MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER+/HER2-Early Breast Cancer Patients

摘要: Background: Oncotype Dx is a genetic assay providing a recurrence score (RS) correlated with the risk of cancer recurrence and adjuvant treatment response in breast carcinoma. We investigated the ability of an MRI-based radiomics approach to predict the risk of tumor recurrence in breast cancer.Methods: A total of 62 patients with biopsy-proved ER+/HER2-early breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS> 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p< 0.05).Findings: The nCV framework delivered an AUC of 0.76 (p= 1.1∙ 10-3). The 47 features included in the top 5%, were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related and 25/47 (53%) derived from high resolution images (1 mm).Interpretation: After a prospective evaluation in more extensive clinical trials, a radiomics-based machine learning approach may identify non-invasively patients …