Heterogeneity of enhancement kinetics in dynamic contrast-enhanced MRI and implication of distant metastasis in invasive breast cancer

Abstract

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: Timeesignal 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 KaplaneMeier 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 cutoff values of persistent TIC and nonenhancement 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 KaplaneMeier 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.

Perfusion phantom for the optimization of dynamic contrast-enhanced dedicated breast CT: iodine contrast curves in a simplified breast phantom

“…In recent years, there has been a growing interest in functional breast imaging, in which the functional information of the breast is obtained by evaluating the uptake and washout of contrast agents over time. [1][2][3][4] This information is then represented in time-intensity curves (TICs). Several studies have highlighted the potential clinical relevance of TIC patterns obtained from dynamic contrast-enhanced MRI (DCE-MRI) for diagnosing and predicting the prognosis of breast cancer pathology.…”

“…Several studies have highlighted the potential clinical relevance of TIC patterns obtained from dynamic contrast-enhanced MRI (DCE-MRI) for diagnosing and predicting the prognosis of breast cancer pathology. [1][2][3] However, one of the major limitations of DCE-MRI is the relatively low temporal resolution, resulting in a low number of time points and limited information that can be obtained from the TICs. This limitation could be overcome by dynamic contrast-enhanced dedicated breast CT (DCE-bCT), which currently has a 5-second acquisition time, 12 times faster than the standard DCE-MRI sequence.…”

Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer

“…Some researchers have focused on identifying imaging markers that can help predict distant metastasis using contrast-enhanced MRI. [7][8][9] The clinical use of diffusion-weighted imaging (DWI) has expanded and is now incorporated into routine clinical MRI protocol in many centers. The appeal of DWI is that it does not require intravenous contrast to perform noninvasive and qualitative assessment of the biological characteristics of tumors.…”

Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning

“…Therefore, early detection of breast cancer with distant metastasis can develop individualized treatment plans, which have specific significance for predicting the survival time of patients and improving the prognosis of patients [ 2 ]. Tumor heterogeneity is one of the characteristics of malignant tumors, which can be divided into spatial heterogeneity and temporal heterogeneity [ 3 ]. Tumor heterogeneity also maps the properties of various regions within the tumor, and tumors with high heterogeneity have poorer prognosis than those of with low heterogeneity [ 4 ].…”

“…After the image segmentation is completed, the segmented area can be formed into a three-dimensional image, and the volume can be obtained. A large amount of internal quantitative information is extracted through the software and combined with the corresponding clinical information, genetic information, serum markers, and histological information to make corresponding predictions [ 3 , 9 ]. However, these studies have certain limitations because patient outcomes are not determined by a single prognostic factor.…”