4.7 Review

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

Related references

Note: Only part of the references are listed.
Review Radiology, Nuclear Medicine & Medical Imaging

Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications

Leslie R. Lamb et al.

Summary: This article reviews the current applications of artificial intelligence in screening mammography and discusses the potential implementation considerations and future development directions in clinical practice.

AMERICAN JOURNAL OF ROENTGENOLOGY (2022)

Article Oncology

Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model

Adam Yala et al.

Summary: Accurate risk assessment is crucial for the success of breast cancer screening programs. The AI-based Mirai model maintained its high accuracy across globally diverse test sets, demonstrating its potential in breast cancer management.

JOURNAL OF CLINICAL ONCOLOGY (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

Sarah Eskreis-Winkler et al.

Summary: This study aimed to develop a deep learning model for automated classification of background parenchymal enhancement (BPE) in breast MRI and compare its performance with current standard-of-care radiology report BPE designations. The results showed that the deep learning model significantly outperformed the radiology report in terms of performance and could provide more accurate BPE assessments.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparing Mammographic Density Assessed by Digital Breast Tomosynthesis or Digital Mammography: The Breast Cancer Surveillance Consortium

Jeffrey A. Tice et al.

Summary: This study investigated the consistency of Breast Imaging Reporting and Data System (BI-RADS) density reporting between digital breast tomosynthesis (DBT) and digital mammography (DM). It also evaluated the effectiveness of using DM and DBT to assess breast density as a risk factor for breast cancer. The results showed that there were no significant differences in density assessment between DBT and DM, and breast density had a similar impact on breast cancer risk when assessed using either method.

RADIOLOGY (2022)

Letter Oncology

Risk Assessment in Population-Based Breast Cancer Screening

Mikael Eriksson et al.

JOURNAL OF CLINICAL ONCOLOGY (2022)

Article Cell Biology

A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care

Mikael Eriksson et al.

Summary: Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and reduces false positives. A DBT-based risk model was developed and validated, which can predict the occurrence of breast cancer. By using imaging features and age for risk assessment, our model can assist radiologists in selecting women for clinical care, leading to earlier detection and improved prognoses.

SCIENCE TRANSLATIONAL MEDICINE (2022)

Review Oncology

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Aimilia Gastounioti et al.

Summary: This article summarizes the application of artificial intelligence in breast cancer risk assessment, including evaluations of breast density, inherent risk, and diagnosis. Furthermore, it discusses the challenges and future directions in AI-based analysis of breast imaging.

BREAST CANCER RESEARCH (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Background Parenchymal Uptake on Molecular Breast Imaging and Breast Cancer Risk: A Cohort Study

Carrie B. Hruska et al.

Summary: The study found that BPU on MBI is associated with breast cancer risk, especially in postmenopausal women with dense breasts. BPU can provide additional discriminatory information for predicting breast cancer risk.

AMERICAN JOURNAL OF ROENTGENOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk

Bethany L. Niell et al.

Summary: The use of quantitative BPE measures may outperform radiologist-assigned category in predicting breast cancer risk, especially at specific enhancement ratio thresholds like BPE%. Further research on risk prediction models incorporating quantitative measures is warranted.

AMERICAN JOURNAL OF ROENTGENOLOGY (2021)

Review Oncology

Performance of Digital Breast Tomosynthesis, Synthetic Mammography, and Digital Mammography in Breast Cancer Screening: A Systematic Review and Meta-Analysis

Mostafa Alabousi et al.

Summary: The study compared the performance of digital mammography (DM) alone, combined digital breast tomosynthesis (DBT) and DM, combined DBT and synthetic 2-dimensional mammography (S2D), and DBT alone in breast cancer screening. The results showed that combined DBT and S2D had the highest CDR and PPV1.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2021)

Article Biochemistry & Molecular Biology

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

William Lotter et al.

Summary: Breast cancer poses a global challenge and early detection through screening mammography is crucial. Recent advancements in applying deep learning to mammography have addressed key difficulties, enhancing accuracy and accessibility of screening mammography.

NATURE MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Factors Associated With Background Parenchymal Enhancement on Contrast-Enhanced Mammography

Zahra Karimi et al.

Summary: Women with dense breasts, younger age, premenopausal status, no history of endocrine therapy, and no history of breast cancer are more likely to have greater BPE. Targeting CEM to the last menstrual period is not indicated.

AMERICAN JOURNAL OF ROENTGENOLOGY (2021)

Article Computer Science, Artificial Intelligence

Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment

Omid Haji Maghsoudi et al.

Summary: Breast density is a crucial risk factor for breast cancer and affects the accuracy of screening mammography. An AI method has been introduced to estimate breast density, showing strong agreement with expert assessment and improved performance in breast cancer risk assessment compared to other methods.

MEDICAL IMAGE ANALYSIS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US

Su Hyun Lee et al.

Summary: The study revealed an association between the glandular tissue component in breast ultrasound and the risk of future breast cancer as well as lobular involution. Higher glandular tissue component was linked to increased cancer risk, while it showed an inverse relationship with lobular involution.

RADIOLOGY (2021)

Article Cell Biology

Toward robust mammography-based models for breast cancer risk

Adam Yala et al.

Summary: Improved breast cancer risk models like Mirai utilize deep learning to achieve earlier detection and less harm from screenings. By training on diverse datasets and demonstrating superior accuracy, Mirai outperforms prior models in identifying high-risk patients across different populations.

SCIENCE TRANSLATIONAL MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Breast cancer and background parenchymal enhancement at breast magnetic resonance imaging: a meta-analysis

Na Hu et al.

Summary: Based on a meta-analysis of 13 studies, it was found that women with high or moderate background parenchymal enhancement on breast magnetic resonance imaging have a significantly increased risk of breast cancer compared to the control group.

BMC MEDICAL IMAGING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study

Zhongtao Bao et al.

Summary: The study aimed to provide evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women. Heterogeneous and fibrous categories were higher among women with cytopathological confirmed breast cancer than those without confirmation. The heterogeneous category was identified as a high-risk ultrasonographic examination category, followed by the fibrous category. Sonographer physicians showed fair to good agreements in categorizing ultrasonic examinations.

BMC MEDICAL IMAGING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Interval Cancers after Negative Supplemental Screening Breast MRI Results in Women with a Personal History of Breast Cancer

Ga Ram Kim et al.

Summary: The study found that the performance of screening breast MRI in women with a personal history of breast cancer was sustained across multiple rounds. A first-degree family history of breast cancer, estrogen receptor- and progesterone receptor-negative primary cancers, and moderate or marked background parenchymal enhancement on MRI were independently associated with the risk of developing interval cancers.

RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response

Natsuko Onishi et al.

Summary: This study retrospectively analyzed the relationship between lack of background parenchymal enhancement (BPE) suppression during neoadjuvant chemotherapy and pathologic response in breast cancer patients. The results showed that in hormone receptor-positive breast cancer, nonsuppressed BPE was associated with lower rates of pathological complete response.

RADIOLOGY (2021)

Review Radiology, Nuclear Medicine & Medical Imaging

Background Parenchymal Enhancement on Breast MRI: A Comprehensive Review

Geraldine J. Liao et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning modeling using normal mammograms for predicting breast cancer risk

Dooman Arefan et al.

MEDICAL PHYSICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Background Parenchymal Enhancement at Contrast-Enhanced Spectral Mammography (CESM) as a Breast Cancer Risk Factor

Vera Sorin et al.

ACADEMIC RADIOLOGY (2020)

Article Medicine, General & Internal

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening

Christopher E. Comstock et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction

Karin Dembrower et al.

RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening

Mikael Eriksson et al.

RADIOLOGY (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Automated Breast Ultrasound Screening for Dense Breasts

Sung Hun Kim et al.

KOREAN JOURNAL OF RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk

Lars J. Grimm et al.

ACADEMIC RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset

Richard Ha et al.

ACADEMIC RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of Quantitative Magnetic Resonance Imaging Background Parenchymal Enhancement Parameters to Improve Determination of Individual Breast Cancer Risk

Diana L. Lam et al.

JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY (2019)

Editorial Material Oncology

Predict, Then Act: Moving Toward Tailored Prevention

Christiane K. Kuhl

JOURNAL OF CLINICAL ONCOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis

Aimilia Gastounioti et al.

RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

Tally Portnoi et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Adam Yala et al.

RADIOLOGY (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

The Association of Background Parenchymal Enhancement at Breast MRI with Breast Cancer: A Systematic Review and Meta-Analysis

Christopher M. Monioson et al.

RADIOLOGY (2019)

Article Medicine, General & Internal

Supplemental MRI Screening for Women with Extremely Dense Breast Tissue

Marije F. Bakker et al.

NEW ENGLAND JOURNAL OF MEDICINE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI

Ashirbani Saha et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Constance D. Lehman et al.

RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Effect of Background Parenchymal Enhancement on Cancer Risk Across Different High-Risk Patient Populations Undergoing Screening Breast MRI

Dorothy A. Sippo et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography

Irfanullah Haider et al.

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY (2018)

Article Oncology

Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI

Janice S. Sung et al.

BREAST CANCER RESEARCH AND TREATMENT (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Background Parenchymal Enhancement Over Exam Time in Patients With and Without Breast Cancer

Amy Melsaether et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

Strengths and Weaknesses of Synthetic Mammography in Screening

Linda Ratanaprasatporn et al.

RADIOGRAPHICS (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

Clinical Performance of Synthesized Two-dimensional Mammography Combined with Tomosynthesis in a Large Screening Population

Mireille P. Aujero et al.

RADIOLOGY (2017)

Article Oncology

Quantification of masking risk in screening mammography with volumetric breast density maps

Katharina Holland et al.

BREAST CANCER RESEARCH AND TREATMENT (2017)

Review Radiology, Nuclear Medicine & Medical Imaging

Evaluation of background parenchymal enhancement on breast MRI: a systematic review

Bianca Bignotti et al.

BRITISH JOURNAL OF RADIOLOGY (2017)

Article Medicine, General & Internal

Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice A Multicenter Observational Study

Brian L. Sprague et al.

ANNALS OF INTERNAL MEDICINE (2016)

Article Computer Science, Interdisciplinary Applications

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

Michiel Kallenberg et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Breast MRI background parenchymal enhancement (BPE) correlates with the risk of breast cancer

Michele Telegrafo et al.

MAGNETIC RESONANCE IMAGING (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

Kathleen R. Brandt et al.

RADIOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Implementation of Synthesized Two-dimensional Mammography in a Population-based Digital Breast Tomosynthesis Screening Program

Samantha P. Zuckerman et al.

RADIOLOGY (2016)

Article Acoustics

CORRELATION OF BREAST ULTRASOUND CLASSIFICATIONS WITH BREAST CANCER IN CHINESE WOMEN

Xin-Yan Hou et al.

ULTRASOUND IN MEDICINE AND BIOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Dense Breast Legislation in the United States: State of the States

Soudabeh Fazeli Dehkordy et al.

Journal of the American College of Radiology (2016)

Article Multidisciplinary Sciences

MRI Background Parenchymal Enhancement Is Not Associated with Breast Cancer

Barbara Bennani-Baiti et al.

PLOS ONE (2016)

Review Oncology

Raised mammographic density: causative mechanisms and biological consequences

Michael J. Sherratt et al.

BREAST CANCER RESEARCH (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

The relationship of breast density in mammography and magnetic resonance imaging in high-risk women and women with breast cancer

Marissa Albert et al.

CLINICAL IMAGING (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of contrast enhancement and diffusion-weighted magnetic resonance imaging in healthy and cancerous breast tissue

Gene Young Cho et al.

EUROPEAN JOURNAL OF RADIOLOGY (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

Ruey-Feng Chang et al.

MEDICAL PHYSICS (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

Using multiscale texture and density features for near-term breast cancer risk analysis

Wenqing Sun et al.

MEDICAL PHYSICS (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

Yuanjie Zheng et al.

MEDICAL PHYSICS (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

The Impact of Bilateral Salpingo-Oophorectomy on Breast MRI Background Parenchymal Enhancement and Fibroglandular Tissue

E. R. Price et al.

EUROPEAN RADIOLOGY (2014)

Article Oncology

Prevalence of Mammographically Dense Breasts in the United States

Brian L. Sprague et al.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2014)

Article Oncology

Ultrasonographic assessment of breast density

Won Hwa Kim et al.

BREAST CANCER RESEARCH AND TREATMENT (2013)

Review Radiology, Nuclear Medicine & Medical Imaging

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

Ioannis Sechopoulos

MEDICAL PHYSICS (2013)

Review Radiology, Nuclear Medicine & Medical Imaging

A review of breast tomosynthesis. Part I. The image acquisition process

Ioannis Sechopoulos

MEDICAL PHYSICS (2013)

Article Radiology, Nuclear Medicine & Medical Imaging

The Relationship of Mammographic Density and Age: Implications for Breast Cancer Screening

Cristina M. Checka et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2012)

Article Radiology, Nuclear Medicine & Medical Imaging

Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI

Valencia King et al.

EUROPEAN RADIOLOGY (2012)

Article Radiology, Nuclear Medicine & Medical Imaging

Background Parenchymal Enhancement at Breast MR Imaging and Breast Cancer Risk

Valencia King et al.

RADIOLOGY (2011)

Article Oncology

Association Between Mammographic Density and Age-Related Lobular Involution of the Breast

Karthik Ghosh et al.

JOURNAL OF CLINICAL ONCOLOGY (2010)

Article Oncology

Receiver Operating Characteristic Curve in Diagnostic Test Assessment

Jayawant N. Mandrekar

JOURNAL OF THORACIC ONCOLOGY (2010)

Article Oncology

Texture Features from Mammographic Images and Risk of Breast Cancer

Armando Manduca et al.

CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION (2009)

Article Oncology

Novel Breast Tissue Feature Strongly Associated With Risk of Breast Cancer

Kevin P. McKian et al.

JOURNAL OF CLINICAL ONCOLOGY (2009)

Article Medicine, General & Internal

Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer

Wendie A. Berg et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2008)

Article Radiology, Nuclear Medicine & Medical Imaging

Volumetric breast density evaluation from ultrasound tomography images

Carri K. Glide-Hurst et al.

MEDICAL PHYSICS (2008)

Review Oncology

Mammographic density, breast cancer risk and risk prediction

Celine M. Vachon et al.

BREAST CANCER RESEARCH (2007)

Article Oncology

Body size, mammographic density, and breast cancer risk

Norman F. Boyd et al.

CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION (2006)

Article Radiology, Nuclear Medicine & Medical Imaging

Operator dependence of physician-performed whole-breast US: Lesion detection and characterization

Wendie A. Berg et al.

RADIOLOGY (2006)

Article Oncology

Tamoxifen and breast density in women at increased risk of breast cancer

J Cuzick et al.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2004)

Article Oncology

Postmenopausal hormone therapy and change in mammographic density

GA Greendale et al.

JOURNAL OF THE NATIONAL CANCER INSTITUTE (2003)

Article Radiology, Nuclear Medicine & Medical Imaging

Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers

ZM Huo et al.

RADIOLOGY (2002)