Evidence map›Paper›PMID 29888046›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age.

Shara I Feld, Jun Fan, Ming Yuan, Yirong Wu, Kaitlin M Woo, Roxana Alexandridis, Elizabeth S Burnside

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022
    Article
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shara I FeldUniversity of Wisconsin Department of Radiology, Madison, WI.
Jun FanHong Kong Baptist University Department of Mathematics, Hong Kong, China.
Ming YuanColumbia University Department of Statistics, New York, NY.
Yirong WuUniversity of Wisconsin Department of Radiology, Madison, WI.
Kaitlin M WooUniversity of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI.
Roxana AlexandridisUniversity of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI.
Elizabeth S BurnsideUniversity of Wisconsin Department of Radiology, Madison, WI.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Christian Capitini · 1985 to 2026
$142.6M
Institutional Clinical and Translational Science AwardUL1TR000427 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2012 to 2016
$32.2M
TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Informatics Techniques to Optimize Mammography for Aging PopulationsR01CA165229 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2012 to 2016
$1.7M
Machine Learning for Improved Mammography ScreeningR01CA127379 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2007 to 2010
$1.4M
Integrating Machine Learning and Physician Expertise for Breast Cancer DiagnosisR01LM010921 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2011 to 2014
$1.2M
Mentoring And Research In Patient-Oriented Breast Cancer DiagnosisK24CA194251 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2015 to 2019
$963k
NCATS NIH HHS UL1 TR000427NCI NIH HHS K24 CA194251NCI NIH HHS P30 CA014520NCI NIH HHS R01 CA127379NCI NIH HHS R01 CA165229NIAID NIH HHS U54 AI117924NLM NIH HHS R01 LM010921
6 · The paper itself

Abstract

While screening and treatment have sharply reduced breast cancer mortality in the past 50 years, more targeted diagnostic testing may improve the accuracy and efficiency of care. Our retrospective, age-matched, case-control study evaluated the differential value of mammography and genetic variants to predict breast cancer depending on patient age. We developed predictive models using logistic regression with group lasso comparing (1) diagnostic mammography findings, (2) selected genetic variants, and (3) a combination of both. For women older than 60, mammography features were most predictive of breast cancer risk (imaging AUC = 0.74, genetic variants AUC = 0.54, combined AUC = 0.71). For women younger than 60 there is additional benefit to obtaining genetic testing (imaging AUC = 0.69, genetic variants AUC = 0.70, combined AUC = 0.72). In summary, genetic testing supplements mammography in younger women while mammography appears sufficient in older women for breast cancer risk prediction.

Identifiers

PMID29888046
PMCPMC5961791

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.