Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Breast Cancer Research

Review

Angela Hardi¹  Akila Anandarajah²  Graham A. Colditz²  Shu Jiang²  Carolyn Stoll²  Yongzhen Chen³ 

[1] Bernard Becker Medical Library, Washington University School of Medicine, MSC 8132-12-01, 660 S Euclid Ave, 63110, Saint Louis, MO, USA;Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave MSC 8100-0094-2200, 63110, Saint Louis, MO, USA;Saint Louis University School of Medicine, Saint Louis, MO, USA;

关键词: Breast density;    Mammography;    Parenchymal patterns;    Risk prediction;    Texture;   

DOI  :  10.1186/s13058-022-01600-5

 received in 2021-12-11, accepted in 2022-12-21,  发布年份 2022

来源: Springer

【 摘 要 】

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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