Abstract

BackgroundAccurate 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.MethodsWe 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.ResultsThe 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.ConclusionUS 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.

Highlights

  • Recurrence remains the principal cause of breast cancer-related death, which seriously endanger the health of women [1, 2]

  • The radiomics signature was significantly associated with disease-free survival (DFS), independent of the clinicopathological predictors

  • Decision curve analysis demonstrated that the radiomics nomogram was clinically useful

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Summary

Introduction

Recurrence remains the principal cause of breast cancer-related death, which seriously endanger the health of women [1, 2]. Radiomics holds promise in predicting breast cancer recurrence due to its high-dimensional features extracted from medical images [6], which are related to the multigene assay recurrence scores of breast cancer and associated to the recurrence survival [7–9]. US radiomics features could distinguish benign breast tumors from malignant tumors, could predict axillary lymph node metastasis, and could assist clinicians with accurate prognosis prediction in breast cancer [10–12]. 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. 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

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