Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer

Abstract

Background Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction. Methods We identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful. Conclusion US radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.