Quantitative Assessment of Cancer Vascular Architecture by Skeletonization of High-Resolution 3-D Contrast-Enhanced Ultrasound Images: Role of Liposomes and Microbubbles

Abstract

The accurate characterization and description of the vascular network of a cancer lesion is of paramount importance in clinical practice and cancer research in order to improve diagnostic accuracy or to assess the effectiveness of a treatment. The aim of this study was to show the effectiveness of liposomes as an ultrasound contrast agent to describe the 3-D vascular architecture of a tumor. Eight C57BL/6 mice grafted with syngeneic B16-F10 murine melanoma cells were injected with a bolus of 1,2-Distearoyl-sn-glycero-3-phosphocoline (DSPC)-based non-targeted liposomes and with a bolus of microbubbles. 3-D contrast-enhanced images of the tumor lesions were acquired in three conditions: pre-contrast, after the injection of microbubbles, and after the injection of liposomes. By using a previously developed reconstruction and characterization image processing technique, we obtained the 3-D representation of the vascular architecture in these three conditions. Six descriptive parameters of these networks were also computed: the number of vascular trees (NT), the vascular density (VD), the number of branches, the 2-D curvature measure, the number of vascular flexes of the vessels, and the 3-D curvature. Results showed that all the vascular descriptors obtained by liposome-based images were statistically equal to those obtained by using microbubbles, except the VD which was found to be lower for liposome images. All the six descriptors computed in pre-contrast conditions had values that were statistically lower than those computed in presence of contrast, both for liposomes and microbubbles. Liposomes have already been used in cancer therapy for the selective ultrasound-mediated delivery of drugs. This work demonstrated their effectiveness also as vascular diagnostic contrast agents, therefore proving that liposomes can be used as efficient “theranostic” (i.e. therapeutic + diagnostic) ultrasound probes.

Quantification of Microvascular Tortuosity during Tumor Evolution Using Acoustic Angiography

“…Tortuosity associated with malignancy has been used previously with other imaging modalities such as magnetic resonance angiography (MRA), photoacoustic tomography, microscopy, and computed tomography (CT) (Jain 2003; Bullitt et al 2004; Bullitt et al 2005b; Bullitt et al 2006; Badea et al 2008; Laufer et al 2012; Sodi et al 2013). However, ultrasound applications of tortuosity metrics have primarily been limited to quantification of skeletonizations from intensity thresholding of Doppler ultrasound images (Huang et al 2008; Molinari et al 2010; Chang et al 2012; Molinari et al 2014). These types of images lack the clear vessel structures visible in acoustic angiography which make segmentation and analysis of tortuosity in individual vessels possible.…”

Ultrasound and Transcriptomics Identify a Differential Impact of Cisplatin and Histone Deacetylation on Tumor Structure and Microenvironment in a Patient-Derived In Vivo Model of Gastric Cancer

“…In preclinical translational studies, one of the most relevant approaches to assess the outcome of a treatment is through the tumor volume and its speed of growth using different mouse models [ 30 ]. The most commonly used mouse models are either cell line-derived models or patient-derived xenografts (PDX).…”

“…This accuracy is likely due to the fact that the tumor implantation site on the flanks of the mouse (i.e., heterotopic implantation) is well suited for ultrasound imaging with a high-frequency probe, as the explored tumors are superficial (i.e., subcutaneous) and not subject to bone/air artifacts. With the ultrasound, the major limiting factor is the depth of the tissue that can be imaged through, especially when using superficial probes [ 30 ]. If the ultrasound is limited by its user’s skills, the exploration of superficial tumors is also not limited by the interposition of skin layers or by non-ellipsoid tumor shapes, therefore enabling a real-time visualization of the tumor, together with the storage of images of interest.…”

Quantitative image analysis for evaluation of tumor response in clinical oncology

“… 86 The accurate characterization and description of the vascular network of a lesion provides a new topological texture biomarker to assess the effectiveness of anti-angiogenesis treatments using such as the density of vascular trees, and the 3D curvature of the vessels. 87 , 88 …”