Features of MRI stromal enhancement with neoadjuvant chemotherapy: a subgroup analysis of the ACRIN 6657/I-SPY TRIAL

Abstract

Abstract. Although the role of cancer-activated stroma in malignant progression has been well investigated, the influence of an activated stroma in therapy response is not well understood. Using retrospective pilot cohorts, we previously observed that MRI detected stromal contrast enhancement was associated with proximity to the tumor and was predictive for relapse-free survival in patients with breast cancer receiving neoadjuvant chemotherapy. Here, to evaluate the association of stromal contrast enhancement to therapy, we applied an advanced tissue mapping technique to evaluate stromal enhancement patterns within 71 patients enrolled in the I-SPY 1 neoadjuvant breast cancer trial. We correlated MR stromal measurements with stromal protein levels involved in tumor progression processes. We found that stromal percent enhancement values decrease with distance from the tumor edge with the estimated mean change ranging −0.48 to −0.17 (P ≤ 0.001) for time points T2 through T4. While not statistically significant, we found a decreasing trend in global stromal signal enhancement ratio values with the use of chemotherapy. There were no statistically significant differences between MR enhancement measurements and stromal protein levels. Findings from this study indicate that stromal features characterized by MRI are impacted by chemotherapy and may have predictive value in a larger study. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Quantitative background parenchymal enhancement to predict recurrence after neoadjuvant chemotherapy for breast cancer

“…This results broaden other published results, that have shown BPE to be linked with breast cancer risk 16,15 , breast metabolic activity 17 , as well as therapeutic response 8,18–20 .…”

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

“…The recurrence-free survival indicator (RFSi) has been considered to distinguish patients who experienced recurrence (non-recurrence-free) and patients who did not experienced recurrence (recurrence-free), respectively (non-RFSi vs. RFSi). Moreover, the regression models often make use of clinical variables as well as radiomic features designed by data scientists [ 17 , 18 , 19 ]. To give some examples, Hylton et al [ 16 ] were pioneers in demonstrating that the Functional Tumor Volume (FTV) computed from the DCE-MRI of 163 patients was predictive of recurrence-free survival.…”

“…Radiomic analysis using regression models with handcrafted features represents the most common choice in RFS prediction [ 16 , 17 , 18 ]. Such types of models often make use of clinical measurements as well as radiomic features designed by data scientists [ 17 , 18 , 19 ]. Only a few classification approaches, always based on handcrafted radiomic features, have been freshly developed to predict the probability of BCR [ 11 ].…”