Alterations in tumor perfusion and microenvironment have been shown to be associated with aggressive cancer phenotypes, raising the need for noninvasive methods of tracking these changes. Dynamic contrast–enhanced ultrasound (DCEUS) and photoacoustic (PA) imaging serve as promising candidates—one has the ability to measure tissue perfusion, whereas the other can be used to monitor tissue oxygenation and hemoglobin concentration. In this study, we investigated the relationship between the different functional parameters measured with DCEUS and PA imaging, using two morphologically different hind-limb tumor models and drug-induced alterations in an orthotopic breast tumor model. Imaging results showed some correlation between perfusion and oxygen saturation maps and the ability to sensitively monitor antivascular treatment. In addition, DCEUS measurements revealed different vascular densities in the core of specific tumors compared with their rims. Noncorrelated perfusion and hemoglobin concentration measurements facilitated discrimination between blood lakes and necrotic areas. Taken together, our results illustrate the utility of a combined contrast-enhanced ultrasound method with photoacoustic imaging to visualize blood flow patterns in tumors. Cancer Res; 76(15); 4320–31. ©2016 AACR.

To ensure survival and progression, tumor cells have acquired a number of characteristics or “hallmarks” that differentiate them from normal cells. Theses specific traits, including the ability to induce abnormal angiogenesis, have been a major area of focus in cancer research (1). In addition to genetically altered cellular characteristics, tumor microenvironmental factors, such as hypoxia, also play an important part in cancer progression and aggressive phenotypes (2). Knowledge of changes in tumor perfusion and microenvironmental patterns is of great importance for detecting aggressive cancer phenotypes and determining the effects of different treatments. Therefore, there is a need for noninvasive imaging methods capable of tracking both changes in tumor perfusion and its microenvironment. Dynamic contrast–enhanced ultrasound (DCEUS) and photoacoustic (PA) imaging are two noninvasive functional imaging modalities, having the potential to facilitate the monitoring of tumor development and treatment efficiency.

Contrast-enhanced ultrasound involves the use of gas-filled microbubbles. Using special pulse sequences and processing techniques the signal originating from the microbubbles can be separated from the soft tissue background (3). Furthermore, microbubbles are similar in size to red blood cells making them ideal for the imaging of blood (4). DCEUS is an established imaging mode for the measurement of functional tissue perfusion, providing us with insights on blood flow and blood volume inside localized tissue regions (5).

PA imaging combines optic and acoustic imaging into a single modality. Short laser pulses directed to the tissue generate regional thermo-elastic expansion and create acoustic waves that are detected by an ultrasound transducer at the surface of the tissue (6, 7). By encoding optical contrast onto acoustic waves, the penetration depth is greatly increased, while retaining the high contrast and spectral specificity of optical imaging. PA imaging is an excellent tool for the visualization of blood, or more specifically hemoglobin in tissue (8, 9). Furthermore, the differing absorption spectra of deoxygenated and oxygenated hemoglobin make it possible to monitor oxygen saturation (SO2) in vivo through the use of multiwavelength imaging, allowing for hypoxia detection in tissue.

There are two commonly accepted forms of hypoxia: diffusion-limited and perfusion-limited (2). Tumor cells located around functional blood vessels are supplied with oxygen and nutrients. When the distance from these vessels increases, the result is decreased pO2 levels, characterizing diffusion-limited hypoxia. Perfusion-limited hypoxia on the other hand is transient and caused by irregularities in blood flow within the tumor vasculature. As both the structure and functional status of tumor vasculature are important factors that affect the oxygenation of tumor tissue, it is important to have imaging tools that can be used to investigate changes in perfusion, such as DCEUS, and monitor changes the oxygenation, such as PA imaging.

In addition to SO2, hemoglobin concentration can also be estimated from PA measurements. Hemoglobin is usually purely intravascular. However, the leakiness of tumor vasculature can lead to extravasation of erythrocytes outside the vessel walls to form static hemorrhagic regions (blood lakes) in some tumors. Defects in the endothelial monolayer are known to explain the leakiness of tumor vessels (10). Both blood lakes and necrotic regions do not incorporate functional vasculature, thus have low perfusion values and potentially high hemoglobin content.

In this study, we examined the advantages of using DCEUS combined with PA imaging. Previous work performed using scanners combining DCEUS and PA imaging tend to analyze physiologic properties estimated using each modality separately (e.g., ref. 11). This sort of analysis could miss functional characteristics that may be derived from comparing the two different but complimenting datasets.

This study assessed the relationship between perfusion parameters measured using DCEUS, and SO2 and hemoglobin concentration measured using PA imaging in two experiments. In the first experiment, the spatial relationship between DCEUS and PA parameters was studied at high resolution using two different hind-limb tumor models with different vascular morphology. In the second experiment, the ability of DCEUS and PA imaging to detect global drug–induced functional perfusion and oxygenation changes in the tumor was studied using an orthotopic model of human breast cancer. Histopathology was used to validate the imaging results.

Tumor models

Xenograft tumors were induced in mice using either LS174T human colorectal cancer cells, or PC3 human prostate cancer cells. Both cell lines were cultured in Gibco DMEM (Life Technologies Inc.) supplemented with 10% FBS (Hyclone). A total of 1 × 106 cells were injected subcutaneously into the left hind limb of female 8-week-old SHO mice (Charles River Laboratories). The tumors were scanned after they reached a depth of 4–6 mm.

Orthotopic primary breast tumors were induced using 231/LM2-4 cells, a metastatic variant of the human MDA-MB-231 breast cancer cell line (12). A small incision in the lower abdominal skin was made above the right inguinal mammary fat pad (MFP) of female 8-week-old SHO mice (Charles River Laboratories), followed by the injection of 2 × 106 cells into the exposed MFP. The skin was then closed with surgical staples and allowed to heal. Tumors were allowed to grow to an approximate volume of 200 mm3 before being imaged and treated. All procedures were completed with the animal anesthetized under isoflurane and in accordance with Sunnybrook Health Science Centre's approved protocol for Animal Care and Use.

Drug induction of tumor microenvironmental changes using Oxi-4503

Tumor ischemia was induced in the orthotopic breast tumors model using a tubulin-binding vascular disrupting agent: Oxi-4503 (OXiGENE Inc.). Each mouse was given either an intraperitoneal injection of 50 mg/kg of Oxi-4503 or injectable saline and allowed 4 hours for the drug to take effect before being imaged again for post-treatment effects.

Noninvasive imaging of tumors

All in vivo imaging was performed using a laser integrated high-frequency ultrasound system (Vevo LAZR, VisualSonics Inc.). In the first experiment, 6 hind-limb tumors were imaged for each tumor type. In the second experiment involving drug induction of tumor microenvironmental changes, two groups of mice were imaged (n = 6 per group) at two separate time points, once before the injections were given, then a second time 4 hours after injection. All animals were anesthetized with 2% isoflurane (Abbott Laboratories Limited) delivered in combination with 1 L/minute oxygen. A 27G butterfly needle was inserted into a lateral tail vein for intravenous injection of the ultrasound contrast agent (UCA). A linear array transducer with fiber optical bundles integrated to each side (LZ-250, fc = 21 MHz) was used to deliver light from a tunable laser (680–970 nm). The tumor was first imaged employing B-mode imaging for region of interest (ROI) selection, followed by PA and contrast mode. PA images were acquired using two alternating wavelengths (750 nm and 850 nm). Contrast-enhanced images were collected after a 50-μL bolus injection of MicroMarker UCA (VisualSonics Inc.) using nonlinear contrast imaging. All three-dimensional (3D) PA data were acquired by moving the transducer with a stepper motor, at a step size of 50 μm.

Image analysis

In the hind-limb experiments, the recorded PA images and DCEUS cines were loaded into a personal computer and analyzed offline using in-house MATLAB programs. To estimate the local time–intensity curves (TIC) in the DCEUS movies, temporal wavelet denoising was applied to each pixel in the scans (13). From the denoised scans, three different perfusion parameters were estimated, producing high-resolution parametric maps of the tumors: peak enhancement, the difference between maximum amplitude, and the baseline intensity of the wash-in curve, used as an indication of the tumor blood volume; wash-in rate, the maximum slope of the curve, used as an indication of blood flow rate; and area under the curve, the integrated difference between the intensity value and its baseline. To estimate the local correlation between different perfusion parameters and oxygenation level, or hemoglobin concentration estimations, the normalized correlation was assessed between each pair of parameters for each pixel.

In the experiment of tumor microenvironmental changes caused by the injected drug, all image analysis was completed offline on the Vevo Lab. PA data were collected as a 3D image stack containing 150 to 200 SO2 2D images. A ROI encompassing the tumor was drawn for each image to determine the averaged SO2 value for each tumor. The ROI selection was done with reference to the anatomic B-mode image. After averaging the contrast intensity in each ROI, global TICs were generated and quantified using Vevo CQ. Two parameters were taken from the TICs: peak enhancement, and wash-in rate.

Histology of tumors

All animals were sacrificed after the last contrast-enhanced ultrasound imaging, for tumor tissue collection. Tumors were excised and cut in half in the same plane as the imaging plane and fixed in formalin for 48 hours before processing. All tissues were cut into 5-μm sections, and stained with either hematoxylin and eosin (H&E), or immunostained with carbonic anhydrase 9 (CA9) or cluster of differentiation 31 (CD31) for hypoxia and angiogenesis, respectively. For the first experiment, a single set of sections was collected from each tumor. As part of the second experiment, four sections spaced 350 μm apart were collected from each tumor. The Leica SCN400 (Leica Microsystems Inc.) slide scanner was used for tumor visualization, under bright field at 40× magnification.

CD31 staining was analyzed with Sedeen Viewer, version 4.0.x., counting the number of vessel structures per mm2 area of tumor slice. Necrotic regions, blood vessels, hypoxic areas, and blood lakes were separated from viable tissue regions using thresholding performed in MATLAB following the use of the color deconvolution plugin in the ImageJ software. Subsequently, necrotic regions and blood lakes were classified using a MATLAB GUI. Regions were segmented and marked as blood lakes if they included red blood cells or plasma and were not surrounded by endothelial cells in the matching CD31 staining. CA9 expression was quantified in MATLAB as percentage area of positive stain per tumor slice. To produce composite histologic images, the masks of hypoxic regions produced from CA9 stains were aligned with the masks of necrotic regions, blood lakes, and red blood cells derived from H&E stains (Supplementary Fig. S1), using the control point registration MATLAB function.

Statistical analysis

Statistical functions in MATLAB were used to assess the statistical significance of differences between the various measurements performed on PC3 and LS174T tumors. In addition to the correlation between DCEUS and PA parameters, different histologic parameters were compared between the two tumors. As Gaussian distributions were not assumed, the Wilcoxon rank-sum test was used and the results were considered significant for values of 0.05 > P.

The program PASW Statistics 18 was used to assess the statistical significance of differences in mean values of DCEUS and PA parameters and the histologic markers in response to the Oxi-4503 treatment. A two-tailed independent samples t test or paired samples t test was used to assess the significance of the mean difference.

LS174T and PC3 tumor xenografts displayed significantly different perfusion and oxygenation patterns

Comparison of DCEUS parametric maps and processed PA scans revealed detailed information about the spatial relationship between perfusion, oxygenation, and hemoglobin distribution in the tumors. Sample images of these functional maps taken from LS174T and PC3 tumors are shown in Fig. 1. 

Figure 1.

Spatial patterns of perfusion, oxygen saturation, and hemoglobin concentration in sample LS174T (left column) and PC3 (right column) tumors. A and B, anatomic B-mode images show the location of the hypoechogenic tumors. Mean DCEUS intensity (C and D; AUC normalized by time), oxygen saturation (E and F), and hemoglobin concentration (G and H) are compared. Scale bars, 1 mm.

Figure 1.

Spatial patterns of perfusion, oxygen saturation, and hemoglobin concentration in sample LS174T (left column) and PC3 (right column) tumors. A and B, anatomic B-mode images show the location of the hypoechogenic tumors. Mean DCEUS intensity (C and D; AUC normalized by time), oxygen saturation (E and F), and hemoglobin concentration (G and H) are compared. Scale bars, 1 mm.

Close modal

DCEUS scans of LS174T tumors presented highly perfused rim with resolvable blood vessels in their cores. These vessels were surrounded by many small nonperfused regions (Fig. 1C). Oxygen saturation maps showed high SO2 in the rim of the tumors and low SO2 in their cores, corresponding well to the DCEUS perfusion maps (Fig. 1E). In contrast to perfusion and SO2 patterns, the distribution of hemoglobin was almost uniform in the tumor (Fig. 1G).

PC3 tumors showed distinctively different anatomic and functional maps. While the rims of PC3 tumors were highly perfused, their core contained large poorly perfused and nonperfused regions (Fig. 1D). Large resolvable vessels were not detected in any of the perfusion maps. High SO2 levels were measured throughout the tumors' rim (Fig. 1F), with low SO2 detected in the core—measured only in regions where the concentration of hemoglobin was high enough to enable oxygen saturation estimations (Fig. 1H). However, vast core regions appeared black, with no detectable hemoglobin concentration.

Overall tumor perfusion correlated with oxygen saturation, but showed discrepancies when compared with hemoglobin distribution

Different perfusion parameters were estimated from high resolution TICs produced from DCEUS scans. The parameters evaluated included peak enhancement, wash-in rate, and area under the curve (AUC, see Fig 1C and D). Similar patterns were observed in the tumor perfusion and oxygen saturation maps, with areas of high SO2 reflective of areas of high blood perfusion. To quantify and evaluate the relationship between the different functional parameters, the correlation between corresponding pixels in the perfusion parametric maps and oxygen saturation maps were assessed. A similar analysis was also performed to evaluate the correlation between different perfusion parameters and hemoglobin concentration. On the basis of this local spatial analysis, reasonable correlation between the perfusion AUC parameter and SO2 was observed for both LS174T and PC3 tumors (Fig. 2A, r = 0.63 ± 0.06 and r = 0.49 ± 0.10 respectively, n = 6). In contrast, the correlation between perfusion and hemoglobin concentration varied significantly between the two tumor models (Fig. 2B, P = 0.0022, n = 6) and was negligible for LS174T tumors.

Figure 2.

Correlation between perfusion, oxygen saturation, and hemoglobin concentration in LS174T and PC3 tumors. A, correlation between matching pixels in the perfusion (DCEUS-AUC) and oxygen saturation parametric maps is fair for both tumors. B, correlation between matching pixels in the perfusion (DCEUS-AUC) and hemoglobin concentration parametric maps is significantly lower for LS174T tumors (*, P < 0.05, n = 6). Data are expressed as mean ± SEM.

Figure 2.

Correlation between perfusion, oxygen saturation, and hemoglobin concentration in LS174T and PC3 tumors. A, correlation between matching pixels in the perfusion (DCEUS-AUC) and oxygen saturation parametric maps is fair for both tumors. B, correlation between matching pixels in the perfusion (DCEUS-AUC) and hemoglobin concentration parametric maps is significantly lower for LS174T tumors (*, P < 0.05, n = 6). Data are expressed as mean ± SEM.

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Vessel distribution analysis using histology showed markedly different vascular morphology between LS174T and PC3 tumors, confirming DCEUS observations

We further examined the differences in perfusion pattern between LS174T and PC3 tumors using immunohistochemistry. Tumors were stained with CD31, a widely used endothelial marker for tumor neovascularization (14, 15), to compare the vascular morphology of the two tumor models. In LS174T tumors, the core was characterized by large deformed blood vessels (Fig. 3A, top right, arrows) that were separated from one another by more than 100 μm—the resolution of DCEUS scans taken in this study. This was reflective of what we observed in the perfusion maps, where large resolvable vessels were present at the core of the tumors. In contrast, PC3 tumors were characterized by small vessels distributed across its core (Fig. 3A, bottom right, arrows). In comparison with the LS174T tumors, vessels found in PC3 tumors were located much closer to each other. CD31+ microvessel counts showed significantly lower density of large vessels in PC3 tumors compared with LS174T tumors (Fig. 3B, n = 6, P = 0.0087). In addition, LS174T tumors exhibited significantly higher density of open lumen vessels compared with PC3 tumors (Fig. 3C, n = 6, P = 0.0022).

Figure 3.

Differences in blood vessel morphology between hind-limb tumor models. A, H&E and CD31 stains reveal noticeable differences in blood vessel morphology in the core of LS174T and PC3 tumors. LS174T tumors are characterized by scattered large luminal vessels (arrows) and huge blood lakes (dashed arrows). In contrast, PC3 tumors' cores contain mainly small vessels clustered with higher density. Large necrotic regions (arrowheads) can be found in location lacking functional blood vessels. B, the number of large vessels (surrounded by more than 5 endothelial cells) per tumor area was significantly higher in LS174 tumors. C, significantly higher density of luminal vessels was measured in LS174T tumors. *, P < 0.01.

Figure 3.

Differences in blood vessel morphology between hind-limb tumor models. A, H&E and CD31 stains reveal noticeable differences in blood vessel morphology in the core of LS174T and PC3 tumors. LS174T tumors are characterized by scattered large luminal vessels (arrows) and huge blood lakes (dashed arrows). In contrast, PC3 tumors' cores contain mainly small vessels clustered with higher density. Large necrotic regions (arrowheads) can be found in location lacking functional blood vessels. B, the number of large vessels (surrounded by more than 5 endothelial cells) per tumor area was significantly higher in LS174 tumors. C, significantly higher density of luminal vessels was measured in LS174T tumors. *, P < 0.01.

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Discrepancies between tumor perfusion and hemoglobin distribution were explained with further histologic analysis, revealing hemorrhagic regions in LS174T tumors

We conducted detailed examination of CD31 and H&E stains to understand the disagreement observed between perfusion and hemoglobin distributions. Histology revealed distinctive morphology differences between the two tumor types, with variable distributions of necrotic regions and blood lakes. CD31 staining were used to differentiate functional vessels from blood lakes based on the absence of endothelial cells (CD31+ stains) from blood lakes' edges.

On the basis of H&E staining, LS174T tumors were characterized by a highly viable periphery (Fig. 4A, left, dark purple), with a core that contained a mosaic of blood lakes (light pink, dashed arrows), large deformed blood vessels, and necrotic regions (light purple, arrowheads). The presence of blood lakes gave insight to the discrepancy that was observed in areas of high hemoglobin concentration where both perfusion and SO2 were low. Similar to LS174T tumors, the rims of PC3 tumors were also composed of highly viable tissue (Fig. 4A, right). However, the cores of PC3 tumors were characterized by large necrotic regions, with no signs of hemorrhagic blood lakes. Necrotic regions in both tumors had low erythrocytes content with few sparse extravasated erythrocytes detectable (Fig. 4D, yellow arrows), pointing to the remaining of dead vessels.

Figure 4.

Differences in blood lakes and necrotic region distribution between hind-limb tumor models. A, H&E histologic staining of sample LS174T and PC3 tumors illustrate the differences between the prevalence of necrotic regions (arrowheads) and blood lakes (dashed arrows) in these two cell lines. Static blood lakes were differentiated from functional vessels based on the presence of endothelial cells in the lining of the vessels in matching CD31 stains (not presented). B, histologic analysis of the proportion of necrotic region area in LS174T and PC3 tumors. PC3 tumors have significantly higher proportion of necrotic area. *, P < 0.01. C, histologic analysis of the proportion of tumor area containing blood lakes in LS174T and PC3 tumors. LS174T tumors are characterized by huge blood lakes, while no significant blood lakes were detected in PC3 tumors. D, extravagated red blood cells were detected in small amounts inside necrotic regions of PC3 tumors (yellow arrows).

Figure 4.

Differences in blood lakes and necrotic region distribution between hind-limb tumor models. A, H&E histologic staining of sample LS174T and PC3 tumors illustrate the differences between the prevalence of necrotic regions (arrowheads) and blood lakes (dashed arrows) in these two cell lines. Static blood lakes were differentiated from functional vessels based on the presence of endothelial cells in the lining of the vessels in matching CD31 stains (not presented). B, histologic analysis of the proportion of necrotic region area in LS174T and PC3 tumors. PC3 tumors have significantly higher proportion of necrotic area. *, P < 0.01. C, histologic analysis of the proportion of tumor area containing blood lakes in LS174T and PC3 tumors. LS174T tumors are characterized by huge blood lakes, while no significant blood lakes were detected in PC3 tumors. D, extravagated red blood cells were detected in small amounts inside necrotic regions of PC3 tumors (yellow arrows).

Close modal

The findings regarding the different composition of LS174T and PC3 tumors were confirmed with statistical analysis. The proportion of necrotic regions in PC3 tumors were significantly higher than in LS174T tumors (Fig. 4B, n = 6, P = 0.004). On the other hand, blood lake areas (10.94 ± 6.61 %) were detected only in LS174T tumors (Fig. 4C).

Histology confirmation of tissue necrosis, hypoxia, and blood lakes suggest that DCEUS and PA imaging should be used in conjunction to separate regions of necrosis from hypoxia

In addition to H&E and CD31, both LS147T and PC3 tumors were also stained for CA9 to validate and compare patterns of hypoxia in the two tumor types. To present a complete histologic image displaying necrotic regions, blood lakes, and hypoxic regions, CA9 and H&E images were segmented and overlaid. A representative composite histologic image of each tumor is shown in Fig. 5A, along with the matching oxygen saturation and perfusion maps for comparison. Red blood cells are shown as white dots, but are not observable in PC3 tumors in the displayed resolution, due to the small sizes of the vessels.

Figure 5.

Hypoxia and oxygen saturation patterns in hind-limb tumor models. A, examples of composite histologic image of LS174T and PC3 tumors combining information from H&E, CA9, and CD31 stains (top) are presented against SO2 images (middle panels) and perfusion maps (bottom panels). The cores of LS174T (top left) are embedded with blood lakes (red), small necrotic regions (blue), and large hypoxic regions (yellow). Viable tumor tissue is presented in black and red blood cells appear as white dots. In agreement, the cores of LS174T tumors are characterized by low SO2 (middle left). The perfusion map of the LS174T tumor shows large vessels in the tumor core separated by nonperfused areas. In contrast, only small patches of hypoxic cells are found in PC3 tumors concentrated around the rims of the vast necrotic regions (top right). The absence of hemoglobin in necrotic regions inside PC3 tumors and low signal originating from small vessels can explain the high percentage of tumor area with low PA signal in PC3 tumors (bottom middle). The perfusion map in the right bottom panel resembles the histologic composition of the tumor. Scale bars, 1 mm. B, histograms of SO2 values in LS174T and PC3 tumors normalized to the total tumor area. C, histologic analysis of hypoxia in LS174T and PC3 tumors show significant difference in percentage of hypoxic area. *, P < 0.01.

Figure 5.

Hypoxia and oxygen saturation patterns in hind-limb tumor models. A, examples of composite histologic image of LS174T and PC3 tumors combining information from H&E, CA9, and CD31 stains (top) are presented against SO2 images (middle panels) and perfusion maps (bottom panels). The cores of LS174T (top left) are embedded with blood lakes (red), small necrotic regions (blue), and large hypoxic regions (yellow). Viable tumor tissue is presented in black and red blood cells appear as white dots. In agreement, the cores of LS174T tumors are characterized by low SO2 (middle left). The perfusion map of the LS174T tumor shows large vessels in the tumor core separated by nonperfused areas. In contrast, only small patches of hypoxic cells are found in PC3 tumors concentrated around the rims of the vast necrotic regions (top right). The absence of hemoglobin in necrotic regions inside PC3 tumors and low signal originating from small vessels can explain the high percentage of tumor area with low PA signal in PC3 tumors (bottom middle). The perfusion map in the right bottom panel resembles the histologic composition of the tumor. Scale bars, 1 mm. B, histograms of SO2 values in LS174T and PC3 tumors normalized to the total tumor area. C, histologic analysis of hypoxia in LS174T and PC3 tumors show significant difference in percentage of hypoxic area. *, P < 0.01.

Close modal

Amidst the areas of necrosis and blood lakes prevalent in the cores of LS174T tumors, there are also vast areas of hypoxia (Fig. 5A, top left). This is correlative of the matching oxygen saturation and perfusion maps (Fig. 5A, left, middle, and bottom plots), where regions of low SO2 were found to concentrate at the core of the tumors, where only sparse large vessels were found. Areas of necrosis and hypoxia could not be differentiated using oxygen saturation maps alone, as low SO2 was observed in the core of the tumors even in the presence of functional vessels. In contrast to LS174T, only small and scarce areas of hypoxia were found in PC3 tumors, surrounding the borders of necrotic tumor regions (Fig. 5A, top right). Histology results were in agreement with matched oxygen saturation maps, showing areas of low to no SO2 at the tumor core (Fig. 5A, right middle). However, in this case, low PA signal was a result of tissue necrosis and sparse vessels, which limited the estimation of SO2. This was confirmed by contrast-enhanced perfusion maps showing large nonperfused areas at the center of PC3 tumors. To quantitatively evaluate the difference between the SO2 patterns in the two tumor types, histograms of SO2 values normalized to the total tumor area were calculated (Fig. 5B). LS174T tumor histogram showed higher frequencies of low SO2 and much lower frequencies of high SO2. In contrast, PC3 tumors displayed relatively similar frequencies of SO2 across the board, but had much lower overall detected SO2, suggesting that markedly larger regions in PC3 tumors did not produce sufficient PA signal. Quantification of CA9 stains showed significantly larger hypoxic regions in LS174T tumors compared with PC3 tumors (Fig. 5C, n = 6, P = 0.0022).

Functional imaging reveals reduced perfusion and oxygen saturation in response to Oxi-4503 treatment

Contrast assessment of tumor perfusion is shown in Fig. 6A, which displays sample images of an Oxi-4503–treated mouse before and after treatment. In the pretreatment image, contrast signal enhancement can be observed in the surrounding tissue and throughout the periphery of the tumor. This, however, is changed 4 hours after the injection of Oxi-4503, showing a very dark tumor center with much less signs of enhancement. As illustrated in Fig. 6B, both groups of mice showed similar levels of peak enhancement and wash-in rate before treatment. However, when tumor perfusion was measured again 4 hours after, a significant change was observed in the Oxi-4503–treated mice, with an 82.1% and 80.5% decrease in tumor blood volume and flow rate, respectively, P < 0.01. This statistically significant change was not observed with the control mice.

Figure 6.

Functional imaging of reaction to Oxi-4503 treatment. A, contrast enhanced ultrasound images of Oxi-4503 treated mice before injection and after injection (top) compared against parametric images of SO2 in Oxi-4503–treated mice before injection and after injection (bottom). B, analysis of peak enhancement and wash-in rate in mice injected with saline or Oxi-4503, before and 4 hours after treatment. *, P < 0.01; **, P < 0.001 using paired- and independent-sample t test. C, analysis of oxygen saturation in mice injected with saline or Oxi-4503 before and 4 hours after treatment. *, P < 0.01; **, P < 0.001 using paired- and independent-sample t test. The scale bars are 1 mm long.

Figure 6.

Functional imaging of reaction to Oxi-4503 treatment. A, contrast enhanced ultrasound images of Oxi-4503 treated mice before injection and after injection (top) compared against parametric images of SO2 in Oxi-4503–treated mice before injection and after injection (bottom). B, analysis of peak enhancement and wash-in rate in mice injected with saline or Oxi-4503, before and 4 hours after treatment. *, P < 0.01; **, P < 0.001 using paired- and independent-sample t test. C, analysis of oxygen saturation in mice injected with saline or Oxi-4503 before and 4 hours after treatment. *, P < 0.01; **, P < 0.001 using paired- and independent-sample t test. The scale bars are 1 mm long.

Close modal

PA assessment of tumor oxygenation is shown in the two lower panels of Fig. 6A, which display sample images of an Oxi-4503–treated mouse before and after treatment. Areas of high oxygenation are shown in bright red, and dark red for low oxygenation regions, while black represents no oxygenation or low PA signal. From the images, it can be observed that the tumor is well oxygenated around the periphery, with very low oxygenation toward the center. This, however, is changed after the injection of Oxi-4503, with very low to no SO2 signal seen throughout the tumor 4 hours after treatment. This was expected, as Oxi-4503 is known to induce localized hypoxia inside the tumors as a result of vessel collapse. The averaged percentage of SO2 for Oxi-4503–treated mice showed a 37.2% decrease in tumor oxygenation after treatment, with P < 0.01 (Fig. 6C). This statistically significant change was not observed with the control.

Histology validation of contrast and PA assessments of response to Oxi-4503 treatment

All tumor slices taken from control and Oxi-4503–treated mice were stained for CA9 and CD31 to validate contrast and PA findings. Shown in Fig. 7A are representative images from H&E and CA9 stains for each group. The control H&E stain displays a large nonviable region in the inner core of the tumor (light purple), and the adjacent image shows brown positive staining for CA9 localized as a rim lining this inner core. However, the Oxi-4503–treated sample presented a different pattern, with multiple foci of CA9-positive regions found throughout the tumor and in viable regions as shown by its H&E staining. Figure 7C demonstrates consistency in n = 6 with approximately 40% more CA9 staining than the control group. Figure 7B presents sample sections of tumor slices from each group stained for CD31, with positive brown staining for vessels. As shown in the figure, the control group appears to have more vessels per area than the drug-treated group. This finding was confirmed with all of the animals, with Fig. 7D showing that the control group had an approximately 2-fold higher vessel density than the Oxi-4503–treated group.

Figure 7.

Histopathologic signs of Oxi-4503 treatment. A, histologic staining (H&E and CA9) of tumors from control and Oxi-4503–treated mice. Histologic staining of H&E and CA9 reveals regions of nonviable tissue (H&E staining in light purple) and hypoxia (CA9 staining in brown). Note that the control group displays a large nonviable region in the inner core of the tumor surrounded by localized brown positive staining for CA9 while the Oxi-4503 presented a different pattern, with multiple foci of CA9-positive regions found throughout the tumor. B, histologic CD31 staining shows microvessel density distribution in regions of tumors from Oxi-4503–treated and control mice. The treated tumors show lower microvessel density compared with the control tumors. Images are taken at ×20 magnification. C, histologic analysis of hypoxia in Oxi-4503–treated and control tumors. *, P < 0.01. D, histologic analysis of microvessel density in tumors of Oxi-4503–treated and control mice. **, P < 0.001.

Figure 7.

Histopathologic signs of Oxi-4503 treatment. A, histologic staining (H&E and CA9) of tumors from control and Oxi-4503–treated mice. Histologic staining of H&E and CA9 reveals regions of nonviable tissue (H&E staining in light purple) and hypoxia (CA9 staining in brown). Note that the control group displays a large nonviable region in the inner core of the tumor surrounded by localized brown positive staining for CA9 while the Oxi-4503 presented a different pattern, with multiple foci of CA9-positive regions found throughout the tumor. B, histologic CD31 staining shows microvessel density distribution in regions of tumors from Oxi-4503–treated and control mice. The treated tumors show lower microvessel density compared with the control tumors. Images are taken at ×20 magnification. C, histologic analysis of hypoxia in Oxi-4503–treated and control tumors. *, P < 0.01. D, histologic analysis of microvessel density in tumors of Oxi-4503–treated and control mice. **, P < 0.001.

Close modal

In recent years, scanners combining several noninvasive imaging modalities are becoming increasingly popular in clinical and preclinical imaging due to their ability to provide additional and complimentary information on the imaged tumor (e.g., refs. 16 and 17). In this study, we examined the advantages of using DCEUS combined with PA as a diagnostic tool, and as a tool for monitoring antivascular treatment. It was shown that a multimodality imaging scheme combining DCEUS and PA produce additional information about the presence of viable tissue, hemorrhagic regions, and necrotic areas. To the best of our knowledge, this level of discrimination has not previously been published with the individual modalities.

Correlation and discrepancies between perfusion maps and oxygen saturation patterns

Previous studies have shown using histology, the spatial relationship between hypoxic and perfused tissue regions in specific xenograft tumors (18). Hypoxia was shown to develop in tissue separated from functional vessels by a distance of approximately 100 μm. On the basis of this we expected to find a correlative relationship between perfusion and SO2 in our imaging maps. The pixel-wise correlation between parametric perfusion and oxygen saturation maps in both LS174T and PC3 tumors were moderately good (r = 0.63 ± 0.06 and r = 0.49 ± 0.10, respectively, n = 6). However, it seems that a global linear regression does not fully capture the relation between these functional parameters. Moreover, in the LS174T tumors, low SO2 was detected throughout the core even though large functional vessels were apparent in the DCEUS scans. Histopathologic analysis combining information from H&E, CA9, and CD31 stains showed the presence of hypoxia but not necrosis in the vicinity of functional vessels, in accordance with previous studies. This inconsistency between PA SO2 estimations and histology results may be due to the fact that tumor cells only show hypoxic stress under extremely low oxygenation levels (2). The resolution needed for detection of extremely low SO2 levels could be challenging for nontomographic PA scanners. These challenges are especially dominant in higher depths, due to the wavelength-dependent absorption (19).

DCEUS perfusion maps reveal information on vascular morphology

As single vessels could not be resolved in PA scans, DCEUS perfusion maps were crucial in detecting difference in vascular morphology between different regions of each tumor, and between the two tumors types. Resolvable large vessels were detected in the core of LS174T tumors, while the rim of these tumors had relatively uniform perfusion. These results were validated using histology, which showed large deformed functional vessels in the center of LS174T tumors, surround by areas of hypoxia (Figs. 3A and 5A, respectively). Previous studies have shown that larger and leakier vessels are generally associated with regions of hypoxia, affirming our observations (20). In contrast to LS174T tumors, large resolvable blood vessels could not be found in any of the PC3 DCEUS scans, suggesting that the tumors were composed of relatively smaller, compact vessels. This was supported by histology showing small and densely clustered vessels in the viable regions of PC3 tumors, with minimal signs of hypoxia (Figs. 3A and 5A, respectively). A certain (global) indication of the hypoxic nature of the LS174T tumors can be derived from the histograms of SO2 measurements from the two tumors (Fig. 5B). These histograms show markedly higher frequencies of low SO2 values in LS174T tumors. This is contrasted by PC3 tumors, showing extensive areas that did not produce sufficient PA signal, indicative of necrotic regions or dysfunctional vasculature. The histologic results were consistent among all tumors, with quantitative analysis showing significantly higher density of large and open lumen vessels in LS174T tumors. Previous studies have shown a positive correlation between the number of open lumen and perfusion (21, 22). A proposed reason for increased number of open lumen vessels was that there may be less compression from surrounding tumor cells.

Combined PA and DCEUS imaging enable distinction between necrotic regions and blood lakes

The last aspect of the spatial correlation between DCEUS and PA imaging presented in this study is the ability to distinguish between necrotic regions and blood lakes. Both nonviable tissue regions and blood lakes appear in DCEUS scans as nonperfused regions. Comparison between perfusion maps and PA-derived hemoglobin concentration estimations facilitate the classification of nonperfused areas into two categories: necrotic regions and blood lakes. While necrotic regions include at most sparse clusters of extravasated erythrocytes, blood lakes are composed of large populations of red blood cells that have escaped from leaky vessels. By using DCEUS in combination with PA imaging the two tissue characteristics can be separated. Nonperfused regions with high concentration of hemoglobin would likely signify areas with blood lakes, while nonperfused regions with low hemoglobin concentration would more likely signify regions of necrosis. Furthermore, correlation between perfusion and hemoglobin concentration can also help indicate the presence of blood lakes verses necrotic tissue. In tissues where substantial amount of blood lakes are present, correlation would likely be low, such as observed in LS174T tumors (Fig. 2B). On the other hand, higher correlation between perfusion and hemoglobin concentration would likely appear in necrosis dominated tissue, such as PC3 tumors. Our observations were validated using histology, where large blood lakes dominated LS174T tumors (Fig. 4C), while necrosis was significantly higher in PC3 tumors (Fig. 4B).

Multimodality imaging facilitates functional assessment of response to Oxi-4503 treatment

The ability of PA and DCEUS to facilitate functional assessment of response to Oxi-4503 treatment was examined; Oxi-4503 was chosen for its well-researched effects of causing tumor vessel collapse, leading to localized hypoxia, and eventually hypoxia-induced necrosis (23, 24).

DCEUS imaging was able to detect the perfusion changes in the drug-treated group showing almost no microbubble signal enhancement 4 hours after Oxi-4503 injection. The 80% decrease in both tumor blood volume and flow rate indicators was comparable with previous studies that demonstrated only a small viable rim remains in the tumor after treatment with Oxi-4503 (25). There were also, however, slight peak enhancement and wash-in rate changes observed in the control group. These changes may have been a result of slight plane shifts, or tumor orientation shifts between the two imaging time points. We subsequently looked at CD31 staining of the tumor tissue to validate our contrast findings. The histologic analysis showed a 50% decrease in the number of vessel per area in the Oxi-4503 mice compared with the control. This level of microvessel density change was slightly lower than our quantified contrast change, which may be due to the fact that CD31 stains for all existing endothelial cells, whereas DCEUS address functional vasculature in real time.

Relative quantification of SO2 was performed using reconstructed parametric maps of dual-wavelength PA imaging. We were able to observe a noticeable decrease of around 40% in tumor oxygenation with Oxi-4503–treated mice, whereas changes were negligible in the control mice. Subsequent validation by histology also showed 45% less hypoxia expression in the control mice compared to Oxi-4503 treated. The presence of large viable hypoxic regions 4 hours after the Oxi-4503 treatment is reasonable as some tissues can survive for more than 5 hours without blood supply before undergoing necrosis (26).The histologic findings in this study support PA findings of drug-induced hypoxia.

Limitations and future research directions

There are two main limitations to the imaging methods studied in this work. The first is the limited resolution of nontomographic PA imaging, both spatially and in separating low SO2 values. New semitomographic methods that enable the combination of DCEUS and PA are being developed and the effect of improved PA resolution on the capabilities of these multimodality scanners is a subject of future research. The second limitation is that currently, DCEUS imaging using bolus injections is limited to a single imaging plane. 3D ultrasound probes have gained popularity in the past few years and the combination of 3D DCEUS and 3D PA could decrease the variability of the scans and enable the study of the 3D morphology of the vasculature.

In summary, DCEUS and PA produced high resolution maps of perfusion and SO2 and hemoglobin concentration. These physiologic parameters provide important information about morphology and functionality of the vasculature feeding the tumor and its microenvironment. In addition, the combination of these two modalities enables the classification of tumor area to viable regions, necrotic tissue regions, and blood lakes. This detection of blood lakes could aid in determining the leakiness of the tumor. Furthermore, DCEUS and PA imaging were able to monitor, with high sensitivity, known drug-induced changes in the tumors, with close correlations to the current gold standard histology measurements. These results were validated quantitatively using histology. Taken together, our findings strongly support that DCEUS and PA imaging could be effective tools for the quantitative functional assessments of the microenvironment of various tumor models.

F.S. Foster reports receiving a commercial research grant from and is a consultant/advisory board member for VisualSonics. No potential conflicts of interest were disclosed by the other authors.

Conception and design: A. Bar-Zion, M. Yin, D. Adam, F.S. Foster

Development of methodology: A. Bar-Zion, M. Yin, D. Adam, F.S. Foster

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Bar-Zion, M. Yin, F.S. Foster

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Bar-Zion, M. Yin

Writing, review, and/or revision of the manuscript: A. Bar-Zion, M. Yin, D. Adam, F.S. Foster

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A. Bar-Zion, F.S. Foster

Study supervision: D. Adam, F.S. Foster

The authors thank the Canadian Institutes of Health Research, the Terry Fox Foundation, and VisualSonics Inc. for financial support.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hanahan
D
,
Weinberg
RA
. 
Hallmarks of cancer: the next generation
.
Cell
2011
;
144
:
646
74
.
2.
Höckel
M
,
Vaupel
P
. 
Tumor hypoxia: definitions and current clinical, biologic, and molecular aspects
.
J Natl Cancer Inst
2001
;
93
:
266
76
.
3.
Eckersley
RJ
,
Chin
CT
,
Burns
PN
. 
Optimising phase and amplitude modulation schemes for imaging microbubble contrast agents at low acoustic power
.
Ultrasound Med Biol
2005
;
31
:
213
9
.
4.
Wilson
SR
,
Burns
PN
. 
Microbubble-enhanced US in body imaging: what role?
Radiology
2010
;
257
:
24
39
.
5.
Hudson
JM
,
Williams
R
,
Tremblay-Darveau
C
,
Sheeran
PS
,
Milot
L
,
Bjarnason
GA
, et al
Dynamic contrast enhanced ultrasound for therapy monitoring
.
Eur J Radiol
2015
;
84
:
1650
7
.
6.
Xu
M
,
Wang
LV
. 
Photoacoustic imaging in biomedicine
.
Rev Sci Instrum
2006
;
77
:
041101
.
7.
Beard
P
. 
Biomedical photoacoustic imaging
.
Interface Focus
2011
;
1
:
602
31
.
8.
Zhang
HF
,
Maslov
K
,
Sivaramakrishnan
M
,
Stoica
G
,
Wang
LV
. 
Imaging of hemoglobin oxygen saturation variations in single vessels in vivo using photoacoustic microscopy
.
Appl Phys Lett
2007
;
90
:
053901
.
9.
Hu
S
,
Wang
LV
. 
Photoacoustic imaging and characterization of the microvasculature
.
J Biomed Opt
2010
;
15
:
011101
.
10.
Hashizume
H
,
Baluk
P
,
Morikawa
S
,
McLean
JW
,
Thurston
G
,
Roberge
S
, et al
Openings between defective endothelial cells explain tumor vessel leakiness
.
Am J Pathol
2000
;
156
:
1363
80
.
11.
Gerling
M
,
Zhao
Y
,
Nania
S
,
Norberg
KJ
,
Verbeke
CS
,
Englert
B
, et al
Real-time assessment of tissue hypoxia in vivo with combined photoacoustics and high-frequency ultrasound
.
Theranostics
2014
;
4
:
604
13
.
12.
Munoz
R
,
Man
S
,
Shaked
Y
,
Lee
CR
,
Wong
J
,
Francia
G
, et al
Highly efficacious nontoxic preclinical treatment for advanced metastatic breast cancer using combination oral UFT-cyclophosphamide metronomic chemotherapy
.
Cancer Res
2006
;
66
:
3386
91
.
13.
Bar Zion
A
,
Tremblay-Darveau
C
,
Yin
M
,
Dan
A
,
Foster
F
. 
Denoising of contrast enhanced ultrasound cine sequences based on a multiplicative model
.
IEEE Trans Biomed Eng
2015
;
99
:
1
1
.
14.
Wang
D
,
Stockard
CR
,
Harkins
L
,
Lott
P
,
Salih
C
,
Yuan
K
, et al
Immunohistochemistry in the evaluation of neovascularization in tumor xenografts
.
Biotech Histochem
2008
;
83
:
179
89
.
15.
Vanzulli
S
,
Gazzaniga
S
,
Braidot
MF
,
Vecchi
A
,
Mantovani
A
,
Wainstok
DCR
. 
Detection of endothelial cells by MEC 13.3 monoclonal antibody in mice mammary tumors
.
Biocell
1997
;
21
:
39
46
.
16.
Bar-Shalom
R
,
Yefremov
N
,
Guralnik
L
,
Gaitini
D
,
Frenkel
A
,
Kuten
A
, et al
Clinical performance of PET/CT in evaluation of cancer: additional value for diagnostic imaging and patient management
.
J Nucl Med
2003
;
44
:
1200
9
.
17.
Deroose
CM
,
De
A
,
Loening
AM
,
Chow
PL
,
Ray
P
,
Chatziioannou
AF
, et al
Multimodality imaging of tumor xenografts and metastases in mice with combined small-animal PET, small-animal CT, and bioluminescence imaging
.
J Nucl Med
2007
;
48
:
295
303
.
18.
Rijken
PFJW
,
Bernsen
HJJA
,
Peters
JPW
,
Hodgkiss
RJ
,
Raleigh
JA
,
van der Kogel
AJ
. 
Spatial relationship between hypoxia and the (perfused) vascular network in a human glioma xenograft: a quantitative multi-parameter analysis
.
Int J Radiat Oncol
2000
;
48
:
571
82
.
19.
Needles
A
,
Heinmiller
A
,
Sun
J
,
Theodoropoulos
C
,
Bates
D
,
Hirson
D
, et al
Development and initial application of a fully integrated photoacoustic micro-ultrasound system
.
IEEE Trans Ultrason Ferroelectr Freq Control
2013
;
60
:
888
97
.
20.
Jain
RK
. 
Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy
.
Science
2005
;
307
:
58
62
.
21.
Chauhan
VP
,
Martin
JD
,
Liu
H
,
Lacorre
DA
,
Jain
SR
,
Kozin
SV
, et al
Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels
.
Nat Commun
2013
;
4
:
2516
.
22.
Stylianopoulos
T
,
Jain
RK
. 
Combining two strategies to improve perfusion and drug delivery in solid tumors
.
Proc Natl Acad Sci U S A
2013
;
110
:
18632
7
.
23.
Salmon
HW
,
Siemann
DW
. 
Effect of the second-generation vascular disrupting agent OXi4503 on tumor vascularity
.
Clin Cancer Res
2006
;
12
:
4090
4
.
24.
Siemann
DW
,
Horsman
MR
. 
Vascular targeted therapies in oncology
.
Cell Tissue Res
2009
;
335
:
241
8
.
25.
Daenen
LG
,
Shaked
Y
,
Man
S
,
Xu
P
,
Voest
EE
,
Hoffman
RM
, et al
Low-dose metronomic cyclophosphamide combined with vascular disrupting therapy induces potent anti-tumor activity in preclinical human tumor xenograft models
.
Mol Cancer Ther
2009
;
8
:
2872
81
.
26.
Petrasek
PF
,
Homer-Vanniasinkam
S
,
Walker
PM
. 
Determinants of ischemic injury to skeletal muscle
.
J Vasc Surg
1994
;
19
:
623
31
.