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Dynamic CT Evaluation of Tumor Vascularity in Renal Cell Carcinoma

¹ Department of Radiology, Tong Ji Hospital, Tong Ji University, Xin Cun Rd. 389, Shanghai 200065, China.
² Department of Radiology, West China Hospital, Sichuan University, Shanghai, China.

Received September 15, 2004; accepted after revision May 19, 2005.

 
Address correspondence to J. H. Wang (jinhongw2002{at}yahoo.com).

Abstract

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OBJECTIVE. The purpose of our study was to evaluate the correlation between the enhancement parameters of dynamic CT; the carcinoma tissue microvessel density (MVD, a hotspot method to provide a histologic assessment of tumor vascularity); and tumor nuclear grade in renal cell carcinomas.

SUBJECTS AND METHODS. Twenty-four patients with histologically diagnosed renal cell carcinoma underwent dynamic enhanced CT. Enhancement parameters, slope of the time-density curve, the density difference before and after tissue enhancement (H), tissue blood ratio (TBR), and area under the time-density curve (AR), were calculated for all lesions. Pathology slides corresponding to the CT plane were stained using mouse antihuman CD34 monoclonal antibody and H and E. Fuhrman nuclear grade was used. Vascular hot spots of microvessels were recorded. Spearman's rank correlation was performed to determine the strength of the relationship between enhancement parameters, MVD determinations, and tumor nuclear grade.

RESULTS. MVD with CD34 staining revealed uneven distribution of positively stained vascular endothelial cells in renal cell carcinoma lesions. Heterogeneous distribution of contrast enhancement was seen among and within individual tumors. The tumors appeared as uneven patterns on time-density curves of renal cell carcinoma lesions. Enhancement parameters of H (median, 21.0 H; range, 2.2-105.8 H), TBR (median, 39%; range, 10.7-154.7%), AR (median, 1.58 H x sec; range, 0.23-3.67 H x sec), and slope (median, 2.76; range, 0.53-6.76) varied greatly. Renal cell carcinoma tissue MVD significantly correlated with all enhancement parameters of dynamic CT. The correlation coefficients (r) were 0.62, 0.54, 0.55, and 0.44, respectively, for H, slope, TBR, and AR (p < 0.0 5). All enhancement parameters did not significantly correlate with tumor nuclear grade. They were not predictive of nuclear grade.

CONCLUSION. Enhancement parameters of dynamic CT may be suited to evaluate tumor vascularity in vivo. Dynamic enhanced CT images may reflect the heterogeneity of tumor angiogenesis on the basis of the correlation between enhancement parameters and MVD of renal cell carcinoma.

Keywords: dynamic CT • genitourinary imaging • kidney • renal cell carcinoma • research • tumor vascularity

Introduction

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Renal cell carcinoma is the most common form of kidney cancer; it is also among the most vascularized of solid tumors. Tumor angiogenesis is known to play an important role in the progression of renal cell carcinoma [1, 2]. Tumor angiogenesis is a process in which blood capillaries sprout from tumor or tumor-surrounding tissue. In recent years, attention has focused on measuring vascularity to provide a histologic assessment of tumor angiogenesis, although vascularity measurement may not be an ideal indicator of tumor angiogenesis. The method of choice is counting tumor microvessel density (MVD) in areas of neoangiogenesis or vascular "hot spots" [3]. MVD is a potential prognostic factor that has been correlated with clinical stage, pathologic stage, metastasis, and histopathologic grade, and is a significant predictor of disease-specific survival and progression after therapy [4]. However, the use of the histologic MVD technique to quantify tumor vascularity and hence to evaluate tumor angiogenesis, although a widely used method, may not be as ideal as a marker in vivo.

The efficacy of antiangiogenesis is known to be correlated with the degree of vascularity. The more hypervascularity a tumor has, the more effective antiangiogenesis is. Several antiangiogenic therapies for renal cell carcinoma have shown promise in preclinical studies and are currently being evaluated in clinical trials [5-8]. Antiangiogenesis has become the new strategy for treating tumors because of its evident effectiveness in constraining tumor growth [9-11]. It is clinically essential for both treatment planning and prognosis to evaluate tumor vascularity and angiogenesis in vivo.

View larger version (148K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1A —Homogeneous enhancement of all renal cell carcinoma lesions in right kidney in 52-year-old woman. Dynamic CT scan shows one region of interest is selected on parenchyma of tumor and aorta.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1B —Homogeneous enhancement of all renal cell carcinoma lesions in right kidney in 52-year-old woman. Graph shows time-density curves of tumor (solid line) and aorta (dotted line).

View larger version (140K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2A —Heterogeneous enhancement of renal cell carcinoma lesions in left kidney in 49-year-old man. Dynamic CT scan shows two regions of interest are selected on parenchyma of tumor and aorta.

View larger version (18K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2B —Heterogeneous enhancement of renal cell carcinoma lesions in left kidney in 49-year-old man. Graph shows time-density curves of tumors (purple and yellow lines) and aorta (blue line).

Subjects and Methods

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Patients
From October 2000 to March 2004, twenty-four patients with histologically diagnosed renal cell carcinoma underwent dynamic enhanced CT (16 males and 8 females; ages, 13-79 years; body weight range, 36-65 kg). Tumor specimens were produced from each patient. None of the patients had seriously impaired function of the heart, liver, or kidneys. The study was designed in a prospective manner and was approved by our institutional review board. Informed consent was obtained from all patients.

Equipment and Contrast Agents
Dynamic helical CT (Somaton Plus 4, Siemens Medical Solutions) was performed by injecting 1.5 mL/kg of body weight of Ultravist 300 ([iopromide] Schering) IV contrast agent via the antecubital route at a rate of 3 mL/sec. The total volume of contrast material ranged from 54 to 97.5 mL. The total duration of injection was 18-32.5 sec. A CT power injector (Envision, Medrad) was used in all cases.

Procedure Design and Scanning Techniques
Scanning was performed as follows: Patients were trained as to the proper breathing technique before the start of CT. Patients held their breath during the first 30 sec of scanning and thereafter breathed in and out lightly and naturally. An abdominal belt was applied to reduce respiratory artifacts. Unenhanced scanning was performed to localize the renal lesion, allowing us to obtain baseline tumor attenuation measurements. Renal single-level dynamic CT was performed. The delay time was 14-17 sec after the beginning of the contrast agent injection, and a total of 17-24 slices were obtained. The same slice at a single level was obtained every 4.9 sec. After completion of the dynamic scanning, whole-kidney helical CT was performed with 7-mm slice thickness, no interslice gap, 7-mm collimation, and a pitch of 1. Finally, the target slice was scanned.

Image Postprocessing
Circular regions of interest (ROIs) were created over the aorta and the tumor parenchyma with time-attenuation curves derived for each. A corresponding time-density curve was automatically produced.

We identified the selected slices and measured the lesions by using the following criteria: Slices were chosen from which tumors, renal cortices, and renal medullas could be easily distinguished, and a circular ROI was selected on the parenchyma of the tumor and abdominal aorta. Two ROIs were selected on regions with a fairly different heterogeneously enhancing lesion, whereas only one ROI was assigned for homogeneously enhancing lesions. The average values of both were calculated. For tumors with varying densities, we selected parenchyma reflecting the characteristics of the tumor and avoided areas of cystoid change and necrosis and the great vessels around and inside the tumor. The area of a circular ROI should not be larger than that of a small tumor, nor should it be too small (not > 3 mm in diameter), or CT values cannot be measured accurately. The ROIs taken were usually 4 mm in diameter (Figs. 1A, 1B, 2A, and 2B).

Analysis of Imaging Data
Density difference—The density difference before and after tissue enhancement ( H) was calculated as H = maximum CT value (peak value) with tissue enhancement minus CT value acquired via tissue unenhanced scanning.

Slope—The slope (S) of the time-density curve was calculated as S = (peak value after tissue enhancement - CT value of baseline) / time period reaching the tissue peak value from baseline. The start and the highest points were the points measured on the curve.

Tissue-blood ratio—The tissue-blood ratio (TBR) was calculated as TBR = (peak value after tumor tissue enhancement - CT value of tumor tissue at baseline) x 100% / (peak value after aorta enhancement - CT value of aorta at baseline).

Areas under the curve—Areas (ARs) under the time-density curve corresponding to different tissues were automatically calculated with the dynamic CT program.

Immunohistochemical Staining of Tissue
Because the preoperative CT images were given to the pathologist, all surgical specimens obtained by radical nephrectomy were cut in an axial plane corresponding to the sections obtained on dynamic CT with an approximate accuracy of 3-5 mm. Extra effort was taken to ensure that the site of tissue sampling corresponded with the ROI selected. After tumors were excised, analysis and observation of all pathology specimens were performed. The cryosections were stained with H and E and mouse antihuman CD34 monoclonal antibodies (Zhongshan Biologic Preparation Co.).

Analysis of Immunohistochemical Results
Vascular hot spots were identified by screening for areas with the highest vessel density in a 100x microscopic low-power field. Then individual microvessels were counted in a 200x microscopic low-power field. Counts were measured as the number of microvessels per 0.2 mm². Criteria for positive staining and microvessel counting were followed as described by Weidner [16]. The average MVD values were calculated in five hot-spot areas of tumor parenchymal cells, including the MVD values of the tumor rim, the tumor core, and three microvascular hot spots. Fuhrman nuclear grade [17] was used on H and E-stained slides.

Statistics Analysis
Data were analyzed using SPSS software, version 10.0.5. Spearman's rank correlation was calculated to test the strength of the association between CT enhancement parameters, tumor MVD, and tumor nuclear grade. Spearman's rank correlation allows statistical inference from an abnormal distribution of variables. Only two-tailed tests were used. A p value of less than 0.05 was considered statistically significant.

Results

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Dynamic Enhanced CT
Dynamic CT enhancement parameters such as H, TBR, AR, and slope were calculated and recorded. The results are shown in Table 1. When comparing H (median, 21.0 H; range, 2.2-105.8 H), TBR (median, 39%; range, 10.7-154.7%), AR (median, 1.58 H x sec; range, 0.23-3.67 H x sec), and slope (median, 2.76; range, 0.53-6.76) values of individual tumors, a large degree of variation was seen.

View this table: [in this window] [in a new window]   TABLE 1: Results for Density Difference Before and After Enhancement (H), Tissue–Blood Ratio (TBR), Area Under the Time–Density Curve (AR), Slope, and Microvessel Density (MVD) on Dynamic CT of Renal Cell Carcinoma Lesions

Distinct patterns of time-density curves of renal cell carcinoma lesions were observed. One pattern was that the time-density curve showed an initial rise that was fast and steep and then abruptly changed to a relatively flat curve. This pattern occurred in 15 patients. An extremely steep change of the initial uptake curve were seen in five patients. Another pattern noted in nine patients was the slow and flat rise of the initial curve slope with low amplitude. The graphs of time-density curves are shown in Figure 3.

View larger version (37K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3 —Graph shows great variation in distribution of time-density curves in renal cell carcinoma. Some cases show steep and rapidly increasing curves, whereas some show flat and slowly rising curves.

MVD by CD34 staining showed an uneven distribution of positively stained vascular endothelial cells in the renal cell carcinoma lesions. Some tumors were found to have clustered capillaries, whereas other tumors were found to have sparse capillaries. Capillaries were unevenly distributed in density on the edges of the tumor, corresponding to the regions where carcinoma cells reproduce most actively, and were scarce or nonexistent in the central part or close to the necrotic areas of the tumor (Figs. 4A and 4B).

View larger version (158K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4A —Capillaries in different patients with renal cell carcinoma. Photomicrographs show clustered (A) and sparse (B) capillaries. Immunoreactivity for CD34 is in brown. (CD34 stain, x200)

View larger version (145K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4B —Capillaries in different patients with renal cell carcinoma. Photomicrographs show clustered (A) and sparse (B) capillaries. Immunoreactivity for CD34 is in brown. (CD34 stain, x200)

View larger version (126K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5A —53-year-old man with renal cell carcinoma. Transverse contrast-enhanced CT scan shows circular enhancement and blood pool image of tumor parenchyma on left side.

View larger version (133K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5B —53-year-old man with renal cell carcinoma. Photomicrograph shows clustered tumor capillaries in brown. (CD34 stain, x200)

View larger version (131K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6A —48-year-old man with small renal cell carcinoma. Dynamic CT scan shows tumors appearing with relatively low density in renal parenchyma phase.

View larger version (136K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6B —48-year-old man with small renal cell carcinoma. Photomicrograph shows tumor capillaries with shape of sprouts appearing in pale brown. (CD34 stain, x200)

View larger version (11K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 7 —Scatterplot shows density before and after tissue enhancement versus microvessel density.

View larger version (12K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8 —Scatterplot shows tissue-blood ratio versus microvessel density.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 9 —Scatterplot shows area under time-density curve versus microvessel density.

View larger version (13K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 10 —Scatterplot shows slope of time-density curve versus microvessel density.

Nuclear Grade
All enhancement parameters and tumor MVD did not significantly correlate with tumor nuclear grade (r = 0.24, 0.32, 0.002, 0.147, and 0.96, for H, TBR, AR, slope, and MVD, respectively; p > 0.05). They were not predictive of nuclear grade.

Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
To date, no single method has been validated for the measurement of the complex process of tumor angiogenesis in vivo. In this study, we sought to identify whether dynamic CT enhancement parameters could be a method of predicting tumor angiogenesis in vivo. Our data revealed that dynamic CT enhancement parameters positively corresponded with tumor MVD in vascular hot spots of renal cell carcinomas. A prior study showed a similar conclusion that is partially consistent with our data [18]. As we know, the slope in the time-density curve reflects the rate of contrast media uptake in early-stage enhancement. Early-stage contrast enhancement is highly suggestive of carcinoma and is believed to be caused by tumor angiogenesis [19]. Our study combining imaging with histopathology indicates that the early-stage enhancement rate of a tumor correlates with the quantity of vessels. Tumor vascularity can partially explain the swift density change in the early-stage enhancement in renal cell carcinoma. Our data were consistent with those of prior studies in finding a positive correlation between the slope and the tumor MVD [20-24].

The values H and TBR are generally used to reflect tumor density change after contrast media injection. Research has shown that injected contrast media tend to flow into tissues that are densely populated with vessels and into extracellular space, thereby suggesting that angiogenesis is the foundation for tumor enhancement [25, 26]. In line with this finding, our study provided evidence that a positive correlation exists between MVD, H, and TBR. This result suggests that lesion enhancement may become more intense as the number of tumor vessels increases. Considering differences in the weight and body fluid of each individual patient, the use of H as an indicator of tissue enhancement is somewhat approximate. Therefore, TBR was adopted in this study to assess the maximum enhancement of the tumor objectively and precisely.

The AR under the time-density curve is the sum of the area in the rapid enhancement phase plus the area in the delayed enhancement phase. The AR might be an overall reflection of a number of factors, including the perfusion rate of iodine in tissues, the period of iodine accumulation, and so forth. The biologic meaning of the AR needs to be examined and researched further.

The results of our study indicate that dynamic enhanced CT may be a valid method for predicting the possibility that a tumor may be suited to antiangiogenic therapy. Dynamic enhancement parameters may be used as indicators for evaluating tumor vascularity. They may identify patients who will benefit most from antiangiogenic therapy. Therefore, vascular assessment is essential for a tumor [27, 28]. The development of a large number of angiogenic and antiangiogenic therapies has created a need for techniques monitoring tumor response to therapy, and noninvasive methods are always preferred. However, tumor dynamics and highly metastatic areas cannot be assessed sufficiently using a histomorphologic approach in vivo. This is an area in which contrast-enhanced CT might contribute a lot. The clinical monitoring of antiangiogenic therapy requires an imaging technique that is capable of detecting tumor vascularity and its changes with high sensitivity and high specificity. Furthermore, antiangiogenic therapy usually requires lifelong treatment, so a noninvasive, cost-effective technique would be highly desirable. With this information, the oncologist might be able to identify and terminate ineffective therapies, thereby averting unwanted side effects and allowing the initiation of alternative treatment strategies [29].

Another interesting finding in our study was that the data showed enhancement parameters, time-density curve patterns, and tumor MVD values varied widely. This result reveals that dynamic CT enhancement parameters and tumor MVD may reveal the heterogeneity of tumor vascularity, hence reflecting the heterogeneity of tumor angiogenesis, based on the correlation between contrast-enhanced parameters and the tumor MVD. This study shows it is possible to evaluate the heterogeneity of tumor vascularity and angiogenesis with dynamic CT enhancement parameters. In a pathology study, Baish et al. [30] showed that tumor MVD was heterogeneous, with higher density of vessels in the circumference of the tumor and lower density near the center. Eberhard et al. [31] showed that a considerable degree of heterogeneity existed in the intensity of angiogenesis in human tumors, and their data revealed distinct quantitative variations in the intensity of angiogenesis in malignant human tumors. To our knowledge, evaluating tumor microvascular heterogeneity with dynamic CT has not been previously reported.

Our study also indicates dynamic enhanced CT images may be used as a possible method for evaluating tumor heterogeneity. When regional density variability is observed on CT images, evaluating the variation of tumor MVD in vivo will be available. Tumor heterogeneity and progression are major features of neoplastic development. A better understanding of tumor heterogeneity will help scientists to clarify important biologic phenomena such as the appearance of metastases, drug resistance, and spontaneous regression. Such findings could improve cancer prevention, diagnosis, and therapy [32].

Despite the fact that little is known about the mechanism of action of most angiogenesis inhibitors, the data suggest that the suitability of tumors for antiangiogenic therapy may differ between different tumor types and even within one type of tumor. Tumors with a low level of angiogenesis may not benefit much from antiangiogenic therapies [33]. To estimate quantitatively the heterogeneity of tumor vascularity and angiogenesis, further study should be undertaken with dynamic enhanced CT. Dynamic enhanced CT techniques using quantitative enhancement parameters may provide a tool to evaluate the heterogeneity of tumor vascularity and angiogenesis.

Nuclear grade is considered a valuable prognostic factor in renal cell carcinoma. Many CT features of renal cell carcinomas have previously been evaluated to predict the nuclear grade of tumor cells and tumor stages and subtypes as the most clinically relevant. CT features appear more heterogeneous and less marginated as nuclear grade increases [34]. Increasing size and tumor extension beyond the renal capsule have been found to correlate with higher-grade nuclear carcinomas [35]. However, the result that enhancement does not predict nuclear grade has also been reported [36].

In this study, we sought to investigate whether pathologic parameters, including tumor nuclear grade and tumor MVD, could be predicted on the basis of dynamic enhancement parameters. We sought to identify several dynamic enhancement parameters that would be simple to use and relevant to pathologic parameters when evaluating the enhancement of renal cell carcinoma. Our results show that the use of dynamic enhancement parameters as a method may not enable the prediction of tumor nuclear grade.

Our study has several limitations: limited anatomic coverage, the inherent risk of radiation exposure, and increased sensitivity to physiologic motion remain potential drawbacks. In reality, selecting a histologic slice that corresponds directly to the CT image plane is not easy. With major advances in imaging techniques, volume CT or MRI may play an increasingly important role in the imaging of angiogenesis. Those techniques may be used with contrast medium to measure vascular characteristics, including blood flow, blood volume, mean fluid transit time, and capillary permeability [37, 15]. A number of requirements need to be met; these include agreed-upon protocols for data acquisition, analysis, and presentation. Appropriate image processing and visualization tools will be needed to achieve these ends and to characterize the microvasculature. However, conventional imaging techniques, including dynamic enhanced CT, will remain important tools because tumor morphologic and functional monitoring will remain an important factor in cancer treatment.

We conclude from our results that in renal cell carcinoma, enhancement parameters of dynamic CT may be used to assess tumor vascularity and hence to assess tumor angiogenesis in vivo. Dynamic enhanced CT images may reflect the heterogeneity of tumor angiogenesis on the basis of the correlation between enhancement parameters and MVD in renal cell carcinoma. In this study, the finding of a possible predictive method for the enhancement parameters is considered to be preliminary because of the small number of patients. Therefore, additional studies with larger numbers of patients are needed to verify these promising results.

Acknowledgments
 
We thank Li Ning for technical assistance, photography, and typing the manuscript. We also thank Zhang Xiuhui and Tang Ruyong for pathology assistance.

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