A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker

Abstract

Background and objective: Vascularity evaluation on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a potential diagnostic value, but it represents a time consuming procedure, affected by intra- and inter-observer variability. This study tests the application of a recently published method to reproducibly quantify breast vascularity, and evaluates if the vascular volume of cancer-bearing breast, calculated from automatic vascular maps (AVMs), may correlate with pathologic tumor response after neoadjuvant chemotherapy (NAC).

Methods: Twenty-four patients with unilateral locally advanced breast cancer underwent DCE-MRI before and after NAC, 8 responders and 16 non-responders. A validated algorithm, based on multiscale 3D Hessian matrix analysis, provided AVMs and allowed the calculation of vessel volume before the initiation and after the last NAC cycle for each breast. For cancer bearing breast, the difference in vascular volume before and after NAC was compared in responders and non-responders using the Wilcoxon two-sample test. A radiologist evaluated the vascularity on the subtracted images (first enhanced minus unenhanced), before and after treatment, assigning a vascular score for each breast, according to the number of vessels with length ≥30mm and maximal transverse diameter ≥2mm. The same evaluation was repeated with the support of the simultaneous visualization of the AVMs. The two evaluations were compared in terms of mean number of vessels and mean vascular score per breast, in responders and non-responders, by use of Wilcoxon two sample test. For all the analysis, the statistical significance level was set at 0.05.

Results: For breasts harboring the cancer, evidence of a difference in vascular volume before and after NAC for responders (median=1.71cc) and non-responders (median=0.41cc) was found (p=0.003). A significant difference was also found in the number of vessels (p=0.03) and vascular score (p=0.02) before or after NAC, according to the evaluation supported by the AVMs.

Conclusions: The encouraging, although preliminary, results of this study suggest the use of AVMs as new biomarker to evaluate the pathologic response after NAC, but also support their application in other breast DCE-MRI vessel analysis that are waiting for a reliable quantification method.

Keywords: Automatic vascular maps; Breast cancer; DCE-MRI; Vessel-detection algorithm.