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Identification of intrinsic imaging phenotypes for breast cancer tumors:
preliminary associations with gene expression profiles
#MMPMID24702725
Ashraf AB
; Daye D
; Gavenonis S
; Mies C
; Feldman M
; Rosen M
; Kontos D
Radiology
2014[Aug]; 272
(2
): 374-84
PMID24702725
show ga
PURPOSE: To present a method for identifying intrinsic imaging phenotypes in
breast cancer tumors and to investigate their association with prognostic gene
expression profiles. MATERIALS AND METHODS: The authors retrospectively analyzed
dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images of the
breast in 56 women (mean age, 55.6 years; age range, 37-74 years) diagnosed with
estrogen receptor-positive breast cancer between 2005 and 2010. The study was
approved by the institutional review board and compliant with HIPAA. The
requirement to obtain informed consent was waived. Primary tumors were assayed
with a validated gene expression assay that provides a score for the likelihood
of recurrence. A multiparametric imaging phenotype vector was extracted for each
tumor by using quantitative morphologic, kinetic, and spatial heterogeneity
features. Multivariate linear regression was performed to test associations
between DCE MR imaging features and recurrence likelihood. To identify intrinsic
imaging phenotypes, hierarchical clustering was performed on the extracted
feature vectors. Multivariate logistic regression was used to classify tumors at
high versus low or medium risk of recurrence. To determine the additional value
of intrinsic phenotypes, the phenotype category was tested as an additional
variable. Receiver operating characteristic analysis and the area under the
receiver operating characteristic curve (Az) were used to assess classification
performance. RESULTS: There was a moderate correlation (r = 0.71, R(2) = 0.50, P
< .001) between DCE MR imaging features and the recurrence score. DCE MR imaging
features were predictive of recurrence risk as determined by the surrogate assay,
with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected,
with two including only low- and medium-risk tumors. When the phenotype category
was used as an additional variable, the Az increased to 0.82 (P < .01).
CONCLUSION: Intrinsic imaging phenotypes exist for breast cancer tumors and
correlate with recurrence likelihood as determined with gene expression
profiling. These imaging biomarkers could ultimately help guide treatment
decisions.
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