stephen Sener; Robert A Smith; Ellen Warner; Martin Yaffe; Kimberly S Andrews; Christy A Russell
Journal:  CA Cancer J Clin       Date:  2007 Mar-Apr       Impact factor: 508.702

2.  Which risk model to use? Clinical implications of the ACS MRI screening guidelines.

Authors:  Elissa M Ozanne; Brian Drohan; Phil Bosinoff; Alan Semine; Michael Jellinek; Claire Cronin; Frederick Millham; Dana Dowd; Taryn Rourke; Caroline Block; Kevin S Hughes
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2012-10-23       Impact factor: 4.254

3.  Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) model for breast cancer risk prediction in women with atypical hyperplasia.

Authors:  Judy C Boughey; Lynn C Hartmann; Stephanie S Anderson; Amy C Degnim; Robert A Vierkant; Carol A Reynolds; Marlene H Frost; V Shane Pankratz
Journal:  J Clin Oncol       Date:  2010-07-06       Impact factor: 44.544

4.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.

Authors:  M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill
Journal:  J Natl Cancer Inst       Date:  1989-12-20       Impact factor: 13.506

5.  Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer.

Authors:  Wendie A Berg; Lorena Gutierrez; Moriel S NessAiver; W Bradford Carter; Mythreyi Bhargavan; Rebecca S Lewis; Olga B Ioffe
Journal:  Radiology       Date:  2004-10-14       Impact factor: 11.105

6.  A breast cancer prediction model incorporating familial and personal risk factors.

Authors:  Jonathan Tyrer; Stephen W Duffy; Jack Cuzick
Journal:  Stat Med       Date:  2004-04-15       Impact factor: 2.373

Review 7.  Gynecological-endocrinological aspects in women carriers of BRCA1/2 gene mutations.

Authors:  A Doren; A Vecchiola; B Aguirre; P Villaseca
Journal:  Climacteric       Date:  2018-10-08       Impact factor: 3.005

Review 8.  Breast cancer and associated factors: a review.

Authors:  M R Ataollahi; J Sharifi; M R Paknahad; A Paknahad
Journal:  J Med Life       Date:  2015

9.  Performance of the Gail and Tyrer-Cuzick breast cancer risk assessment models in women screened in a primary care setting with the FHS-7 questionnaire.

Authors:  Fernanda Sales Luiz Vianna; Juliana Giacomazzi; Cristina Brinckmann Oliveira Netto; Luciana Neves Nunes; Maira Caleffi; Patricia Ashton-Prolla; Suzi Alves Camey
Journal:  Genet Mol Biol       Date:  2019-06-03       Impact factor: 1.771

10.  Comparative Analysis between the Gail, Tyrer-Cuzick and BRCAPRO Models for Breast Cancer Screening in Brazilian Population.

Authors:  Kely Paviani Stevanato; Raíssa Bocchi Pedroso; Pedro Iora; Lander Dos Santos; Fernando Castilho Pelloso; Willian Augusto de Melo; Maria Dalva de Barros Carvalho; Sandra Marisa Pelloso
Journal:  Asian Pac J Cancer Prev       Date:  2019-11-01
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