scholarly journals Deep learning modeling using normal mammograms for predicting breast cancer risk

2019 ◽  
Vol 47 (1) ◽  
pp. 110-118 ◽  
Author(s):  
Dooman Arefan ◽  
Aly A. Mohamed ◽  
Wendie A. Berg ◽  
Margarita L. Zuley ◽  
Jules H. Sumkin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk

Toward robust mammography-based models for breast cancer risk

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Massachusetts General Hospital ◽  
The United States ◽  
University Hospital ◽  
Chang Gung Memorial Hospital ◽  
Risk Models

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation

2020 ◽  
Author(s):  
Stijn De Buck ◽  
Jeroen Bertels ◽  
Chelsey Vanbilsen ◽  
Tanguy Dewaele ◽  
Chantal Van Ongeval ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Estimation

scholarly journals Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231653 ◽  
Author(s):  
Suzanne C. Wetstein ◽  
Allison M. Onken ◽  
Christina Luffman ◽  
Gabrielle M. Baker ◽  
Michael E. Pyle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Learning Assessment ◽  
Terminal Duct Lobular Unit ◽  
Automated Prediction

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

Radiology ◽  
2020 ◽  
Vol 294 (2) ◽  
pp. 265-272 ◽  
Author(s):  
Karin Dembrower ◽  
Yue Liu ◽  
Hossein Azizpour ◽  
Martin Eklund ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Mammographic Density ◽  
Risk Score ◽  
Density Score

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

Radiology ◽  
2019 ◽  
Vol 292 (1) ◽  
pp. 60-66 ◽  
Author(s):  
Adam Yala ◽  
Constance Lehman ◽  
Tal Schuster ◽  
Tally Portnoi ◽  
Regina Barzilay
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction

scholarly journals Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

2021 ◽  
Vol 7 (6) ◽  
pp. 98
Author(s):  
João Mendes ◽  
Nuno Matela
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Machine Learning Algorithms ◽  
Cancer Risk Assessment ◽  
Cancer Statistics ◽  
Screening Programs ◽  
Breast Cancer Risk Assessment

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.

Breast Cancer Risk Prediction Using Deep Learning

Radiology ◽  
2021 ◽  
pp. 211446
Author(s):  
Min Sun Bae ◽  
Hyug-Gi Kim
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction
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