  Purpose-Optimal use of established and imaging methods, such as
multiparametric magnetic resonance imaging(mpMRI) can simultaneously identify
key functional parameters and provide unique imaging phenotypes of breast
cancer. Therefore, we have developed and implemented a new machine-learning
informatic system that integrates clinical variables, derived from imaging and
clinical health records, to compare with the 21-gene array assay, OncotypeDX.
Materials and methods-We tested our informatics modeling in a subset of
patients (n=81) who had ER+ disease and underwent OncotypeDX gene expression
and breast mpMRI testing. The machine-learning informatic method is termed
Integrated Radiomic Informatic System-IRIS was applied to the mpMRI, clinical
and pathologic descriptors, as well as a gene array analysis. The IRIS method
using an advanced graph theoretic model and quantitative metrics. Summary
statistics (mean and standard deviations) for the quantitative imaging
parameters were obtained. Sensitivity and specificity and Area Under the Curve
were calculated for the classification of the patients. Results-The OncotypeDX
classification by IRIS model had sensitivity of 95% and specificity of 89% with
AUC of 0.92. The breast lesion size was larger for the high-risk groups and
lower for both low risk and intermediate risk groups. There were significant
differences in PK-DCE and ADC map values in each group. The ADC map values for
high- and intermediate-risk groups were significantly lower than the low-risk
group. Conclusion-These initial studies provide deeper understandings of
imaging features and molecular gene array OncotypeDX score. This insight
provides the foundation to relate these imaging features to the assessment of
treatment response for improved personalized medicine.
