High-throughput Mammographic-density Measurement: a Tool for Risk Prediction of Breast Cancer

Overview

Journal Breast Cancer Res

Specialty Oncology

Date 2012 Aug 1

PMID 22846386

Citations 49

Authors

Jingmei Li

Laszlo Szekely

Louise Eriksson

Boel Heddson

Ann Sundbom

Kamila Czene

Per Hall

Keith Humphreys

Affiliations

Soon will be listed here.

Abstract

Introduction: Mammographic density (MD) is a strong, independent risk factor for breast cancer, but measuring MD is time consuming and reader dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image-processing software for the fully automated analysis of MD and penalized regression to construct a measure that mimics a well-established semiautomated measure (Cumulus). We also describe measures that incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk.

Methods: We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). The Pearson product-moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure, which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images.

Results: We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884; 95% CI, 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percentage density, significantly improved the fit of the logistic regression model of breast cancer risk (P = 0.0002).

Conclusions: Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.

Citing Articles

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

Acciavatti R, Lee S, Reig B, Moy L, Conant E, Kontos D Radiology. 2023; 306(3):e222575.

PMID: 36749212 PMC: 9968778. DOI: 10.1148/radiol.222575.

Evaluation of breast stiffness pathology based on breast compression during mammography: Proposal for novel breast stiffness scale classification.

Prokop J, Marsalek P, Sengul I, Pelikan A, Janoutova J, Horyl P Clinics (Sao Paulo). 2022; 77:100100.

PMID: 36137345 PMC: 9493386. DOI: 10.1016/j.clinsp.2022.100100.

Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.

Li Y, Yuan W, Fan M, Zheng B, Li L J Digit Imaging. 2022; 35(4):910-922.

PMID: 35262841 PMC: 9485387. DOI: 10.1007/s10278-019-00266-4.

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

Gastounioti A, Pantalone L, Scott C, Cohen E, Wu F, Winham S Radiology. 2021; 301(3):561-568.

PMID: 34519572 PMC: 8608738. DOI: 10.1148/radiol.2021210190.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study.

Warner E, Rice M, Zeleznik O, Fowler E, Murthy D, Vachon C NPJ Breast Cancer. 2021; 7(1):68.

PMID: 34059687 PMC: 8166859. DOI: 10.1038/s41523-021-00272-2.

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