Evidence map›Paper›PMID 30052617›Full record

ArticleJournal of computer assisted tomography

Assessment of Quantitative Magnetic Resonance Imaging Background Parenchymal Enhancement Parameters to Improve Determination of Individual Breast Cancer Risk.

Diana L Lam, Daniel S Hippe, Averi E Kitsch, Savannah C Partridge, Habib Rahbar

Abstract readEvaluation Study
In one paragraph

Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Background parenchymal enhancement on breast MRI: A comprehensive review.Journal of magnetic resonance imaging : JMRI · 2020
    Review
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

5 authors.

Diana L LamFrom the Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA.
Daniel S Hippe
Averi E Kitsch
Savannah C Partridge
Habib Rahbar

Funding

MRI Characterization of Biological Risk of Ductal Carcinoma in SituR01CA203883 · NCI · UNIVERSITY OF WASHINGTON · PI RAHBAR, HABIBOLLAH · 2017 to 2021
$1.8M
NCI NIH HHS R01 CA203883
6 · The paper itself

Abstract

objectivesThe aims of this study were to identify optimal quantitative breast magnetic resonance imaging background parenchymal enhancement (BPE) parameters associated with breast cancer risk and compare performance to qualitative assessments.

methodsUsing a matched case-control cohort of 46 high-risk women who underwent screening magnetic resonance imaging (23 who developed breast cancer matched to 23 who did not), fibroglandular tissue area, BPE area, and intensity metrics (mean, SD, quartiles, skewness, and kurtosis) were quantitatively measured at varying enhancement thresholds. Optimal thresholds for discriminating between cancer and control cohorts were identified for each metric and performance summarized using area under the receiver operating characteristic curve.

resultsWomen who developed breast cancer exhibited greater BPE area (adjusted P = 0.004) and higher intensity statistics (adjusted P < 0.004, except skewness and kurtosis with P > 0.99) than did control subjects, with areas under the receiver operating characteristic curve ranging from 0.75 to 0.78 at optimized thresholds.

conclusionsElevated quantitative BPE parameters, related to both area and intensity of enhancement, are associated with breast cancer development.

Indexed as

AdultAgedBreastBreast NeoplasmsCase-Control StudiesCohort StudiesEvaluation Studies as TopicFemaleHumansMagnetic Resonance ImagingMiddle AgedRisk

Identifiers

PMID30052617
PMCPMC6331225

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.