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Purpose-Optimal use of established and imaging methods, such asmultiparametric magnetic resonance imaging(mpMRI) can simultaneously identifykey functional parameters and provide unique imaging phenotypes of breastcancer. Therefore, we have developed and implemented a new machine-learninginformatic system that integrates clinical variables, derived from imaging andclinical health records, to compare with the 21-gene array assay, OncotypeDX.Materials and methods-We tested our informatics modeling in a subset ofpatients (n=81) who had ER+ disease and underwent OncotypeDX gene expressionand breast mpMRI testing. The machine-learning informatic method is termedIntegrated Radiomic Informatic System-IRIS was applied to the mpMRI, clinicaland pathologic descriptors, as well as a gene array analysis. The IRIS methodusing an advanced graph theoretic model and quantitative metrics. Summarystatistics (mean and standard deviations) for the quantitative imagingparameters were obtained. Sensitivity and specificity and Area Under the Curvewere calculated for the classification of the patients. Results-The OncotypeDXclassification by IRIS model had sensitivity of 95% and specificity of 89% withAUC of 0.92. The breast lesion size was larger for the high-risk groups andlower for both low risk and intermediate risk groups. There were significantdifferences in PK-DCE and ADC map values in each group. The ADC map values forhigh- and intermediate-risk groups were significantly lower than the low-riskgroup. Conclusion-These initial studies provide deeper understandings ofimaging features and molecular gene array OncotypeDX score. This insightprovides the foundation to relate these imaging features to the assessment oftreatment response for improved personalized medicine.

Advanced machine learning informatics modeling using clinical and  radiological imaging metrics for characterizing breast tumor characteristics  with the OncotypeDX gene array