Heterogeneity of enhancement kinetics in dynamic contrast-enhanced MRI and implication of distant metastasis in invasive breast cancer. Issue 12 (December 2020) - Zhao, R.; Ma, W.J.; Tang, J.; Chen, Y.Z.; Zhang, L.N.; Lu, H.; Liu, P.F.
Abstract : AIM: To investigate the heterogeneity of enhancement kinetics for breast tumour in order to demonstrate the predictive power of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features for distant metastasis (DM) in invasive breast cancer. MATERIALS AND METHODS: Time–signal intensity curve (TIC) patterns from 128 patients with invasive breast cancer were analysed by a pixel-based DCE-MRI analysis. This MRI technique enabled pixels with varying TIC patterns (persistent, plateau, washout and non-enhancement) to be categorised semi-automatically and the percentage of different TIC patterns in each breast tumour to be calculated. The percentage of TIC patterns was compared between the DM and non-DM groups. DM-free survival was estimated using Kaplan–Meier survival analysis. RESULTS: This study demonstrated a larger percentage of persistent TIC and non-enhancement TIC was associated with DM in invasive breast cancer. The cut-off values of persistent TIC and non-enhancement TIC were 22.5% and 2.5%. Combining TIC patterns and traditional predictors (tumour size and axillary lymph node status) can improve the prediction efficiency. The multivariable model yielded an area under the receiver operating characteristic curve (AUC) of 0.87 with 0.70 sensitivity and 0.87 specificity in leave-one-out cross-validation (LOOCV). These predictors showed significant differences in DM-free survival by Kaplan–Meier analysis. CONCLUSION: This study shows that breast tumours with higher heterogeneity are more likely to metastasise, and pixel-based TIC analysis has utility in predicting distant metastasis of invasive breast cancer. Highlights: Primary breast tumor with higher heterogeneity are more likely to metastasize. TIC features are associated with distant metastasis in invasive breast cancer. Combining TIC types, tumor size and lymph node status can improve predictive accuracy.