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

Abstract

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 (R2 = 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.

Computer‐aided heterogeneity analysis in breast MR imaging assessment of ductal carcinoma in situ: Correlating histologic grade and receptor status

“…In recent years, applications of heterogeneity analysis have demonstrated the potential of MR imaging biomarkers in improving the specificity of breast MR and providing clinically relevant biological indicators of invasive breast cancers (22–28). Until now, heterogeneity analysis has not been utilized in the development of MR imaging biomarkers of DCIS.…”

“…Previous studies utilizing heterogeneity analysis in the development of MR imaging biomarkers focused on differentiating benign from malignant breast lesions and molecular subtypes of invasive breast cancer (22–24). Recent applications of heterogeneity analysis show promising results in the assessment of response of invasive breast cancer being treated with neoadjuvant therapy and the recurrence risk of invasive breast cancer (25–28). …”

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

“…By converting medical images into highdimensional, mineable, and quantitative imaging features via high-throughput extraction of data-characterization algorithms, radiomics methods provide an unprecedented opportunity to improve decision-support in oncology at low cost and noninvasively (14,17). Some previous studies have shown that biomarkers based on quantitative radiomics features are associated with clinical prognosis and underlying genomic patterns across a range of cancer types (18)(19)(20)(21)(22).…”

Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography.

“…Prior studies have reported promising performance of texture features and radiomics with and without the use of machine learning in the prediction of tumor response from MR mammography (MRM). Most MRM radiomics studies so far have focused specifically on contrast‐enhanced MR sequences, and have evaluated tumor response to neoadjuvant chemotherapy or prediction of histological subtype, OncotypeDX risk categories, early metastasis, molecular subtypes, gene and protein expression, and commonly used gene assays …”