Key steps for effective breast cancer prevention

KL Britt, J Cuzick, KA Phillips - Nature Reviews Cancer, 2020 - nature.com
Despite decades of laboratory, epidemiological and clinical research, breast cancer
incidence continues to rise. Breast cancer remains the leading cancer-related cause of …

An overview of mammographic density and its association with breast cancer

SS Nazari, P Mukherjee - Breast cancer, 2018 - Springer
In 2017, breast cancer became the most commonly diagnosed cancer among women in the
US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in …

A deep learning mammography-based model for improved breast cancer risk prediction

A Yala, C Lehman, T Schuster, T Portnoi, R Barzilay - Radiology, 2019 - pubs.rsna.org
Background Mammographic density improves the accuracy of breast cancer risk models.
However, the use of breast density is limited by subjective assessment, variation across …

Federated learning for breast density classification: A real-world implementation

HR Roth, K Chang, P Singh, N Neumark, W Li… - Domain Adaptation and …, 2020 - Springer
Building robust deep learning-based models requires large quantities of diverse training
data. In this study, we investigate the use of federated learning (FL) to build medical imaging …

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial

M Salim, Y Liu, M Sorkhei, D Ntoula, T Foukakis… - Nature Medicine, 2024 - nature.com
Screening mammography reduces breast cancer mortality, but studies analyzing interval
cancers diagnosed after negative screens have shown that many cancers are missed …

Mammographic breast density assessment using deep learning: clinical implementation

CD Lehman, A Yala, T Schuster, B Dontchos, M Bahl… - Radiology, 2019 - pubs.rsna.org
Purpose To develop a deep learning (DL) algorithm to assess mammographic breast
density. Materials and Methods In this retrospective study, a deep convolutional neural …

Artificial intelligence in the interpretation of breast cancer on MRI

D Sheth, ML Giger - Journal of Magnetic Resonance Imaging, 2020 - Wiley Online Library
Advances in both imaging and computers have led to the rise in the potential use of artificial
intelligence (AI) in various tasks in breast imaging, going beyond the current use in …

SALMON: survival analysis learning with multi-omics neural networks on breast cancer

Z Huang, X Zhan, S Xiang, TS Johnson, B Helm… - Frontiers in …, 2019 - frontiersin.org
Improved cancer prognosis is a central goal for precision health medicine. Though many
models can predict differential survival from data, there is a strong need for sophisticated …

Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study

RF Brem, L Tabár, SW Duffy, MF Inciardi, JA Guingrich… - Radiology, 2015 - pubs.rsna.org
Purpose To determine improvement in breast cancer detection by using supplemental three-
dimensional (3D) automated breast (AB) ultrasonography (US) with screening …

Screening for breast cancer with mammography

PC Gøtzsche, KJ Jørgensen - Cochrane database of …, 2013 - cochranelibrary.com
Screening for breast cancer with mammography - Gøtzsche, PC - 2013 | Cochrane Library Skip
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