Literature DB >> 28444533

Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications.

Jessica A Cintolo-Gonzalez1, Danielle Braun2,3, Amanda L Blackford4, Emanuele Mazzola3, Ahmet Acar5, Jennifer K Plichta6, Molly Griffin5, Kevin S Hughes5.   

Abstract

Numerous models have been developed to quantify the combined effect of various risk factors to predict either risk of developing breast cancer, risk of carrying a high-risk germline genetic mutation, specifically in the BRCA1 and BRCA2 genes, or the risk of both. These breast cancer risk models can be separated into those that utilize mainly hormonal and environmental factors and those that focus more on hereditary risk. Given the wide range of models from which to choose, understanding what each model predicts, the populations for which each is best suited to provide risk estimations, the current validation and comparative studies that have been performed for each model, and how to apply them practically is important for clinicians and researchers seeking to utilize risk models in their practice. This review provides a comprehensive guide for those seeking to understand and apply breast cancer risk models by summarizing the majority of existing breast cancer risk prediction models including the risk factors they incorporate, the basic methodology in their development, the information each provides, their strengths and limitations, relevant validation studies, and how to access each for clinical or investigative purposes.

Entities:  

Keywords:  Breast Cancer Screening; Risk Models; Risk assessment

Mesh:

Year:  2017        PMID: 28444533     DOI: 10.1007/s10549-017-4247-z

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  40 in total

1.  Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort.

Authors:  Anne Marie McCarthy; Zoe Guan; Michaela Welch; Molly E Griffin; Dorothy A Sippo; Zhengyi Deng; Suzanne B Coopey; Ahmet Acar; Alan Semine; Giovanni Parmigiani; Danielle Braun; Kevin S Hughes
Journal:  J Natl Cancer Inst       Date:  2020-05-01       Impact factor: 13.506

2.  Population frequencies of pathogenic alleles of BRCA1 and BRCA2: analysis of 173 Danish breast cancer pedigrees using the BOADICEA model.

Authors:  Thorkild Terkelsen; Lise-Lotte Christensen; Deirdre Cronin Fenton; Uffe Birk Jensen; Lone Sunde; Mads Thomassen; Anne-Bine Skytte
Journal:  Fam Cancer       Date:  2019-10       Impact factor: 2.375

3.  Breast cancer screening: in the era of personalized medicine, age is just a number.

Authors:  Andrea Cozzi; Simone Schiaffino; Paolo Giorgi Rossi; Francesco Sardanelli
Journal:  Quant Imaging Med Surg       Date:  2020-12

4.  Characterization of Cancer-Induced Nociception in a Murine Model of Breast Carcinoma.

Authors:  Amanda Spring de Almeida; Flávia Karine Rigo; Samira Dal-Toé De Prá; Alessandra Marcone Milioli; Diéssica Padilha Dalenogare; Gabriele Cheiran Pereira; Camila Dos Santos Ritter; Diulle Spat Peres; Caren Tatiane de David Antoniazzi; Carolina Stein; Rafael Noal Moresco; Sara Marchesan Oliveira; Gabriela Trevisan
Journal:  Cell Mol Neurobiol       Date:  2019-03-08       Impact factor: 5.046

5.  Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  J Natl Cancer Inst       Date:  2018-09-01       Impact factor: 13.506

6.  Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk.

Authors:  James Sun; Dung-Tsa Chen; Jiannong Li; Weihong Sun; Sean J Yoder; Tania E Mesa; Marek Wloch; Richard Roetzheim; Christine Laronga; M Catherine Lee
Journal:  J Surg Res       Date:  2019-08-13       Impact factor: 2.192

7.  Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.

Authors:  Despina Kontos; Stacey J Winham; Andrew Oustimov; Lauren Pantalone; Meng-Kang Hsieh; Aimilia Gastounioti; Dana H Whaley; Carrie B Hruska; Karla Kerlikowske; Kathleen Brandt; Emily F Conant; Celine M Vachon
Journal:  Radiology       Date:  2018-10-30       Impact factor: 11.105

8.  Derivation and Validation of a Risk Prediction Model for Vancomycin-Associated Acute Kidney Injury in Chinese Population.

Authors:  Nana Xu; Qiao Zhang; Guolan Wu; Duo Lv; Yunliang Zheng
Journal:  Ther Clin Risk Manag       Date:  2020-06-22       Impact factor: 2.423

Review 9.  Key steps for effective breast cancer prevention.

Authors:  Kara L Britt; Jack Cuzick; Kelly-Anne Phillips
Journal:  Nat Rev Cancer       Date:  2020-06-11       Impact factor: 60.716

10.  Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success.

Authors:  Erika A Waters; Jennifer M Taber; Amy McQueen; Ashley J Housten; Jamie L Studts; Laura D Scherer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-10-12       Impact factor: 4.254

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