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Functional CT for Quantifying Tumor Perfusion in Antiangiogenic Therapy in a Rat Model¹

¹ From the Departments of Diagnostic Radiology (Z.K., S.P., S.K., Y.T., C.C.) and Surgical Oncology and Cancer Biology (L.M.E.), University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Radiology, Lawson Research Institute, St Joseph's Health Center, London, Ontario, Canada (T.Y.L.). From the 2003 RSNA Annual Meeting. Received July 25, 2004; revision requested September 30; revision received October 27; accepted November 12. Supported by grants CA-90270 from the National Institutes of Health and CA-90810 from the National Cancer Institute. Address correspondence to Z.K. (e-mail: zkan{at}di.mdacc.tmc.edu).

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
PURPOSE: To determine the histologic basis of perfusion parameters measured at functional computed tomography (CT) and to examine the relationship between changes in perfusion and changes in histologic parameters after antiangiogenic therapy in a rat model.

MATERIALS AND METHODS: This study had institutional animal care and use committee approval. Among 20 Fischer rats with implanted FN13762 tumors in the liver, 10 were treated with SU5416, a tyrosine kinase inhibitor of vascular endothelial growth factor receptor, and 10 were treated with the diluent only as control rats. Six rats chosen at random from each group underwent functional CT for the measurement of tumor blood flow, blood volume, mean transit time, and permeability–surface area product. Tumor tissue slides corresponding to functional CT sections were examined to measure tumor microvascular density, number of luminal vessels, vascular perimeter, and vascular area. Two-tailed Student t testing was used to determine differences in growth, numbers of metastases to major organs, vascularity, and perfusion between SU5416-treated and control tumors. Pearson correlation coefficients were used to investigate relationships between vascular parameters.

RESULTS: Mean tumor volume and number of metastases, respectively, were lower in SU5416-treated rats than in control rats (1580 mm³ ± 830 [standard deviation] vs 2330 mm³ ± 960 and 22.4 ± 11.0 vs 35.2 ± 17.3); however, these differences were not significant (P = .084 and P = .079). Mean tumor microvascular density was significantly lower in SU5416-treated rats than in control rats (6.4 vessels per field ± 4.6 vs 17.2 vessels per field ± 7.5, P < .001); however, vessel perimeter and vessel area, respectively, were significantly larger in treated rats than in control rats (470 µm per field ± 320 vs 360 µm per field ± 270, P = .02; and 4010 µm² per field ± 2990 vs 2230 µm² per field ± 1750, P = .001). Significant correlations were observed between microvascular density and vessel perimeter and area (r = 0.59 and r = 0.25, respectively; P < .01 for both) in SU5416-treated tumors but not control tumors. Blood flow, blood volume, and permeability–surface area product at functional CT were significantly higher in SU5416-treated tumors than in control tumors (P < .001 for all).

CONCLUSION: These results validate the idea that functional CT can help quantify the perfusion function of mature vessels but not changes in microvessel density in antiangiogenic therapy.

© RSNA, 2005

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
Tumor-associated neovascularization (angiogenesis) is essential for tumor growth, invasion, and metastasis. Targeting angiogenesis to deprive the tumor of its blood supply has emerged as a promising approach for the treatment of human neoplasia (1–4). Tumor angiogenesis is a complex, multistep process involving endothelial cell proliferation and migration, capillary formation and maturation, coordinated remodeling of extracellular tumor stroma, and anastomosis with the preexisting host vasculature.

Various angiogenic factors that mediate specific steps in the angiogenic process have been identified. Among them, vascular endothelial growth factor (VEGF) is the most potent proangiogenic factor (4,5). VEGF stimulates endothelial cell replication and migration, increases vascular permeability, and maintains the integrity of tumor vessels. This factor functions through the binding of its high-affinity receptors, which are expressed almost exclusively on endothelial cells. VEGF and its receptors are overexpressed in various human cancers, such as those of the breast, colon, and prostate (4,5). Thus, targeting VEGF or its receptors is an important strategy used in the development of antiangiogenic agents (4,6). More than 300 antiangiogenic agents have been produced, more than 50 are currently in various stages of clinical trials, and the first antiangiogenic drug—bevacizumab (Avastin; Genentech, South San Francisco, Calif), an anti-VEGF monoclonal antibody—has been approved by the U.S. Food and Drug Administration for the treatment of patients with colorectal cancer in combination with chemotherapy (7,8).

An urgent need in the study of antiangiogenic therapy is to develop a validated technique for evaluating the antiangiogenic effects of such treatments in tumors (9). Tumor vascularity, as assessed with microvascular density (MVD)—particularly that measured in the most neovascular area (the "hot spot") of a tumor—is currently the histologic marker for assessing angiogenic activity because of its proved relationship with tumor growth and metastasis and the patient's prognosis (9–12). However, the study of MVD requires repeated tumor biopsy that is not pragmatic in clinical practice because of its invasiveness, its limited tumor coverage, and the fact that it does not yield information about tumor perfusion (9,12). Furthermore, MVD has not been a good measure of antiangiogenic activity (13).

Functional computed tomography (CT) is a relatively recently developed imaging technology that enables quantification of tissue hemodynamics (14–16). By acquiring a continual cine scan at a fixed tissue location after an injection of contrast medium, a time-attenuation curve is generated. On the basis of the linear relationship between the CT-measured enhancement of the contrast medium and the concentration of the contrast medium in the blood, analysis of the time-attenuation curve with appropriate kinetic modeling enables quantification of various aspects of tissue perfusion. In addition, automated image analysis on a pixel-by-pixel basis generates parametric images (ie, functional maps) on which quantitative data of tumor perfusion are displayed.

The accuracy and reproducibility of functional CT perfusion measurements have been validated with the classic microsphere output technique, with positron emission tomography, and with other modalities (14–19). In addition, functional CT has been shown to be superior to conventional imaging in the early detection and differentiation of ischemic lesions and occult tumors because it enables the quantification of changes in tissue perfusion, which occur earlier than structural changes do (20–22).

The purpose of our study was to determine the histologic basis of the perfusion parameters measured at functional CT and to examine the relationship between changes in perfusion and changes in histologic parameters after antiangiogenic therapy.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
Sugen (South San Francisco, Calif; now a Pfizer Company) provided the SU5416—an investigational agent—used in this study. However, the authors had control over the data and the information reported for publication.

Animal and Tumor Model
Eight-week-old female Fischer 344 rats (weight range, 180–200 g) were obtained from Charles River Laboratories (Wilmington, Mass) and maintained in the animal care facility of the Department of Veterinary Medicine and Surgery of the University of Texas M. D. Anderson Cancer Center. The rats were caged in groups of three and fed with standard rodent food and water ad libitum. Animal experiments were conducted in accordance with the protocol approved by our institutional animal care and use committee.

FN13762 murine mammary cancer cells (23) were inoculated into the mammary fat pad of a female Fischer 344 rat by injecting 1 x 10⁶ cells in 0.1 mL of phosphate-buffered saline. When the tumor grew to approximately 10 mm, this animal was euthanized, and the tumor was harvested for implantation into the livers of the 20 test rats.

After an intraperitoneal injection of 50 mg per kilogram of body weight of pentobarbital (Nembutal; Abbott Laboratories, North Chicago, Ill) for anesthesia, an incision was made in the upper middle part of the abdomen of each rat. The middle hepatic lobe was exposed, and a small cut was made on the liver surface with a sharp scalpel. Fresh tumor tissue (2 x 2 x 2 mm) was embedded 5 mm deep in the liver parenchyma. The cut was then closed by applying pressure with a cotton-tipped applicator, and the abdomen was closed with sutures. The procedure was performed in aseptic surgical conditions. The rats were sorted randomly into two groups: 10 rats were treated with the antiangiogenic SU5416, and the other 10 rats served as control rats. Six rats for each group were randomly chosen to undergo functional CT. Tumor inoculations in all 20 rats were performed by one of the authors (Z.K.).

Antiangiogenic Therapy
Starting on the third day after tumor implantation, 15 mg/kg of SU5416 dissolved in dimethyl sulfoxide (Fisher Scientific, Fair Lawn, NJ) was administered intraperitoneally daily for 14 days at a concentration of 10 mg/mL (24–28). The control rats were treated with an equivalent volume of dimethyl sulfoxide without SU5416. The functional CT examinations were performed in the designated rats 1 day after the last drug or diluent administration. The treatments in all 20 rats were performed by one of the authors (Z.K.).

Functional CT and Quantification of Perfusion Parameters
Before functional CT was performed, silicone rubber tubing with an inside diameter of 0.3 mm and an outside diameter of 0.5 mm (Dow Corning, Midland, Mich) was inserted into and secured in the jugular vein of each rat for contrast agent injection. Animals were placed in the center of the CT scanner (Lightspeed; GE Medical Systems, Milwaukee, Wis) and underwent unenhanced scout scanning through the tumors to enable selection of the appropriate transverse level for perfusion scanning with functional CT. Single-location multisection (four detector rows, 1.25-mm-thick sections) cine CT scanning was begun 3 seconds before a bolus of 1.5 mL/kg of iodinated contrast medium (Optiray 320; Mallinckrodt, St Louis, Mo) was injected into the jugular vein and was continued for 50 seconds at a speed of 0.8 second per rotation. All images were acquired by using a 120-kVp tube voltage, an 80-mA tube current, and a 90-mm field of view. Contrast agent injections in all 12 rats that underwent CT studies were performed by one of the authors (C.C.).

The data obtained at functional CT were then reconstructed onto a 512 x 512-pixel image matrix with an improved temporal resolution of 0.4 second between images. The reconstructed image data were then transferred to an image workstation (Advantage Windows; GE Medical Systems) for calculating perfusion parameters. Absolute values of four perfusion parameters—blood flow (in milliliters per minute per 100 g of rat body weight), blood volume (in milliliters per 100 g), mean transit time (in seconds), and permeability–surface area product (in milliliters per minute per 100 g)—were measured by using perfusion software (Perfusion II; GE Medical Systems). The parametric map images were created by using the highest spatial resolution pixel-by-pixel calculation technique (14) (Fig 1). Before the parametric perfusion values in a tumor were measured, a cursor indicating a 6-pixel region of interest was placed within the aorta to determine the enhancement value of arterial input (14). A region of interest was then drawn on the raw CT images on which the whole tumor was delineated by contrast enhancement. In addition to measuring the entire tumor, we marked as functional hot spots on the parametric images the areas inside the tumor where the highest blood flow, blood volume, mean transit time, and permeability–surface area product values were measured. Two radiologists (S.P., S.K.) performed the measurements independently, and interobserver variance was analyzed.

View larger version (66K): [in this window] [in a new window] [Download PPT slide]   Figure 1a. (a) Contrast material–enhanced CT image shows a rat tumor (arrows) in the liver delineated by a region of interest drawn according to the contrast enhancement. (b–e) Functional CT map images of (b) blood flow, (c) blood volume, (d) mean transit time, and (e) permeability–surface area product show that the distribution of perfusion in the tumor (arrows) is heterogeneous and reveal the relationships between the perfusion parameters (eg, the fact that the mean transit time distribution is inversely related to blood flow). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (84K): [in this window] [in a new window] [Download PPT slide]   Figure 1b. (a) Contrast material–enhanced CT image shows a rat tumor (arrows) in the liver delineated by a region of interest drawn according to the contrast enhancement. (b–e) Functional CT map images of (b) blood flow, (c) blood volume, (d) mean transit time, and (e) permeability–surface area product show that the distribution of perfusion in the tumor (arrows) is heterogeneous and reveal the relationships between the perfusion parameters (eg, the fact that the mean transit time distribution is inversely related to blood flow). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (87K): [in this window] [in a new window] [Download PPT slide]   Figure 1c. (a) Contrast material–enhanced CT image shows a rat tumor (arrows) in the liver delineated by a region of interest drawn according to the contrast enhancement. (b–e) Functional CT map images of (b) blood flow, (c) blood volume, (d) mean transit time, and (e) permeability–surface area product show that the distribution of perfusion in the tumor (arrows) is heterogeneous and reveal the relationships between the perfusion parameters (eg, the fact that the mean transit time distribution is inversely related to blood flow). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 1d. (a) Contrast material–enhanced CT image shows a rat tumor (arrows) in the liver delineated by a region of interest drawn according to the contrast enhancement. (b–e) Functional CT map images of (b) blood flow, (c) blood volume, (d) mean transit time, and (e) permeability–surface area product show that the distribution of perfusion in the tumor (arrows) is heterogeneous and reveal the relationships between the perfusion parameters (eg, the fact that the mean transit time distribution is inversely related to blood flow). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (79K): [in this window] [in a new window] [Download PPT slide]   Figure 1e. (a) Contrast material–enhanced CT image shows a rat tumor (arrows) in the liver delineated by a region of interest drawn according to the contrast enhancement. (b–e) Functional CT map images of (b) blood flow, (c) blood volume, (d) mean transit time, and (e) permeability–surface area product show that the distribution of perfusion in the tumor (arrows) is heterogeneous and reveal the relationships between the perfusion parameters (eg, the fact that the mean transit time distribution is inversely related to blood flow). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

Immunohistochemical Microvascular Staining
Four tissue slices corresponding to the four CT sections obtained in each tumor were immunohistochemically stained for the specific endothelial antigen CD31. Frozen tumor slices were air dried for 60 minutes and then fixed in cold acetone for 5 minutes. After three washes in phosphate-buffered saline for 3 minutes each time, the slices were incubated with 3% hydrogen peroxide in methanol for 10 minutes to block endogenous peroxidase. Slices were then washed again with phosphate-buffered saline (three times for 3 minutes each time) and incubated for 20 minutes in a protein-blocking solution consisting of phosphate-buffered saline with 1% normal goat serum and 5% horse serum (Sigma Chemical, St Louis, Mo).

Then, the blocking solution was drained and the slices were incubated with a 1:100 dilution of mouse monoclonal anti-rat CD31 antibody (BD Biosciences Pharmingen, San Diego, Calif) at 4°C overnight. Slices were then rinsed again in phosphate-buffered saline (three times for 3 minutes each time) and incubated in the protein-blocking solution for 10 minutes before the addition of peroxidase-conjugated goat anti-mouse secondary antibody in a 1:50 dilution (Serotec, Indianapolis, Ind) for 1 hour at room temperature. The samples were then stained with chromogen diaminobenzidine (Stable DAB; Research Genetics, Huntsville, Ala) for 5–10 minutes and subsequently counterstained with Gill's No. 3 hematoxylin (Sigma-Aldrich, St Louis, Mo). The slices were then washed with distilled water, dried, and mounted with Universal Mount (Research Genetics). Another four tissue slices were stained for vascular pericytes with -smooth muscle actin (DakoCytomation, Carpinteria, Calif) and with hematoxylin and eosin.

Quantification of Histologic Vascular Parameters
Vascular parameters were counted and measured in the tumors of the 12 rats that underwent functional CT. The tumor on each slide was divided evenly into 20 areas; in each area, one field of 0.15 mm² (x200 magnification, 0.43 x 0.34 mm) was chosen randomly for the purposes of counting MVD, luminal vascular number (LVN), luminal vascular perimeter (LVP), and luminal vascular area (LVA). For the purpose of counting MVD, any CD31-highlighted endothelial cell or cell cluster that was clearly separate from adjacent tissue elements was counted as a single countable microvessel (10–12,29). We also counted the MVD in the vascular hot spot in the tumor. Briefly, the entire tumor was searched microscopically at low magnifications (x40 and x100), and the area containing the largest number of discrete microvessels (the hot spot) was identified; the MVD was then counted in that area by using high-power magnification (x200). For LVN, LVP, and LVA, only blood vessels with a discernible lumen lined by CD31-highlighted endothelium were counted. During counting, the LVN was further divided into two categories: the vessels with one or more complete layer(s) of -smooth muscle actin–stained pericytes and smooth muscle cells (regarded as relatively mature tumor vessels) and the vessels with an incomplete layer of pericytes (regarded as immature vessels). Two authors performed the vascular measurements (S.P., S.K.)

The microscopic images were captured and saved in a computer (Pentium 4–1300; Gateway, Poway, Calif) by using an AxioCam camera with a color charge-coupled device sensor (Carl Zeiss Vision, Munchen-Hallbugmoos, Germany) mounted on an Axioskop 2-plus microscope (Carl Zeiss, Thornwood, NY). The software used for vascular measurements was AxioVision Image Analysis (Carl Zeiss Vision).

Statistical Analysis
All data, including tumor size, number of metastases, the vascular parameters MVD, LVN, LVP, and LVA, and the perfusion parameters blood flow, blood volume, mean transit time, and permeability–surface area product, are presented as means ± standard deviations. The two-tailed Student t test was used to compare vascular and perfusion parameters between the SU5416-treated and the control tumors. Power analysis based on two-sample t testing was performed by using nQuery 2.0 (nQuery, Saugus, Mass). The Pearson correlation coefficient (r) was used to investigate the relationships between the vascular parameters in both the treated and the control groups. All statistical results were calculated by using SAS 8.0 (SAS, Cary, NC). A P value of less than .05 was considered to indicate a statistically significant difference.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
Effect of SU5416 Treatment on Tumor Growth and Metastasis
All 10 rats treated with SU5416 tolerated the anesthesia, surgery, and treatment well. One rat in the control group died of an unknown cause on the 10th day of dimethyl sulfoxide administration. In SU5416-treated rats, the average volume of liver tumors was 32% lower than the average volume in the control group (1580 mm³± 830 vs 2330 mm³± 960), and the average number of metastatic nodules was 36% lower than the average number in the control group (22.4 ± 11.0 vs 35.2 ± 17.3), but these differences were not statistically significant (P = .084 and P = .079, respectively).

Effects of SU5416 Treatment on Tumor Vascularity
The MVD values in both the entire tumor and the hot spot were significantly lower in the SU5416-treated group than in the control group (P < .001 for both) (Fig 2). The total number of luminal vessels and the number of immature luminal vessels did not differ significantly between the SU5416-treated and control groups (P = .09 and P = .30, respectively) (Table 1). The LVN of mature vessels and the LVP and LVA values of all luminal vessels in SU5416-treated tumors were significantly higher than those in control tumors (P = .004, P = .02, and P = .001, respectively) (Fig 3, Table 1).

View larger version (168K): [in this window] [in a new window] [Download PPT slide]   Figure 2a. Photomicrographs show that MVD (vessels stain positively for CD31 and thus appear brown) is markedly lower in (a) a tumor in an SU5416-treated rat than (b) a tumor in a control rat. More luminal vessels (arrows) were found in the tumors of SU5416-treated rats than in the tumors of control rats. (CD31 stain; original magnification, x400.)

View larger version (152K): [in this window] [in a new window] [Download PPT slide]   Figure 2b. Photomicrographs show that MVD (vessels stain positively for CD31 and thus appear brown) is markedly lower in (a) a tumor in an SU5416-treated rat than (b) a tumor in a control rat. More luminal vessels (arrows) were found in the tumors of SU5416-treated rats than in the tumors of control rats. (CD31 stain; original magnification, x400.)

View larger version (158K): [in this window] [in a new window] [Download PPT slide]   Figure 3a. Photomicrographs show that blood vessels in (a, b) a tumor of an SU5416-treated rat are covered with more layers of smooth muscle cells (arrows) than are the vessels in (c, d) a tumor (arrowheads) of a control rat. (Photomicrographs in a and c were obtained with a CD31 stain, original magnification, x200; those in b and d were obtained with -smooth muscle actin stain, original magnification, x200.)

View larger version (161K): [in this window] [in a new window] [Download PPT slide]   Figure 3b. Photomicrographs show that blood vessels in (a, b) a tumor of an SU5416-treated rat are covered with more layers of smooth muscle cells (arrows) than are the vessels in (c, d) a tumor (arrowheads) of a control rat. (Photomicrographs in a and c were obtained with a CD31 stain, original magnification, x200; those in b and d were obtained with -smooth muscle actin stain, original magnification, x200.)

View larger version (159K): [in this window] [in a new window] [Download PPT slide]   Figure 3c. Photomicrographs show that blood vessels in (a, b) a tumor of an SU5416-treated rat are covered with more layers of smooth muscle cells (arrows) than are the vessels in (c, d) a tumor (arrowheads) of a control rat. (Photomicrographs in a and c were obtained with a CD31 stain, original magnification, x200; those in b and d were obtained with -smooth muscle actin stain, original magnification, x200.)

View larger version (170K): [in this window] [in a new window] [Download PPT slide]   Figure 3d. Photomicrographs show that blood vessels in (a, b) a tumor of an SU5416-treated rat are covered with more layers of smooth muscle cells (arrows) than are the vessels in (c, d) a tumor (arrowheads) of a control rat. (Photomicrographs in a and c were obtained with a CD31 stain, original magnification, x200; those in b and d were obtained with -smooth muscle actin stain, original magnification, x200.)

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 4a. (a–d) Four consecutive functional CT blood flow map images obtained in an SU5416-treated rat show increased blood flow in the pericentral area of the tumor (arrows). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 4b. (a–d) Four consecutive functional CT blood flow map images obtained in an SU5416-treated rat show increased blood flow in the pericentral area of the tumor (arrows). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 4c. (a–d) Four consecutive functional CT blood flow map images obtained in an SU5416-treated rat show increased blood flow in the pericentral area of the tumor (arrows). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (83K): [in this window] [in a new window] [Download PPT slide]   Figure 4d. (a–d) Four consecutive functional CT blood flow map images obtained in an SU5416-treated rat show increased blood flow in the pericentral area of the tumor (arrows). The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (76K): [in this window] [in a new window] [Download PPT slide]   Figure 5a. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 5b. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (80K): [in this window] [in a new window] [Download PPT slide]   Figure 5c. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (90K): [in this window] [in a new window] [Download PPT slide]   Figure 5d. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (86K): [in this window] [in a new window] [Download PPT slide]   Figure 5e. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

View larger version (85K): [in this window] [in a new window] [Download PPT slide]   Figure 5f. Functional CT blood flow map images show increased blood flow in the tumors (arrows) of (a–c) SU5416-treated rats than in the tumors of (d–f) control rats. The increase in blood flow is mostly in the pericentral area. The color spectrum indicates the value of the perfusion parameter, ranging from high (red) to low (blue).

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

A functional CT study yields perfusion measurements that complement morphologic information and offers an ideal noninvasive method of monitoring antiangiogenic therapy. If we are to use tumor perfusion to evaluate antiangiogenic therapy, it is essential to verify the relationship between tumor perfusion and tumor angiogenesis. Currently, the MVD is the most commonly used histologic marker of tumor angiogenesis because of its prognostic value (9–12). Nevertheless, the relationship between tumor perfusion and tumor vasculature and the answer to the question of whether the tumor perfusion measurement could be used as a functional surrogate for MVD in the study of tumor angiogenesis are not known because of a lack of experimental confirmation.

We found that a significant reduction in MVD in both the entire tumor and its hot spot was associated with a significant increase in the LVN, LVP, and LVA values in tumors of SU5416-treated rats compared with these parameters in the tumors of control rats. At the same time, results of our functional CT perfusion studies showed that blood flow, blood volume, and permeability–surface area product measured in the entire tumor and hot spot were significantly higher in tumors of SU5416-treated rats than in tumors of control rats. These results indicate that functional CT perfusion measurements do not reflect changes in MVD but correspond instead to changes in the function of the mature tumor vasculature.

The very nature of a functional CT study could possibly explain its limitations in reflecting MVD changes. Functional CT involves recording the time-attenuation curve of the contrast medium in the circulation. Thus, we could only measure vessels with blood flow; functional CT could not reveal endothelial cells, immature vessels, or even mature vessels without the presence of blood flow. According to the standard method of counting it, the MVD includes all vessels at different maturational levels. A single endothelial cell and a mature vessel each have a value of 1 in measurements of MVD (11,12,29). In an angiogenic tumor, and especially in its hot spot, the MVD is composed largely of newly replicated endothelial cells and premature vessels that cannot be detected with a functional CT study.

Another possible reason for our paradoxic results (ie, that the perfusion measurements corresponded to changes in the mature vessels but not to the MVD) is the heterogeneous response of the different vascular components in the tumor vasculature to antiangiogenic therapy (31–33). Results of histopathologic studies have demonstrated that immature vessels are selectively obliterated as a consequence of VEGF withdrawal and that mature vessels, which are covered with complete pericyte and smooth muscle cell layers, are less sensitive to VEGF withdrawal and retain the ability to enlarge (34,35). In the present study, SU5416 treatment preferentially reduced the number of newly divided endothelial cells or immature vessels. Pairwise correlation analysis between vascular parameters revealed no correlation between MVD and LVN, LVP, or LVA in untreated tumors. However, strong correlations were observed between MVD and both LVN and LVP in SU5416-treated tumors because of the reduction in the proportion of immature vessels in the MVD value.

Jain (36) and Tong et al (37) have proposed that this contradictory effect (a reduction in MVD with an increase in blood perfusion in tumors) could result from a normalization mechanism of antiangiogenic therapy. Antiangiogenic therapy inhibits endothelial proliferation and vascular permeability, induces apoptosis of endothelial and tumor cells (reducing tumor cellularity), and decreases tumor interstitial fluid pressure—actions that collectively lead to an increase in blood perfusion in the residual tumor vessels. Other reasons may also lie behind this contradictory effect. In reviewing our original cine CT images, we found that some areas of high blood flow in images obtained with functional CT were in fact the sites of shunting of blood flow (unpublished data)—a finding that suggests the complicated nature of the changes in tumor perfusion during antiangiogenic therapy.

The ability of functional CT to enable quantification of the function of the tumor vasculature is of unique importance in the study of antiangiogenic therapy. Blood perfusion is a more direct and accurate index of the blood supply to the tumor than MVD and is thus a better marker for the evaluation of antiangiogenic therapy (9,36). Miles et al (22) reported that functional CT measurements of the arterial perfusion of hepatic metastatic lesions were positively correlated with patient survival, demonstrating the potential of perfusion studies in the prediction of therapeutic outcomes. Purdie et al (19) demonstrated, in an animal model, that the tumor perfusion parameters blood flow, blood volume, mean transit time, and permeability–surface area product, as measured with functional CT, constitute physiologic markers that are related to the vascular changes induced by tumor angiogenesis. In a study involving an animal liver tumor model, we found that functional CT enabled accurate quantification of changes in tumor perfusion during and after an embolization procedure and, thus, that functional CT could guide rational use of the embolization therapy and help optimize the therapeutic outcomes (38). Jain (36) and other researchers (39–41) have pointed out that close monitoring of tumor perfusion warrants rational scheduling of antiangiogenic therapy in combination with radiation therapy, hormonal therapy, and chemotherapy.

The lack of accurate measurement of vascular permeability has been a limitation of functional CT and other functional imaging studies (19). In tumor angiogenesis, vascular permeability is an important aspect of the angiogenic susceptibility of the tumor vasculature because leakage of blood proteins from the circulation facilitates remodeling of the extravascular matrix for tumor vessel formation, tumor growth, and metastasis (4,5). At functional CT, permeability–surface area product measurement is affected by several factors. First, use of the low-molecular-weight contrast agents available for clinical CT studies in functional CT studies leads to overestimation of the permeability–surface area product (19). Second, the functional status of the tumor vasculature affects the permeability–surface area product markedly; functional CT cannot yield a measurement of the permeability–surface area product value in a highly permeable vessel with no detectable blood flow. Furthermore, within a vessel with blood flow, the permeability–surface area product measured with low-molecular-weight contrast agents is flow rate dependent; a higher blood flow rate would yield a higher permeability–surface area product measurement (42,43). As a result, permeability–surface area products were most likely overestimated in the tumors of treated rats but underestimated in those of control rats in our study. Other factors, such as the scanning time, amount of contrast agent, and analytic program used would also have influenced the results of permeability–surface area product measurement.

Practical application: Our study results have validated the idea that functional CT can be used to quantify tumor perfusion in tumor antiangiogenic therapy; functional CT can therefore help in exploring the mechanisms involved in antiangiogenic therapy and enabling the rational use of antiangiogenic therapy in tumor treatment. Our results have also demonstrated that changes in tumor perfusion do not correspond to the change in MVD after antiangiogenic therapy because functional CT cannot depict the nonfunctioning vascular components in a tumor and the heterogeneity of the tumor vasculature's response to the therapy. Therefore, results of a functional CT perfusion study cannot be used as a functional surrogate for MVD, and, for the same reason, MVD, as a measure of tumor neovascularity, may not be a true measure of tumor perfusion in antiangiogenic therapy.

	ACKNOWLEDGMENTS

 
The authors thank Ella Anderson, RTR, and Delise Herron, RT, BS, for technical assistance; Xian Zhou, MS, for statistical analysis; and Ellen M. McDonald, PhD, for editorial review.

	FOOTNOTES

 

Abbreviations: LVA = luminal vascular area • LVN = luminal vascular number • LVP = luminal vascular perimeter • MVD = microvascular density • VEGF = vascular endothelial growth factor

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, Z.K., S.P.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, all authors; experimental studies, Z.K., S.P., S.K., Y.T., T.Y.L., C.C.; statistical analysis, Z.K., S.P., S.K.; and manuscript editing, Z.K., S.P., L.M.E., C.C.

	References

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