Purpose: To develop a radiomics signature based on preoperative magnetic resonance imaging (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 = 194) and validation (n = 100) sets. A radiomics signature (Rad-score) was 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 receiver operating 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 variables with DFS. A radiomics nomogram combining the Rad-score 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 = 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.

The results of this study revealed that tumors with higher entropy on T2-weighted images and lower entropy on contrast material–enhanced T1-weighted subtraction images were associated with poorer recurrence-free survival rates in patients with breast cancer. PurposeTo determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer.Materials and MethodsBetween January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material–enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS).ResultsThere were 26 events, including 22 recurrences (10 local-regional and 12 d...

Lee SH, et al. Korean J Radiol. 2020 Jul;21(7):779-792. https://doi.org/10.3348/kjr.2019.0855

Choi JS, et al. Korean J Radiol. 2019 Mar;20:e48. https://doi.org/10.3348/kjr.2018.0530

Mun SH, et al. Korean J Radiol. 2014 Jan-Feb;15(1):20-28. https://doi.org/10.3348/kjr.2014.15.1.20

The purpose of this study was to identify perfusional subregions sharing similar kinetic characteristics from dynamic contrast-enhanced magnetic resonance imaging (MRI) using data-driven clustering, and to evaluate the effect of perfusional heterogeneity based on those subregions on patients’ survival outcomes in various risk models. From two hospitals, 308 and 147 women with invasive breast cancer who underwent preoperative MRI between October 2011 and July 2012 were retrospectively enrolled as development and validation cohorts, respectively. Using the Cox-least absolute shrinkage and selection operator model, a habitat risk score (HRS) was constructed from the radiomics features from the derived habitat map. An HRS-only, clinical, combined habitat, and two conventional radiomics risk models to predict patients’ disease-free survival (DFS) were built. Patients were classified into low-risk or high-risk groups using the median cutoff values of each risk score. Five habitats with distinct perfusion patterns were identified. An HRS was an independent risk factor for predicting worse DFS outcomes in the HRS-only risk model (hazard ratio = 3.274 [95% CI = 1.378–7.782]; p = 0.014) and combined habitat risk model (hazard ratio = 4.128 [95% CI = 1.744–9.769]; p = 0.003) in the validation cohort. In the validation cohort, the combined habitat risk model (hazard ratio = 4.128, p = 0.003, C-index = 0.760) showed the best performance among five different risk models. The quantification of perfusion heterogeneity is a potential approach for predicting prognosis and may facilitate personalized, tailored treatment strategies for breast cancer.