Ultrasound-based quantitative microvasculature imaging for early prediction of response to neoadjuvant chemotherapy in patients with breast cancer.

Abstract

Background: Angiogenic activity of cancerous breast tumors can be impacted by neoadjuvant chemotherapy (NAC), thus potentially serving as a marker for response monitoring. While different imaging modalities can aid in evaluation of tumoral vascular changes, ultrasound-based approaches are particularly suitable for clinical use due to their availability and noninvasiveness. In this paper, we make use of quantitative high-definition microvasculature imaging (qHDMI) based on contrast-free ultrasound for assessment of NAC response in breast cancer patients.

Methods: Patients with invasive breast cancer recommended treatment with NAC were included in the study and ultrafast ultrasound data were acquired at pre-NAC, mid-NAC, and post-NAC time points. Data acquisitions also took place at two additional timepoints - at two and four weeks after NAC initiation in a subset of patients. Ultrasound data frames were processed within the qHDMI framework to visualize the microvasculature in and around the breast tumors. Morphological analyses on the microvasculature structure were performed to obtain 12 qHDMI biomarkers. Pathology from surgery classified response using residual cancer burden (RCB) and was used to designate patients as responders (RCB 0/I) and non-responders (RCB II/III). Distributions of imaging biomarkers across the two groups were analyzed using Wilcoxon rank-sum test. The trajectories of biomarker values over time were investigated and linear mixed effects models were used to evaluate interactions between time and group for each biomarker.

Results: Of the 53 patients included in the study, 32 (60%) were responders based on their RCB status. The results of linear mixed effects model analysis showed statistically significant interactions between group and time in six out of the 12 qHDMI biomarkers, indicating differences in trends of microvascular morphological features by responder status. In particular, vessel density (p-value: 0.023), maximum tortuosity (p-value: 0.049), maximum diameter (p-value: 0.002), fractal dimension (p-value: 0.002), mean Murray's deviation (p-value: 0.034), and maximum Murray's deviation (p-value: 0.022) exhibited significantly different trends based on responder status.

Conclusions: We observed microvasculature changes in response to NAC in breast cancer patients using qHDMI as an objective and quantitative contrast-free ultrasound framework. These finding suggest qHDMI may be effective in identifying early response to NAC.