Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.

David E Frankhouser,Ashish Mahabal,Eric Dietze,Victoria L Seewaldt

doi:10.3389/fradi.2021.735567

Abstract

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Highlights

Vascularization and Breast Cancer InitiationAngiogenesis is a dynamic process and an important early step during breast cancer initiation and progression [1, 2]
We aim to review computational approaches for detection and analysis of blood vessels in dynamic contrast enhanced (DCE)-Magnetic Resonance Imaging (MRI) images and present some of the strategies we have developed for co-registry of DCE-MRI images and detection of blood vessels and neovascularization
Multiple texture-based parameters of vessels show statistically significant differences between breasts with benign vs. malignant tumors The algorithm detected similar vessel structure from two different scans collected from the same individual Vessel detection algorithm shown to have 89% detection rate with 98% sensitivity Removing vessel structures from images decreased false positive rate of parenchymal lesions by 68% Vessel volume was decreased in responders vs. non-responders of neoadjuvant chemotherapy A vessel detection algorithm with center line tracking to fill-in incomplete vessels demonstrated 86% sensitivity and 88% specificity Malignant lesions have a greater number of lesion-associated vessels not directly measure biology; the characteristics of the breast tissue increase the computational difficulty of measuring blood vessels

Summary

Introduction

Angiogenesis is a dynamic process and an important early step during breast cancer initiation and progression [1, 2]. The morphologic quantitation and characterization of blood vessels in a biopsy specimen relies on indirect measures such as microvessel density (number of small and tortuous vessels, immunohistochemistry (factor VIII, CD31, CD34)) [2,3,4,5,6]. The variability of morphological characterization of blood vessels becomes limiting when evaluating [1] response to neoadjuvant chemotherapy and [2] women who are at high-risk for developing a breast cancer (e.g., germline BRCA mutation). Imaging strategies are being developed and optimized for early detection of vascularization, and neovascularization

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Conclusion