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 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).

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

2020 ◽  
pp. canprevres.0154.2020
Author(s):  
Julian O. Kim ◽  
Daniel J. Schaid ◽  
Celine M. Vachon ◽  
Andrew Cooke ◽  
Fergus J. Couch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Endocrine Therapy ◽  
Risk Estimation ◽  
Risk Scores ◽  
Polygenic Risk

scholarly journals Detection of potential microcalcification clusters using multivendor for‐presentation digital mammograms for short‐term breast cancer risk estimation

2019 ◽  
Vol 46 (4) ◽  
pp. 1938-1946 ◽  
Author(s):  
Maya Alsheh Ali ◽  
Mikael Eriksson ◽  
Kamila Czene ◽  
Per Hall ◽  
Keith Humphreys
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Estimation ◽  
Short Term ◽  
Microcalcification Clusters

scholarly journals Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Aimilia Gastounioti ◽  
Meng-Kang Hsieh ◽  
Eric Cohen ◽  
Lauren Pantalone ◽  
Emily F. Conant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Estimation ◽  
Mammographic Parenchymal Patterns

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
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