Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay.

Abstract

Simple SummaryArtificial Intelligence methods using machine learning and radiomics is an emerging area of research for radiological and oncological applications for patient management. Recent evidence from breast cancer suggests that different breast cancer subtypes may respond differently to adjuvant therapies. The use of a 21-gene array assay called OncotypeDX can predict potential recurrence of cancer in patients with estrogen positive breast cancer after treatment, however, there are potential cost disadvantages that hamper its widespread use. Multiparametric magnetic resonance imaging can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast cancer, which is used in radiomic analysis. Radiomics provide quantitative information of different tissue types. We have developed a new machine learning radiomic informatics tool that integrates clinical and imaging variables, single, and multiparametric radiomics to compare with the OncotypeDX test to stratify patients into three risk groups: low, medium, and high risk of breast cancer recurrence.Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.