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


Journal

Journal of computer assisted tomography
ISSN: 1532-3145
Titre abrégé: J Comput Assist Tomogr
Pays: United States
ID NLM: 7703942

Informations de publication

Date de publication:
Historique:
pubmed: 28 7 2018
medline: 19 1 2019
entrez: 28 7 2018
Statut: ppublish

Résumé

The 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. Using 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. Women 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. Elevated quantitative BPE parameters, related to both area and intensity of enhancement, are associated with breast cancer development.

Identifiants

pubmed: 30052617
doi: 10.1097/RCT.0000000000000774
pmc: PMC6331225
mid: NIHMS970992
doi:

Types de publication

Evaluation Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

85-92

Subventions

Organisme : NCI NIH HHS
ID : R01 CA203883
Pays : United States

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Auteurs

Diana L Lam (DL)

From the Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA.

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Classifications MeSH