Fast microbubble dwell-time based ultrasonic molecular imaging approach for quantification and monitoring of angiogenesis in cancer

Abstract

PURPOSE: To develop and test a fast ultrasonic molecular imaging technique for quantification and monitoring of angiogenesis in cancer. MATERIALS AND METHODS: A new software algorithm measuring the dwell time of contrast microbubbles in near real-time (henceforth, fast method) was developed and integrated in a clinical ultrasound system. In vivo quantification and monitoring of tumor angiogenesis during anti-VEGF antibody therapy was performed in human colon cancer xenografts in mice (n=20) using the new fast method following administration of vascular endothelial growth factor receptor 2 (VEGFR2)-targeted contrast microbubbles. Imaging results were compared with a traditional destruction/replenishment approach (henceforth, traditional method) in an intra-animal comparison. RESULTS: There was excellent correlation (R(2)=0.93; P<0.001) between the fast method and the traditional method in terms of VEGFR2-targeted in vivo ultrasonic molecular imaging with significantly higher (P=0.002) imaging signal in colon cancer xenografts using VEGFR2-targeted compared to control non-targeted contrast microbubbles. The new fast method was highly reproducible (ICC=0.87). Following anti-angiogenic therapy, ultrasonic molecular imaging signal decreased by an average of 41±10%, whereas imaging signal increased by an average of 54±8% in non-treated tumors over a 72-hour period. Decreased VEGFR2 expression levels following anti-VEGF therapy were confirmed on ex vivo immunofluorescent staining. CONCLUSIONS: Fast ultrasonic molecular imaging based on dwell time microbubble signal measurements correlates well with the traditional measurement method, and allows reliable in vivo monitoring of anti-angiogenic therapy in human colon cancer xenografts. The improved work-flow afforded by the new quantification approach may facilitate clinical translation of ultrasonic molecular imaging.

Figure 1

Schematic diagram shows principle of fast method for quantification of molecularly-attached contrast microbubbles on a clinical ultrasound system. A: The fast method algorithm adds the signal from stationary microbubbles (presence of stationary signal was encoded as 1; absence of signal was encoded as 0) in each imaging voxel over time (summed over all imaging frames); B: B-mode image of representative human colon cancer xenograft in mouse hindlimb outlined by region of interest (yellow line); C: Image obtained in same imaging plane as in (B) prior to injection of microbubbles shows background image; D: Image from the same plane obtained after VEGFR2-targeted contrast microbubbles injection shows imaging signal (blue) from molecularly attached microbubbles within the tumor region of interest

Figure 2

Flow diagram summarizes sequence of intra-animal imaging experiments obtained in the same human colon cancer xenografts. A: First, background images, were obtained using both the fast method and traditional method; B: Tumor vascularity was then measured in all tumors to normalize molecular imaging signal to tumor perfusion; C: Finally, ultrasonic molecular imaging signal was measured using both the fast method and the traditional method in the same mice

Figure 3

Schematic diagram summarizes experimental plan of longitudinal ultrasonic molecular imaging study for monitoring tumor angiogenesis. Following induction of human colon cancer xenografts in mice, a baseline (0 h) ultrasound imaging scan was performed in all mice. Mice were then randomly divided into 2 groups (mice undergoing daily anti-angiogenic vs. no (saline) treatment) and daily imaging was repeated until 72 h. All animals were then sacrificed and tumors were excised for ex vivo analysis of VEGFR2 expression levels on tumor vessels

Figure 4

Representative transverse ultrasonic molecular images of human colon cancer xenograft in same mouse acquired with both the fast and traditional method following intravenous administration of non-targeted control microbubbles (left column) and VEGFR2-targeted microbubbles (right column)

Figure 5

Transverse ultrasonic molecular images of human colon cancer xenograft scanned using both the fast and traditional method following VEGFR2-targeted microbubbles injections. Second set of images (right column) was acquired 30 min after first data set was acquired (left column). Note that distribution of molecular imaging signal obtained during consecutive imaging sessions is highly comparable using both methods indicating excellent reproducibility of both methods for quantification of ultrasonic molecular imaging signal. Also note that ultrasonic molecular imaging signal was measured slightly higher after second microbubble injection using both methods, likely due to incomplete clearance of targeted microbubbles from first injection

Figure 6

Representative near real-time (fast method) ultrasonic molecular images from longitudinal monitoring trial of anti-angiogenic therapy in treated (anti-VEGF antibody; B20) and non-treated (saline only) mice bearing human colon cancer xenografts. Ex vivo immunostaining of VEGFR2 (red; white arrows) and tumor neovasculature (CD31, green endothelial marker; white arrows) confirmed high VEGFR2 expression levels on the vasculature of non-treated tumor and down-regulation of VEGFR2 in treated tumor after anti-angiogenic therapy; representative merged (co-localization: yellow) fluorescent micrographs (scale bar: 100 µm) are shown

Figure S1

Display screen of ultrasound machine used in this study showing near real-time software image and B-mode anatomical image side by side. Ultrasound imaging was motion compensated in real-time using a tracking box (green dashed line; green arrows), that tracked pixel displacements in B-mode, and applied displacements to both B-mode and contrast (near real-time software) images (27). Settings (threshold, 50; dwell percentage, 80%; gain, -10; depth-dependent tissue equalization (TEQ) depth-gain levels) were determined after alignment of the subcutaneous tumor in the field of view, with focus set at the level of the tumor. TEQ levels were automatically determined for the first tumor, and the same levels were applied to all tumors. The gain (-10) was adjusted to maximize the brightness just above the system noise (note: appearance of minor levels at top of display); at this setting, tissue noise is also present and accounted for by background subtraction. Note that there is very little to no signal (or tissue noise) within the tumor in this example. ROIs (yellow lines) were drawn around the tumor for instantaneous quantification displayed on the graph and numerically in green box (red arrow between graph and green box).

Figure S2

Assessment of optimal dwell percentage was performed by comparing the VEGFR2-targeted microbubble signal in subcutaneous human colon cancer xenografts using dwell percentages of 10%, 20%, 35%, 50%, 65%, and 80%. Values of microbubble signal was divided by the microbubble signal at dwell percentage =10% to visualize the relative change between values. Note minimal differences between dwell percentages of 50%, 65%, and 80%