Purpose: To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings. Experimental Design: We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training (n ¼ 194) and validation (n ¼ 100) sets. A radiomics signature (Rad-score)

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... s generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high-and low-risk groups was determined using receiveroperating characteristic curve analysis. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of the radiomics signature, MRI findings, and clinicopathological vari-ables with DFS. A radiomics nomogram combining the Radscore and MRI and clinicopathological findings was constructed to validate the radiomic signatures for individualized DFS estimation. Results: Higher Rad-scores were significantly associated with worse DFS in both the training and validation sets (P ¼ 0.002 and 0.036, respectively). The radiomics nomogram estimated DFS [C-index, 0.76; 95% confidence interval (CI); 0.74-0.77] better than the clinicopathological (C-index, 0.72; 95% CI, 0.70-0.74) or Rad-score-only nomograms (C-index, 0.67; 95% CI, 0.65-0.69). Conclusions: The radiomics signature is an independent biomarker for the estimation of DFS in patients with invasive breast cancer. Combining the radiomics nomogram improved individualized DFS estimation. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis):