MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence

TU-CD-BRB-07: Identification of Associations Between Radiologist-Annotated Imaging Features and Genomic Alterations in Breast Invasive Carcinoma, a TCGA Phenotype Research Group Study

Purpose: To determine associations between radiologist-annotated MRI features and genomic measurements in breast invasive carcinoma (BRCA) from the Cancer Genome Atlas (TCGA). Methods: 98 TCGA patients with BRCA were assessed by a panel of radiologists (TCGA Breast Phenotype Research Group) based on a variety of mass and non-mass features according to the Breast Imaging Reporting and Data System (BI-RADS). Batch corrected gene expression data was obtained from the TCGA Data Portal. The Kruskal-Wallis test was used to assess correlations between categorical image features and tumor-derived genomic features (such as gene pathway activity, copy number and mutation characteristics). Image-derived features were also correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) status. Multiple hypothesis correction was done using Benjamini-Hochberg FDR. Associations at an FDR of 0.1 were selected for interpretation. Results: ER status was associated with rim enhancement and peritumoral edema. PR status was associated with internal enhancement. Several components of the PI3K/Akt pathway were associated with rim enhancement as well as heterogeneity. In addition, several components of cell cycle regulation and cell division were associated with imaging characteristics.TP53 and GATA3 mutations were associated with lesion size. MRI features associated with TP53 mutation status were rim enhancement and peritumoral edema. Rim enhancement was associated with activity of RB1, PIK3R1, MAP3K1, AKT1,PI3K, and PIK3CA. Margin status was associated with HIF1A/ARNT, Ras/ GTP/PI3K, KRAS, and GADD45A. Axillary lymphadenopathy was associated with RB1 and BCL2L1. Peritumoral edema was associated with Aurora A/GADD45A, BCL2L1, CCNE1, and FOXA1. Heterogeneous internal nonmass enhancement was associated with EGFR, PI3K, AKT1, HF/MET, and EGFR/Erbb4/neuregulin 1. Diffuse nonmass enhancement was associated with HGF/MET/MUC20/SHIP, and HGF/MET/RANBP9. Linear nonmass enhancement was associated with PIK3R1 and AKT activity. Conclusion: MRI-genomic association analysis revealed that several BRCA-associated gene features were associated with radiologist-annotated image features

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

PURPOSE: To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. MATERIALS AND METHODS: Analysis was conducted on an institutional review board–approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. RESULTS: Multiple linear regression analyses demonstrated significant associations (R(2) = 0.25–0.32, r = 0.5–0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. CONCLUSION: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. (©) RSNA, 2016 Online supplemental material is available for this article

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff's α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman's rank correlation coefficients were used to compare HEIP and CEIP. Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with  < 10 . CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement. Quantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine