Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
OBJECTIVE
To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles.


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).


CONCLUSIONS
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.
From the Department of Radiology (A.B.A., D.D., S.G., M.R., D.K.) and Department of Pathology and Laboratory Medicine (C.M., M.F.), University of Pennsylvania Perelman School of Medicine, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Ahmed Bilal
AB
Ashraf
Dania
D
Daye
Sara
S
Gavenonis
Carolyn
C
Mies
Michael
M
Feldman
Mark
M
Rosen
Despina
D
Kontos
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000473:pathology; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D020869:Gene Expression Profiling; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008207:Lymphatic Metastasis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D009364:Neoplasm Recurrence, Local; D010641:Phenotype; D012189:Retrospective Studies; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012306:Risk; D014481:United States
D003287:Contrast Media; C064925:gadodiamide; D019786:Gadolinium DTPA