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DOI: 10.1148/radiol.2292021007
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(Radiology 2003;229:429-438.)
© RSNA, 2003

Experimental Studies

Gadopentetate Dimeglumine versus Ultrasmall Superparamagnetic Iron Oxide for Dynamic Contrast-enhanced MR Imaging of Tumor Angiogenesis in Human Colon Carcinoma in Mice1

Quido G. de Lussanet, MD, Walter H. Backes, PhD, Arjan W. Griffioen, PhD, Jos M. A. van Engelshoven, MD, PhD and Regina G. H. Beets-Tan, MD, PhD

1 From the Department of Radiology (Q.G.d.L., W.H.B., J.M.A.v.E., R.G.H.B.T.) and Angiogenesis Laboratory of Internal Medicine (A.W.G.), Maastricht University Hospital, P. Debyelaan 25, PO Box 5800, 6202 AZ Maastricht, the Netherlands. From the 2002 RSNA scientific assembly. Received August 15, 2002; revision requested September 24; final revision received March 20, 2003; accepted April 7. Supported in part by the Cardiovascular Research Institute Maastricht (CARIM), the Netherlands. Address correspondence to Q.G.d.L. (e-mail: qdlu@rdia.azm.nl).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
PURPOSE: To compare the kinetic physiologic properties of a clinical contrast agent, gadopentetate dimeglumine, with those of ultrasmall superparamagnetic iron oxide (USPIO) particles for dynamic contrast material–enhanced magnetic resonance (MR) imaging of tumor angiogenesis in human colon carcinoma in mice with a clinical MR imaging unit.

MATERIALS AND METHODS: Thirty-two mice with human colon carcinoma were injected with either gadopentetate dimeglumine (n = 16) or USPIO (n = 16) for dynamic contrast-enhanced MR imaging and pre- and postcontrast T2 and T2* measurements. Dynamic contrast-enhanced MR imaging measurements were analyzed by using a two-compartment model to calculate the endothelial transfer coefficient surface area product (KPS) for the tumor microvasculature, the reflux coefficient (k), and the fractional plasma volume (fPV). KPS, k, and fPV maps were compared with histologic microvessel density (MVD) and used to observe differences between core and rim regions of tumor.

RESULTS: Results in 30 mice (15 in the gadopentetate dimeglumine group and 15 in the USPIO group) could be used. KPS values measured with both agents correlated well with MVD in hot spots (gadopentetate dimeglumine: r = 0.6, P = .02; USPIO: r = 0.6, P = .01). No significant difference (P = .4) in correlation was found between the two agents. Both USPIO and gadopentetate dimeglumine demonstrated higher MVD and KPS values in tumor rim than in tumor core (P < .01). Tumor k values correlated poorly with whole-tumor MVD for both gadopentetate dimeglumine (r = 0.3, P = .4) and USPIO (r = 0.2, P = .6), while fPV values correlated well with whole-tumor MVD for USPIO (r = 0.6, P = .02) but not gadopentetate dimeglumine (r = -0.01, P = .98). T2 and T2* measurements showed small differences between areas of high and low angiogenic activity with both agents.

CONCLUSION: The kinetic physiologic properties of gadopentetate dimeglumine are as good as those of USPIO for dynamic contrast-enhanced MR imaging for calculating KPS as a measurement of angiogenesis in human colon carcinoma. Further studies with patients may reveal whether gadopentetate dimeglumine might be used for this purpose in clinical practice.

© RSNA, 2003

Index terms: Angiogenesis • Animals • Contrast media, comparative studies • Magnetic resonance (MR), contrast media • Magnetic resonance (MR), relaxometry • Neoplasms, blood supply • Neoplasms, experimental studies


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Tumor growth and metastasis formation are dependent on angiogenesis, the process of new blood vessel formation (1). Modulation of angiogenesis is a promising strategy for tumor treatment (1,2). To gain insight into the angiogenic potential of a tumor, new methods for diagnosis have been developed worldwide, among which noninvasive imaging techniques are promising. Dynamic contrast material–enhanced T1-weighted magnetic resonance (MR) imaging is a noninvasive technique that can aid in the diagnosis of angiogenic activity by enabling the measurement of increased microvessel permeability and blood volume in the voxels in a selected volume of angiogenic tissue (3,4). Dynamic contrast-enhanced MR imaging enables the measurement of microvessel permeability by permitting assessment of the rate at which a contrast agent transfers from the blood to the extravascular extracellular space. This rate can be expressed as the endothelial transfer coefficient surface area product (KPS). Similarly, the transfer of the contrast agent back to the blood can be expressed as the reflux coefficient (k). In addition, dynamic contrast-enhanced MR imaging enables the measurement of increased blood volume by permitting the calculation of fractional plasma volume (fPV) (5).

Reports in current literature suggest that the suitability of dynamic contrast-enhanced MR imaging is largely dependent on the choice of contrast agent (6). Clinically approved small-molecular contrast agents, such as gadopentetate dimeglumine (7), are thought to be less suited for dynamic contrast-enhanced MR imaging than large-molecular contrast agents (5,6), such as so-called ultrasmall superparamagnetic iron oxide (USPIO) particles (8). Gadopentetate dimeglumine has ideal paramagnetic properties for highly T1-weighted MR imaging (9,10). However, its short circulation times and fast diffusion rate compared with the blood pool characteristics of USPIO are drawbacks (11). To date, USPIO particles are not clinically approved for use in dynamic contrast-enhanced MR imaging (12). USPIO particles also induce a relatively strong T2* reduction, which may affect T1-weighted MR imaging (13,14). However, the T2* effect could be useful for the assessment of tissue angiogenesis when one is performing T2*-weighted measurements (15).

To our knowledge, to date there have been no published reports of studies in which the MR imaging–derived microcirculation characteristics of clinically approved small-molecular contrast agents were directly compared with those of USPIO particles in the depiction of tumor angiogenesis. The most commonly used and appropriate histologic validation technique for dynamic contrast-enhanced MR imaging results is the determination of the microvessel density (MVD) in stained tumor specimens (3,6,16).

Thus, the purpose of this study was to compare the kinetic physiologic properties of gadopentetate dimeglumine with those of USPIO particles for dynamic contrast-enhanced MR imaging of tumor angiogenesis in human colon carcinoma in mice with a clinical MR imaging unit.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Animal Model
This study was approved by the ethical review committee of our institution. Thirty-two male nude mice (Swiss nu/nu, Charles River, Maastricht, the Netherlands) (age range, 6–10 weeks) received an injection (Q.G.d.L.) of 106 LS 174T human colon carcinoma cells subcutaneously in the left flank. The mice were randomly selected for imaging with either gadopentetate dimeglumine or USPIO particles. To increase variation in tumor volume and MVD, the mice were also selected to be imaged on different days after tumor injection (ie, on day 10, day 13, day 16, or day 19).

Before imaging, the mice were anesthetized (Q.G.d.L.) with subcutaneous injection of 100 mg of ketamine (Nimatek; Eurovet, Bladel, the Netherlands) per kilogram of body weight and 10 mg/kg of xylazine HCl (Sedamun; Eurovet). The temperature in the MR imaging unit bore was kept at approximately 28°C by placing warm water bags near the mouse. For contrast agent injection into the tail vein, a 50-cm x 0.28-mm (inner diameter) tube (non-sterile tubing, Portex, Hythe, England) with a 27-gauge needle tip was prepared with three fluid volumes: an anticoagulant (0.5 µL of 0.5 mol/L heparin [Heparine; Leo Pharma, Breda, the Netherlands]); either 0.1 mmol/kg of gadopentetate dimeglumine (Magnevist; Schering, Berlin, Germany) or, in the manner advised by Turetschek et al (8,17), 2.5 mg Fe/kg of USPIO particles (NC100150 injection for biologic use only [batch no. 812010], Nycomed Imaging, Oslo, Norway); and 15 µL of saline (0.3 mol/L NaCl) to flush the bolus. The contrast agent and saline were manually injected (Q.G.d.L.) during the fifth dynamic volume acquisition in less than 40 seconds. Hematocrit values were determined from centrifuged glass heparinized capillary tubes (Micro-Hematocrit Capillary Tubes; Chase Scientific Glass, Rockwood, Tenn) of 75 mm in length and 1.1 mm in inner diameter that were filled with blood from an eye orbit puncture performed (Q.G.d.L.) immediately after MR imaging to relate blood and plasma tracer concentrations.

Contrast Agents
Gadopentetate dimeglumine is a small (0.5 kDa, <1 nm) paramagnetic gadolinium chelate with non–protein-interacting and extracellular properties. After intravenous injection, plasma concentrations of this agent decline biexponentially with time owing to (a) rapid diffusion to the extracellular space (distribution phase half-life: 10 minutes) and (b) renal clearance (elimination phase half-life: 100 minutes) (18,19). Gadopentetate dimeglumine is approved for use worldwide and is used for many clinical MR imaging applications (20).

USPIO is a superparamagnetic iron oxide crystal (Fe3O4) with an oxidized starch coating (particle size, <20 nm). USPIO particles, in general, are near clinical approval, and NC100150 injection has been used in phase II trials for liver imaging (12) and MR angiography (21). After intravenous injection, USPIO particles reduce the T1 relaxation time of blood to below 100 msec for more than 2 hours before they are taken up mainly by the liver reticuloendothelial system (half-life: 45 minutes) (22,23).

MR Imaging
MR imaging was performed with a small (5-cm-diameter) surface coil and a 1.5-T MR imaging system (Philips Intera; Philips Medical Systems, Best, the Netherlands). A T2-weighted anatomic acquisition (multisection turbo spin echo; repetition time msec/echo time msec, 3,300/200; flip angle, 90°) that covered the tail-end half of the mouse was performed for tumor delineation and size measurements and to yield an anatomic reference for the subsequent acquisitions. Before and 60 minutes after contrast agent administration, T2-weighted (multisection turbo spin echo; 300/25, 50; flip angle, 90°) and T2*-weighted (three-dimensional fast field echo; 70/25, 50; flip angle, 15°) dual-echo acquisitions were performed, with 16 transverse sections, matrix dimensions of 128 x 128, and a voxel size of 0.5 x 0.5 x 2.0 mm3. Six T1-weighted acquisitions (three-dimensional fast field echo, 50/7) with different flip angles (2°, 5°, 10°, 15°, 25°, and 35°) were performed so that we could determine the local T1 relaxation times before contrast enhancement (24). The T1-weighted dynamic contrast-enhanced series (76 volume acquisitions; temporal resolution, 39 seconds) were performed with the same parameters as the T1 relaxation measurements (flip angle, 35°). The spatial resolution of the acquired sections was 0.5 x 0.5 x 4 mm3. Adjacent sampled sections were 2-mm displaced and subsequently interpolated to 2.0-mm-thick sections during reconstruction.

Analysis of MR Imaging Data
The T2-weighted anatomic acquisition was used to locate and delineate the tumor. The volume of each tumor was measured (Q.G.d.L.). This was achieved by determining the areas (in square millimeters) of the tumor in consecutive MR sections that contained tumor tissue. The sum of these areas was multiplied by the section thickness (2 mm). The kinetic analysis of the images is described briefly in the Appendix. The data processing was performed in the MATLAB programming environment (The MathWorks, Natick, Mass). Local time–averaged T1 relaxation rates (ie, R10) of the precontrast images and the postcontrast image signal intensity time courses (with signal intensity measured in arbitrary units [AU]) were used to determine the plasma concentration in the aorta, representing the arterial input function R1(t) and the tissue concentration in the tumor. Individual R1(t) time courses were used to correct for variations in manual injection of the contrast agent, intrinsic circulation properties, and contrast agent elimination.

KPS, k, and fPV values were determined by applying a two-compartment bidirectional model (5,25). The KPS and k values were expressed as milliliters per minute per 100 cm3 of tissue, and the fPV values were expressed as milliliters per cubic centimeter of tissue. The plasma relaxation time course for the arterial input function of gadopentetate dimeglumine was fitted to a biexponential function to account for the fast extracellular distribution and slow renal clearance of this agent. Conversely, the USPIO plasma relaxation time course could be modeled with a monoexponential decay function. The relationship between the T1 shortening of the signal enhancement and the contrast agent concentration was determined from the analyses of in vitro concentration measurements for both agents. For USPIO, which is known to exhibit relatively large T2* shortening, the T2* effects were included in this relationship as described by Tofts et al (26), again by using the analyses of the in vitro measurements. We assumed a linear relationship between changes in relaxation rate and concentration for both agents.

The kinetic analyses were performed for each voxel in the central section through the tumor. These analyses yielded color-coded maps of the KPS values, which were overlaid on the corresponding precontrast T1-weighted images. These KPS maps were then used to define regions of interest for the whole tumor, the tumor rim, and the tumor core so that we could spatially average KPS, k, and fPV values for these three tumor regions. Pre- and postcontrast T2 and T2* values were determined by drawing regions of interest (Q.G.d.L. and W.H.B.) for the whole tumor, tumor rim, and tumor core on the dual-echo images of corresponding sections, as performed for the kinetic analysis. The areas of whole-tumor regions of interest (range, 6–140 mm2) varied depending on the size of the individual tumor. The dual-echo signal intensity values in these regions were used to calculate the T2 and T2* values by dividing 25 msec (ie, the first echo time minus the second echo time) by the natural logarithm of the signal intensity ratio. Finally, the pre- and postcontrast T2 and T2* values were inverted to calculate the changes in T2 and T2* relaxation rates (ie, R2 and R2*, respectively) after contrast agent injection.

Histologic Examination
After MR imaging, the mice, while still anesthetized, were sacrificed by means of cervical dislocation, and the tumors were excised. Transverse cryosections (4 µm) were obtained through the middle part of the tumors, corresponding to the transverse orientation of the selected MR imaging sections. Skin tissue marked the lateral side of the tumors on the cryosections. The cryosections were immunohistochemically stained by using CD31 antibody (Goat-anti-Rat-PO-DAB; Pharmingen, Uithoorn, the Netherlands).

MVD values were calculated (Q.G.d.L.) from the total number of vessels in a microscopic field of 0.2 mm2 (magnification, x250) for the whole cryosection through the tumor, creating a MVD map for each cryosection. The method of counting vessels in whole cryosections through the tumor, instead of in only random fields or vascular hot spot selections, was chosen to reduce operator dependency. MVD values were obtained independently from the MR imaging analyses. MVD values were calculated for the whole tumor, the tumor rim, the tumor core, and microvascular hot spots (ie, the five highest MVD values per tumor cryosection). Testing for a correlation between KPS and histologic tumor grade, a correlation found, for instance, in rat breast tumors (5), is not applicable in this xenograft model. This is because all tumors in this model are intermediately to well differentiated and hardly ever metastasize, implying limited variation in tumor grade.

In Vitro Experiment
To estimate the T2* effect of USPIO (8,17), we used commercially obtained mouse plasma (Harlan SeraLab, Loughborough, England) with citrate buffer. We prepared 21 samples of 2.5 mL: 10 samples with increments in concentration of gadopentetate dimeglumine (range, 0.01–0.50 mmol/mL of plasma), 10 samples with increments in concentration of USPIO (range, 0.001–5.000 mg Fe per milliliter of plasma), and one control plasma sample. The same MR imaging protocol (T1 measurement and T2 and T2* dual-echo measurements) and equipment were used as in the mouse experiment. The slopes of the T1, T2, and T2* relaxations versus the contrast agent concentrations in plasma (r1plasma, r2plasma, and r2*plasma) were calculated, and the ratios r2plasma/r1plasma and r2*plasma/r1plasma were determined for both gadopentetate dimeglumine and USPIO.

Statistical Analysis
The number of animals included were estimated by performing a power analysis (27) (95% CI and 80% power) and considering a predetermined possible dropout rate of less than 10%, which amounted to 16 animals per contrast agent group, for a total of 32 animals. MVD values were compared with tumor volumes, KPS, k, and fPV values, and change in R2 and R2* values, and tumor ages (in terms of days after inoculation) were compared with tumor volumes by determining the Spearman rank correlation coefficient. The Spearman rank correlation allows statistical inference from a non-normal distribution of variables. The equality of the KPS correlation coefficients of the gadopentetate dimeglumine and USPIO groups was tested by using Fischer z transformation (27).

MVD values and tumor volumes were compared between the gadopentetate dimeglumine and USPIO groups by using the two-tailed Student t test. The two-tailed Student t test was also used for comparing reductions in tumor R2 and R2* values after contrast agent injection within the gadopentetate dimeglumine and USPIO groups; for comparing differences in MVD, KPS, k, and fPV values and change in R2 and R2* between tumor rim and tumor core; and for comparing T2* signal variations in tumor rim with T2* signal deviations in tumor core and muscle. Statistical analyses were performed by using commercial software (SPSS 10.0.5; SPSS, Chicago, Ill), and P < .05 was considered to indicate a statistically significant difference.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
DISCUSSION
 APPENDIX
 REFERENCES
 
Thirty mice were successfully imaged (one died and one could not be successfully imaged owing to severe motion artifacts) and included in the evaluation of MVD measurements.

Tumor Characteristics
Tumor volumes ranged between 25 and 1,500 mm3 (average volume, 443 mm3 ± 539 [SD] with USPIO and 495 mm3 ± 445 with gadopentetate dimeglumine) and showed a negative correlation with MVD (r = -0.57, P < .05) and a positive correlation with tumor age (r = 0.40, P < .05) (Table 1). The MVD ranged between 3.1 and 20.5 vessels per 0.2 mm2 (average MVD, 7.1 ± 5.3 with USPIO and 5.0 ± 2.2 with gadopentetate dimeglumine). Standard error of the mean for individual MVD values ranged between 5% and 15% and was slightly higher for rim MVD values than for core MVD values. Neither the mean tumor volume (P = .9) nor the mean MVD value (P = .2) was significantly different between the USPIO and gadopentetate dimeglumine groups. The mean MVD value in the tumor rim was significantly (P < .01) higher than that in the tumor core (Table 1). The mean measured hematocrit value was 0.48 ± 0.03.


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  		TABLE 1. Tumor Characteristics and Dynamic Contrast-enhanced MR Imaging-Derived Parameters in Terms of Ascending Rim MVD Values

 
Dynamic T1-weighted Measurements
Quantitatively color-coded KPS maps and their corresponding T2-weighted images are shown in Figure 1. These maps highlight the areas of high angiogenic activity and show the possible variation between tumors. In 22 of the 30 tumors, more than 90% of the pixels containing tumor tissue were successfully analyzed; in four tumors (one imaged with gadopentetate dimeglumine, three imaged with USPIO) about 25% of the pixels were rejected; and in four tumors (two imaged with gadopentetate dimeglumine, two imaged with USPIO), about 50% of the pixels were rejected owing to nonenhancement or because a preset maximum fit error, as described in the Appendix, was exceeded.



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  		Figure 1. Color-coded tumor KPS maps and corresponding transverse T2-weighted anatomic images in, A, a mouse with a tumor with high KPS at the rim (arrows) with respect to the tumor core and, B, a mouse with a heterogeneous tumor.

 
The KPS values for the USPIO group (mean, 8.1 ± 4.3 [SD]) were significantly higher (P = .01) than for the gadopentetate dimeglumine group (mean, 4.6 ± 1.6). The two agents had similar correlation coefficients (Table 2) but yielded different scatterplots (Fig 2), which limits the prediction of MVD from KPS, especially at high MVD values with gadopentetate dimeglumine. Nevertheless, the KPS values measured with either agent correlated significantly with MVD. The differences between the correlation coefficients for the gadopentetate dimeglumine and USPIO groups were not significant (P > .4). The KPS generally correlated less significantly with MVD in hot spots than with MVD in the whole tumor and in the tumor rim (Table 2).


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  		TABLE 2. Statistical Correlation between MVD and Dynamic Contrast-enhanced MR Imaging Parameters

 


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  		Figure 2. Scatterplot of KPS in tumor rim versus MVD in tumor rim shows a smaller range of MVD with gadopentetate dimeglumine ({diamondsuit}) than with USPIO ({square}); regression lines illustrate that correlation between KPS and MVD values for gadopentetate dimeglumine (solid line) and USPIO (dotted line) does not significantly differ (P > .4).

 
Contrary to Turetschek et al (8), we could derive the k values from the data by using the bidirectional model and did not set the k at zero. However, the k values measured with USPIO were very small (mean, 0.9 ± 0.8)—15 times smaller (P = .003) than the values measured with gadopentetate dimeglumine (mean, 14.0 ± 15.0). The k values measured with USPIO showed no correlation with MVD. Measurements with gadopentetate dimeglumine showed a positive correlation between k and MVD that was significant for the tumor core only (Table 2).

The fPV values differed between the two agents. The tumor fPV values measured with USPIO (mean, 0.20 ± 0.30) were three to four times higher (P = .04) than those measured with gadopentetate dimeglumine (mean, 0.07 ± 0.09). The fPV significantly correlated with MVD when measured with USPIO (except in the tumor rim) but not when measured with gadopentetate dimeglumine (Table 2).

Additional dynamic contrast-enhanced MR imaging results revealed that, like the MVD values, all KPS, fPV, and k values—with the exception of k values measured with gadopentetate dimeglumine—were significantly higher in the tumor rim than in the tumor core (Table 1).

Signal Intensity Time Courses
Differences in the magnetic properties of the agents were illustrated by their plasma and tissue concentration time courses (Fig 3). The USPIO plasma signal intensity time course showed an immediate signal intensity decrease followed by a slow recovery (Fig 3a). Conversely, the gadopentetate dimeglumine signal intensity time course showed an immediate signal intensity increase followed by a decay (Fig 3b). The signal intensity of tumor tissue showed very small increases with USPIO (Fig 3a) as compared with gadopentetate dimeglumine, which caused a relatively large increase in signal intensity that was followed by a slow reduction starting approximately 15 minutes after injection (Fig 3b). Small areas in the rims of most tumors showed decreases in signal intensity over time that resembled the signal intensity time course of plasma, indicating that these areas had a strong vascular content. With USPIO, these appeared as small areas of strong signal intensity reduction in the tumor rim on the dynamic images obtained immediately after contrast agent injection.



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  		Figure 3a. (a) Graph of USPIO plasma signal intensity time course. Signal intensity in the aorta ({triangleup}) shows an immediate decrease after contrast agent injection (t0) followed by a slow recovery (dotted line), while the average signal intensity in tumor ({circ}) shows a small but gradual increase (solid line) after contrast agent injection. (b) Graph of gadopentetate dimeglumine plasma signal intensity time course. Signal intensity in the aorta ({triangleup}) shows an immediate increase after contrast agent injection (t0) followed by a biexponential decline (dotted line), while the average signal intensity in tumor ({circ}) shows a substantial increase but diminishes again (solid line) approximately 15 minutes after contrast agent injection.

 


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  		Figure 3b. (a) Graph of USPIO plasma signal intensity time course. Signal intensity in the aorta ({triangleup}) shows an immediate decrease after contrast agent injection (t0) followed by a slow recovery (dotted line), while the average signal intensity in tumor ({circ}) shows a small but gradual increase (solid line) after contrast agent injection. (b) Graph of gadopentetate dimeglumine plasma signal intensity time course. Signal intensity in the aorta ({triangleup}) shows an immediate increase after contrast agent injection (t0) followed by a biexponential decline (dotted line), while the average signal intensity in tumor ({circ}) shows a substantial increase but diminishes again (solid line) approximately 15 minutes after contrast agent injection.

 
T2 and T2* Relaxation Rate Measurements
The T2* relaxation rates in tumor were approximately 25% higher with USPIO than with gadopentetate dimeglumine (Fig 4). USPIO caused a significant 10% R2* reduction (from 39.0 to 43.1 sec-1; P = .02) in tumor, while gadopentetate dimeglumine did not (from 35.2 to 34.1 sec-1; P = .4). Reductions in R2 were not significant with either USPIO (from 18.5 to 17.2 sec-1; P = .1) or gadopentetate dimeglumine (from 16.7 to 16.5 sec-1; P = .5). No significant correlation was found between changes in R2 and R2* and MVD with either USPIO (P > .3) or gadopentetate dimeglumine (P > .5). However, the relative SDs of the T2 and especially the T2* image signal intensities were more than 45% at the tumor rim (mean signal intensity, 654 AU ± 295 [SD]). This deviation was significantly higher than that in the tumor core (mean signal intensity, 670 AU ± 167) and that in muscle tissue (mean signal intensity, 740 AU ± 187) with both agents (P < .01).



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  		Figure 4. Graph of R2* changes in tumor after contrast agent injection shows that R2* changes are much greater with USPIO ({square}) than with gadopentetate dimeglumine ({diamondsuit}); error bars represent standard errors of the mean.

 
In Vitro Experiment
The in vitro measurements in our study supported the idea that the assumed linear relations between T1, T2, and T2* relaxation rates and concentration were sufficient. The plasma relaxivity values (r1plasma, r2plasma, and r2*plasma) for the concentration ranges tested for gadopentetate dimeglumine and USPIO are given in Table 3. The r2plasma/r1plasma (and the r2*plasma/r1plasma) ratio is about three times higher for USPIO than for gadopentetate dimeglumine; this illustrates that USPIO has a smaller signal intensity–increasing effect in the acquired T1-weighted images (Table 3) for the echo time used.


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  		TABLE 3. Relaxation and Plasma Relaxivity in Vitro

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
APPENDIX
 REFERENCES
 
The main finding of this study was that permeability values (ie, KPS values), as measured with dynamic contrast-enhanced MR imaging, showed equal correlation with histologic findings (ie, MVD) when either gadopentetate dimeglumine or USPIO was used (Table 2). To our knowledge, this is the first study that revealed a significant correlation between KPS and MVD with gadopentetate dimeglumine when a clinical MR imaging unit and a human colon cancer cell line inoculated in animals were used.

Assessment of Tumor Microvessel Permeability
The results of this study do not support a general statement that large-molecular contrast agents such as USPIO are favorable to small-molecular agents such as gadopentetate dimeglumine for assessing tumor microvessel permeability (ie, KPS) with dynamic contrast-enhanced MR imaging. Favorable properties of USPIO, as compared with the properties of gadopentetate dimeglumine, were considered to be its large molecular size and prolonged circulation times. Results of studies of rat breast tumors that involved the use of albumin-bound gadopentetate dimeglumine (5) suggest that large-molecular agents are better than small-molecular agents for differentiating between benign and malignant tumors because microvessels in benign tumors are almost nonpermeable to large-molecular contrast agents but not to small-molecular contrast agents.

In malignant tumors such as colorectal cancer, however, permeability is expected to be independent of the molecular size of contrast agents because vessel pores in human colon carcinoma are typically much larger (400–600 nm) (28) than the diameter of either contrast agent used in our study (gadopentetate dimeglumine, <1 nm; USPIO, <20 nm). Our results—specifically, the equivalent angles of inclination of the regression line through the KPS-versus-MVD scatterplot for USPIO and gadopentetate dimeglumine (Fig 2)—support this expectation. This plot suggests that an increase in MVD yields an increase in KPS that is equal for both contrast agents.

Another advantage of the use of large-molecular agents such as USPIO would be the ability to image at lower temporal resolutions because of the prolonged circulation times of large-molecular compared with small-molecular agents (29). However, results of studies have shown that, to minimize sampling errors of rapid changes in contrast agent concentration, high temporal resolutions are preferable, particularly for imaging the supplying blood vessels (30,31). Moreover, current MR imaging units already allow the use of fast-acquisition dynamic contrast-enhanced imaging techniques that enable first-pass permeability measurements (32).

Our KPS measurements, obtained with a clinical MR imaging unit, suggest that gadopentetate dimeglumine may be as good as USPIO for dynamic contrast-enhanced MR imaging of tumor angiogenesis. In our opinion, gadopentetate dimeglumine may even be preferable to USPIO for clinical dynamic contrast-enhanced MR imaging measurements because of its safe use and worldwide approval (20), as well as its rapid clearance (19), which allows for short-term follow-up.

T2* Effects
A disadvantage of the use of superparamagnetic contrast agents (eg, USPIO) is that T2* relaxation may negatively affect T1-weighted MR imaging (17,23). These effects are most prominent at higher concentrations. Our in vitro measurements (Table 3) and in vivo T2*-weighted measurements (Fig 4) reflect the strong T2* relaxation effects of USPIO compared with the effects of gadopentetate dimeglumine for the concentrations used in this study. Significant T2* effects due to angiogenic heterogeneity of the tissue structures (15,33) (ie, correlation with MVD) were not observed in this study with either USPIO (P > .3) or gadopentetate dimeglumine (P > .5).

One explanation for this may be that postcontrast R2 and R2* were measured 50–60 minutes after contrast agent injection, when most of the contrast agent had cleared. Furthermore, the deviations of the signal intensity values in the selected regions of interest were high, particularly in the tumor rim, and this may have obscured changes in R2 and R2*. Our results show significantly higher relative SDs of the individual T2* signals in the tumor rim than in the tumor core and muscle tissue for both gadopentetate dimeglumine and USPIO. This suggests that measurements of the T2* signal changes shortly after contrast agent injection may, in fact, be sensitive for different levels of angiogenic activity with both gadopentetate dimeglumine and USPIO.

Our results support the notion that possible negative T2* effects of the contrast agent on the T1-weighted dynamic contrast-enhanced MR imaging measurements should be taken into account, particularly with USPIO.

Values for k and fPV
The k and fPV measurements reflect differences between USPIO and gadopentetate dimeglumine. The k values illustrate differences in circulation and diffusion properties. The fPV values illustrate differences in magnetic properties, as well as differences in mobility through the interstitial matrix of the extravascular extracellular space.

The rapid diffusion of gadopentetate dimeglumine causes a rapid decrease in intravascular plasma concentration (18,19), which allows for a higher reflux throughout the tumor. Conversely, the slow clearance of USPIO (22,23) resulted in a sustained high intravascular concentration and low k values (Table 1). It is, however, unclear whether a correlation should exist between k and MVD values.

The superparamagnetic properties of USPIO may have caused an overestimation of the fPV (Table 1). Intravascular USPIO particles produce a bulk magnetization that excites water protons beyond the vascular compartment, while gadopentetate dimeglumine produces a more local magnetization effect (23,34). This may explain why USPIO fPV values were several times higher than gadopentetate dimeglumine fPV values (Table 1).

The high mobility and fast spread through the interstitial matrix of small-molecular agents (35) may have negatively influenced the fPV measurements for gadopentetate dimeglumine, and this may explain their poor correlation with MVD (Table 2). In the tumor rim, USPIO fPV values also correlated poorly with MVD (Table 2). This may be explained by high angiogenic activity in the tumor rim with degradation of the intercellular matrix (2), which allows free movement of both USPIO and gadopentetate dimeglumine. We are aware that the use of fPV measurements has its limitations in this model. The fPV parameter is modeled as if the tissue has the same concentration-over-time course shape as the arterial input function. This may deviate from the true blood volume (11). Nevertheless, in our study, the fPV values measured with both agents revealed a higher plasma volume in the tumor rim than in the tumor core. The latter finding is in agreement with the MVD trend (Table 1).

Results and conclusions from our data are limited in several ways. Results from this human colon carcinoma model are preliminary, and their applicability remains to be proved in humans. Furthermore, MVD may not be the ideal surrogate marker of angiogenesis. Therefore, correlations between KPS and MVD observed for either agent cannot be perfect because co-registration between MR imaging sections and pathology sections is limited. Basically, MVD represents a morphologic marker, whereas dynamic contrast-enhanced MR imaging provides functional markers. Also, MVD assessments are inherently prone to sampling errors and are reported to be observer dependent, so they are at most semiquantitative. For instance, at low MVD values, the counting of stained vessels within a microscopic field is relatively easy and reliable. At higher MVD values, however, it is much more difficult to discern and count separate vessels because they may cluster. This inaccuracy might explain the larger scattering of data at high MVD values depicted in Figure 2. Furthermore, dysfunctional microvessels might affect MVD measurements because of difficulties in distinguishing between functional vessels and dysfunctional nonperfused vessels or endothelial cell clusters on histologic sections. Conversely, dynamic contrast-enhanced MR imaging parameters are affected only by functional vessels.

In spite of its shortcomings, MVD is generally considered the best standard for validating functional imaging techniques such as dynamic contrast-enhanced MR imaging, as long as these techniques are still being developed and need further improvement. Another limitation in our study may have been the two-compartment model we used, which has limited value in accounting for the complex and heterogeneous situation in a tumor environment; extended models with multiple compartments (36) may prove to be more accurate.

In addition, the relationship between the MR signal intensity and the concentration of contrast agent in vivo may deviate from the relationship determined in vitro due to compartmentalization effects, a limitation to the correct quantification of MR imaging microcirculation parameters that might be overcome by further improving the dynamic contrast-enhanced MR imaging technique. One way to do so is by speeding up the acquisition to obtain more sample points for the steep and error-prone arterial input function needed in the two-compartment model. High dynamic acquisition speeds are beneficial, particularly for small-molecular contrast agents, to avoid enhancement of the extracellular compartment during the first pass of the bolus due to leakage from the circulation. But even for the USPIO used in the present study, some degree of leakage might be expected. Any degree of leakage of the USPIO used would mean that the USPIO may not be the ideal large-molecular reference contrast agent because it is not perfectly intravascular. At the dose used in this study, the USPIO contrast agent yielded strong T2* effects, particularly during the bolus passage, that affected T1-weighted measurements. Consequently, the development of new contrast agents and dose optimization are other potential methods to further improve dynamic contrast-enhanced MR imaging of angiogenesis.

Practical application: Our findings suggest that the kinetic physiologic properties of gadopentetate dimeglumine are as good as those of USPIO for calculating a microvessel permeability parameter (ie, KPS). Extended characterization of the tumor microenvironment, such as determination of the local blood volume fraction, might benefit more from new large-molecular contrast agents or optimized use of existing agents. Results of further work in which human subject protocols are used may show whether gadopentetate dimeglumine is indeed successful for diagnosing angiogenesis and monitoring the effects of antiangiogenic treatment in patients with colon cancer.


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
REFERENCES
 
The tracer kinetic analysis was performed by using a two-compartment model with a method very similar to that described by Daldrup et al (5). Within a tumor, these two compartments represent the blood plasma and interstitial (extravascular extracellular) space and are assumed to be in equilibrium.

The dynamic exchange of the contrast agent between the two compartments amounts to the dynamic variation of the concentration of the contrast agent in the interstitial water of the tumor tissue CI(t) (in millimoles per cubic centimeter of tissue), where t is time, and is given by the differential equation

where CP(t) is the concentration of the contrast agent in the plasma space of the tumor tissue (in millimoles per milliliters), KPS is the endothelial transfer coefficient surface area product (in milliliters per minute per 100 cm3 of tissue), k is the reflux coefficient for the transfer of contrast agent back to the blood (in min-1), and d/dt means differential.

The concentration of the contrast agent in the tumor tissue at any time t, CT(t) (in millimoles per cubic centimeter of tissue) is composed of the contribution of both the interstitial and the plasma content, and is expressed as

where fPV is the fractional plasma volume of the tumor tissue (in milliliters per cubic centimeter of tissue) and the integral runs from t0 to t. The parameter t0 represents the mean delay of the transit of the tracer from the supplying blood vessel to the tumor tissue.

The concentration of the contrast agent in the plasma at any time was related to the signal intensity (SI) of the gradient echo images with the Ernst formula:

where M0 is the equilibrium magnetization (which is dependent on the proton density); {alpha} is the flip angle; E1 = exp(-TR/T1), where TR is the repetition time and T1 is the longitudinal relaxation time; TE is the echo time; and T2* is the transverse relaxation time. The shortening of the T1 relaxation time owing to the contrast agent is described by the following equation:

where R1 is the T1 relaxation rate, T10 is the T1 value before contrast agent injection (when the concentration C = 0), and r1 is the relaxivity of the contrast agent at 37°C and 1.5 T. The values of M0* = M0 exp(-TE/T2*) and T10 were determined by fitting the Ernst formula to the signal intensities from the images acquired before contrast agent injection with different flip angles ({alpha} = 2°, 5°, 10°, 15°, 25°, and 35°). Changes in the plasma T1 relaxation rate R1P were related to the measured changes in the blood T1 relaxation rate R1B by using the following formula:

where Hct is the hematocrit value. For gadopentetate dimeglumine, increases in signal intensity were noted only in the tumor tissue and in the aorta. Accordingly, plasma concentration was calculated for gadopentetate dimeglumine as if T2* effects owing to the concentration variation were negligible. For USPIO, signal intensity decreases were observed in the aorta, reflecting strong T2* effects at high tracer concentration. To relate the signal intensity to the concentration of USPIO, we used the following equation:

where R2* is the T2* relaxation, 1/T2*0 is the T2* value before contrast agent injection (thus C = 0), and r2* is the relaxivity of the contrast agent at 37°C and 1.5 T. We stress that this linear relationship is an approximation applied for the vascular input function only. Owing to the compartmentalization effect, the resulting R2* is expected to be unpredictable but larger in the tumor tissue. Voxels exhibiting no signal intensity increase in the tumor tissue were omitted from the kinetic analysis.

The time course of the intravascular contrast agent concentration was used to determine an effective vascular input function. The vascular input function was calculated by fitting a decay curve to the plasma contrast agent concentration time course data CP(t) from the aorta. This decay curve consisted of (for gadopentetate dimeglumine) two exponentials (ie, four parameters) and (for USPIO) one exponential (ie, two parameters). Subsequently, the exponential model for the vascular input function was substituted into Equation (A2), yielding a multiple exponential expression. From the resulting expression, the parameters KPS, k, fPV, and t0 were determined by fitting to the CT time course by using the numerical Levenberg-Marquardt optimization algorithm. Pixels with a fit error greater than 50% were rejected and omitted from the kinetic analyses.


   ACKNOWLEDGMENTS
 
We thank Nycomed-Amersham in Norway for supplying the USPIO contrast agent. We also thank Daisy van der Schaft, PhD, and Sandra van der Niet, BS, from the angiogenesis laboratory at our institution for their expertise and assistance with preparing the mice and performing the histologic analyses. Finally, our gratitude is extended to Alexandra Buehler, PhD, for helping us with her veterinary expertise.


   FOOTNOTES
 
Abbreviations: AU = arbitrary units, MVD = microvessel density, USPIO = ultrasmall superparamagnetic iron oxide

Author contributions: Guarantors of integrity of entire study, J.M.A.v.E., R.G.H.B.T.; study concepts, Q.G.d.L., W.H.B., J.M.A.v.E., R.G.H.B.T.; study design and literature research, all authors; experimental studies, Q.G.d.L., W.H.B., A.W.G.; data acquisition, Q.G.d.L., W.H.B.; data analysis/interpretation, Q.G.d.L., W.H.B., A.W.G.; statistical analysis, all authors; manuscript preparation, Q.G.d.L., W.H.B., A.W.G., R.G.H.B.T.; manuscript definition of intellectual content, Q.G.d.L., W.H.B., J.M.A.v.E., R.G.H.B.T.; manuscript editing, revision/review, and final version approval, all authors


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 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
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