Pathologic characteristics of breast parenchyma in patients with hereditary breast carcinoma, including BRCA1 and BRCA2 mutation carriers

Cancer ◽  
2002 ◽  
Vol 97 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Camilo Adem ◽  
Carol Reynolds ◽  
Cheryl L. Soderberg ◽  
Jeffrey M. Slezak ◽  
Shannon K. McDonnell ◽  
...  
2011 ◽  
Vol 120 ◽  
pp. S22-S23
Author(s):  
B. Norquist ◽  
R. Garcia ◽  
K. Agnew ◽  
J. Gross ◽  
R. Swensen ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yue Jiao ◽  
Fabienne Lesueur ◽  
Chloé-Agathe Azencott ◽  
Maïté Laurent ◽  
Noura Mebirouk ◽  
...  

Abstract Background Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors. Methods To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named “PRL + ML”) combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network. Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988–0.992) than either PRL (range 0.916–0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants). Conclusions Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries.


2021 ◽  
Vol 132 ◽  
pp. S357-S358
Author(s):  
Shana Kim ◽  
Jan Lubinski ◽  
Tomasz Huzarski ◽  
Pal Moller ◽  
Susan Armel ◽  
...  

2015 ◽  
Vol 151 (3) ◽  
pp. 653-660 ◽  
Author(s):  
Tehillah S. Menes ◽  
Mary Beth Terry ◽  
David Goldgar ◽  
Irene L. Andrulis ◽  
Julia A. Knight ◽  
...  

2013 ◽  
Vol 103 (1) ◽  
pp. 34-40 ◽  
Author(s):  
L. Koskenvuo ◽  
C. Svarvar ◽  
S. Suominen ◽  
K. Aittomäki ◽  
T. Jahkola

2010 ◽  
Vol 126 (2) ◽  
pp. 521-527 ◽  
Author(s):  
Bella Kaufman ◽  
Yael Laitman ◽  
Elad Ziv ◽  
Ute Hamann ◽  
Diana Torres ◽  
...  

2011 ◽  
Vol 104 (9) ◽  
pp. 1384-1392 ◽  
Author(s):  
K Metcalfe ◽  
S Gershman ◽  
H T Lynch ◽  
P Ghadirian ◽  
N Tung ◽  
...  

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