scholarly journals Translate but validate: necessary steps in improving the use and utility of cancer risk models

2020 ◽  
Vol 31 (6) ◽  
pp. 537-540
Author(s):  
M. B. Terry
Keyword(s):  
Cancer Risk ◽  
Risk Models

Comparing Human and Animal Cancer Risk Models Using Multistage Theory: Exponential vs. Relative vs. Additive Risk

Risk Analysis ◽  
1991 ◽  
pp. 55-63
Author(s):  
Robert N. Brown ◽  
Carol Brignoli Gable ◽  
Linda K. Tollefson ◽  
Janet A. Springer ◽  
Ronald J. Lorentzen
Keyword(s):  
Cancer Risk ◽  
Risk Models ◽  
Additive 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).

scholarly journals Assessing Breast Cancer Risk Models in Marin County, a Population With High Rates of Delayed Childbirth

2014 ◽  
Vol 14 (3) ◽  
pp. 212-220.e1 ◽  
Author(s):  
Mark Powell ◽  
Farid Jamshidian ◽  
Kate Cheyne ◽  
Joanne Nititham ◽  
Lee Ann Prebil ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Models ◽  
Marin County

scholarly journals Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results

2019 ◽  
Vol 112 (4) ◽  
pp. 418-422
Author(s):  
Robert J MacInnis ◽  
Yuyan Liao ◽  
Julia A Knight ◽  
Roger L Milne ◽  
Alice S Whittemore ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Disease Incidence ◽  
Brca2 Mutation ◽  
Estimation Algorithm ◽  
Breast Cancers ◽  
Risk Models ◽  
Replication Studies

Abstract The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%–56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models.

scholarly journals Performance of Common Genetic Variants in Breast-Cancer Risk Models

2010 ◽  
Vol 362 (11) ◽  
pp. 986-993 ◽  
Author(s):  
Sholom Wacholder ◽  
Patricia Hartge ◽  
Ross Prentice ◽  
Montserrat Garcia-Closas ◽  
Heather Spencer Feigelson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Genetic Variants ◽  
Risk Models ◽  
Common Genetic Variants

scholarly journals Risk models for breast cancer

2019 ◽  
Vol 15 (4) ◽  
pp. 503-510
Author(s):  
T. V. Pyatchanina ◽  
A. N. Ohorodnyk
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Scientific Evidence ◽  
Breast Cancer Patients ◽  
Brca1 And Brca2 ◽  
Risk Models ◽  
Brca1 And Brca2 Genes

Scientific evidence indicates the stabilization of indicators of morbidity and mortality from breast cancer in women in Ukraine and the existence of a number of models for predicting the breast cancer risk with the consideration of life style factors, detectable mutations of BRCA1 and BRCA2 genes, family history, as well as predicative and prognostic factors (clinical, molecular-biological) to determine the possible ways of the tumor process and the survival of breast cancer patients.

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