Literature DB >> 31848103

Risk-based breast cancer screening strategies in women.

Elaine F Harkness1, Susan M Astley2, D Gareth Evans3.   

Abstract

The incidence of breast cancer continues to increase worldwide. Population-based screening is available in many countries but may not be the most efficient use of resources, thus interest in risk-based/stratified screening has grown significantly in recent years. An important part of risk-based screening is the incorporation of mammographic density (MD) and single nucleotide polymorphisms (SNPs) into risk prediction models to be combined with classical risk factors. In this article, we discuss different measures of MD and risk prediction models that are available. Risk-stratified screening options including supplemental or alternative screening modalities including digital breast tomosynthesis (DBT), automated ultrasound (ABUS) and magnetic resonance imaging (MRI) are discussed, as well as potential risk-based interventions (diet and lifestyle, chemoprevention and risk-reducing surgery). Furthermore, we look at risk feedback in practice and the cost-effectiveness and acceptability of risk-based screening, highlighting some of the current challenges.
Copyright © 2019. Published by Elsevier Ltd.

Entities:  

Keywords:  Breast cancer screening; Mammographic density; Risk prediction models; Risk stratification

Mesh:

Year:  2019        PMID: 31848103     DOI: 10.1016/j.bpobgyn.2019.11.005

Source DB:  PubMed          Journal:  Best Pract Res Clin Obstet Gynaecol        ISSN: 1521-6934            Impact factor:   5.237


  11 in total

1.  Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer.

Authors:  Yuanyuan Zheng; Kainan Wang; Ning Li; Qianran Zhang; Fengxi Chen; Man Li
Journal:  Front Surg       Date:  2022-05-17

2.  Association of Estrogen-Related Polygenetic Risk Scores with Breast Cancer and Interactions with Alcohol Intake, Early Menarche, and Nulligravida.

Authors:  Sang Shin Song; Suna Kang; Sunmin Park
Journal:  Asian Pac J Cancer Prev       Date:  2022-01-01

3.  Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study.

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4.  Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review.

Authors:  Ru Chen; Rongshou Zheng; Jiachen Zhou; Minjuan Li; Dantong Shao; Xinqing Li; Shengfeng Wang; Wenqiang Wei
Journal:  Front Public Health       Date:  2021-12-01

5.  Views of health professionals on risk-based breast cancer screening and its implementation in the Spanish National Health System: A qualitative discussion group study.

Authors:  Celmira Laza-Vásquez; Núria Codern-Bové; Àngels Cardona-Cardona; Maria José Hernández-Leal; Maria José Pérez-Lacasta; Misericòrdia Carles-Lavila; Montserrat Rué
Journal:  PLoS One       Date:  2022-02-04       Impact factor: 3.240

Review 6.  The current status of risk-stratified breast screening.

Authors:  Ash Kieran Clift; David Dodwell; Simon Lord; Stavros Petrou; Sir Michael Brady; Gary S Collins; Julia Hippisley-Cox
Journal:  Br J Cancer       Date:  2021-10-26       Impact factor: 9.075

Review 7.  Breast cancer screening and early diagnosis in Chinese women.

Authors:  Rui Ding; Yi Xiao; Miao Mo; Ying Zheng; Yi-Zhou Jiang; Zhi-Ming Shao
Journal:  Cancer Biol Med       Date:  2022-04-05       Impact factor: 5.347

8.  Association of genetic variants of FBXO32 and FOXO6 in the FOXO pathway with breast cancer risk.

Authors:  Haijiao Wang; Hongliang Liu; Lingling Zhao; Sheng Luo; Tomi Akinyemiju; Shelley Hwang; Ying Yue; Qingyi Wei
Journal:  Mol Carcinog       Date:  2021-07-01       Impact factor: 5.139

9.  Spectrochemical analysis of liquid biopsy harnessed to multivariate analysis towards breast cancer screening.

Authors:  Daniel L D Freitas; Ingrid M Câmara; Priscila P Silva; Nathália R S Wanderley; Maria B C Alves; Camilo L M Morais; Francis L Martin; Tirzah B P Lajus; Kassio M G Lima
Journal:  Sci Rep       Date:  2020-07-30       Impact factor: 4.379

Review 10.  Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning.

Authors:  Mahaly Baptiste; Sarah Shireen Moinuddeen; Courtney Lace Soliz; Hashimul Ehsan; Gen Kaneko
Journal:  Genes (Basel)       Date:  2021-05-12       Impact factor: 4.096

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