Literature DB >> 12586794

Application of breast cancer risk prediction models in clinical practice.

Susan M Domchek1, Andrea Eisen, Kathleen Calzone, Jill Stopfer, Anne Blackwood, Barbara L Weber.   

Abstract

Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all levels of risk. In addition, the likelihood that breast cancer risk is due to specific genetic susceptibility (such as BRCA1 or BRCA2 mutations) can be determined. Recent developments have reinforced the clinical importance of breast cancer risk assessment. Tamoxifen chemoprevention as well as prevention studies such as the Study of Tamoxifen and Raloxifene are available to women at increased risk of developing breast cancer. In addition, specific management strategies are now defined for BRCA1 and BRCA2 mutation carriers. Risk may be assessed as the likelihood of developing breast cancer (using risk assessment models) or as the likelihood of detecting a BRCA1 or BRCA2 mutation (using prior probability models). Each of the models has advantages and disadvantages, and all need to be interpreted in context. We review available risk assessment tools and discuss their application. As illustrated by clinical examples, optimal counseling may require the use of several models, as well as clinical judgment, to provide the most accurate and useful information to women and their families.

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Year:  2003        PMID: 12586794     DOI: 10.1200/JCO.2003.07.007

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  55 in total

1.  Pre-test prediction models of BRCA1 or BRCA2 mutation in breast/ovarian families attending familial cancer clinics.

Authors:  M de la Hoya; O Díez; P Pérez-Segura; J Godino; J M Fernández; J Sanz; C Alonso; M Baiget; E Díaz-Rubio; T Caldés
Journal:  J Med Genet       Date:  2003-07       Impact factor: 6.318

2.  A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRCAPRO.

Authors:  D G R Evans; D M Eccles; N Rahman; K Young; M Bulman; E Amir; A Shenton; A Howell; F Lalloo
Journal:  J Med Genet       Date:  2004-06       Impact factor: 6.318

3.  Linkage of a pedigree drawing program and database to a program for determining BRCA mutation carrier probability.

Authors:  Sharon R Sand; David S DeRam; Deborah J MacDonald; Kathleen R Blazer; Jeffrey N Weitzel
Journal:  Fam Cancer       Date:  2005       Impact factor: 2.375

4.  Concerns about cancer risk and experiences with genetic testing in a diverse population of patients with breast cancer.

Authors:  Reshma Jagsi; Kent A Griffith; Allison W Kurian; Monica Morrow; Ann S Hamilton; John J Graff; Steven J Katz; Sarah T Hawley
Journal:  J Clin Oncol       Date:  2015-04-06       Impact factor: 44.544

5.  Comparison between CaGene 5.1 and 6.0 for BRCA1/2 mutation prediction: a retrospective study of 150 BRCA1/2 genetic tests in 517 families with breast/ovarian cancer.

Authors:  Ivana Antonucci; Martina Provenzano; Luca Sorino; Michela Balsamo; Gitana Maria Aceto; Pasquale Battista; David Euhus; Ettore Cianchetti; Patrizia Ballerini; Clara Natoli; Giandomenico Palka; Liborio Stuppia
Journal:  J Hum Genet       Date:  2016-12-08       Impact factor: 3.172

6.  Effect of misreported family history on Mendelian mutation prediction models.

Authors:  Hormuzd A Katki
Journal:  Biometrics       Date:  2006-06       Impact factor: 2.571

7.  Contribution of the Defective BRCA1, BRCA2 and CHEK2 Genes to the Familial Aggregation of Breast Cancer: a Simulation Study Based on the Swedish Family-Cancer Database.

Authors:  Justo Lorenzo Bermejo; Alfonso García Pérez; Kari Hemminki
Journal:  Hered Cancer Clin Pract       Date:  2004-11-15       Impact factor: 2.857

8.  Chronic hepatitis B: whom to treat and for how long? Propositions, challenges, and future directions.

Authors:  Sang Hoon Ahn; Henry L Y Chan; Pei-Jer Chen; Jun Cheng; Mahesh K Goenka; Jinlin Hou; Seng Gee Lim; Masao Omata; Teerha Piratvisuth; Qing Xie; Hyung Joon Yim; Man-Fung Yuen
Journal:  Hepatol Int       Date:  2010-02-20       Impact factor: 6.047

Review 9.  Management of genetic syndromes predisposing to gynecologic cancers.

Authors:  Susan Miesfeldt; Amanda Lamb; Christine Duarte
Journal:  Curr Treat Options Oncol       Date:  2013-03

10.  Applications of machine learning in cancer prediction and prognosis.

Authors:  Joseph A Cruz; David S Wishart
Journal:  Cancer Inform       Date:  2007-02-11
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