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Can in Vivo Assessment of Tissue Hemodynamics with Dynamic Contrast-enhanced CT Be Used in the Diagnosis of Tumors and Monitoring of Cancer Therapy Outcomes?

Department of Radiology, University of Alabama Medical Center, 619 S 19th St, GSB G301D, Birmingham, AL 35249-6830. wts@uab.edu.

View larger version (128K): [in this window] [in a new window] [Download PPT slide]   Wlad T. Sobol, PhD

View larger version (129K): [in this window] [in a new window] [Download PPT slide]   Joel K. Curé, MD

Advances in imaging technologies such as computed tomography (CT) and magnetic resonance imaging provide opportunities to explore tissue hemodynamics and study the tissue microvasculature (1,2). Preliminary results presented by Cheong et al in this issue of Radiology (3) indicate that when a sufficiently robust model is used, the microcirculatory parameters related to extravasation exhibit distinct, nonoverlapping ranges when calculated for normal brain tissue (both white and gray matter) and meningiomas.

The Science

Imaging studies of microcirculation rely on rapid bolus injection of a contrast medium, followed by rapidly repeated imaging of the volume of interest. Cheong et al set out to evaluate the practical merits of three different classes of models for estimating the values of microcirculatory parameters used in the clinical assessment of lesions. The first is a simple and conventional two-compartment model that assumes instant distribution and mixing of the contrast medium within each compartment; that is, the concentration of contrast medium in each compartment within the volume of interest is assumed to be homogeneous. The second is an advanced distributed-parameter model according to which the concentration of contrast medium in the intravascular space may vary both in space and in time. This model is more realistic but is mathematically complicated and, thus, difficult to use. The third model represents a compromise between those two extremes: It is an adiabatic tissue homogeneity model that rests on a simplification of the distributed-parameter model, inasmuch as the contrast medium concentration in the extravascular extracellular space is assumed to change more slowly than that in the intravascular space.

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The Practice

Clinical use.—The practical implications of the findings by Cheong et al are fascinating. It is true that most meningiomas manifest extraaxially and are not protected by the blood-brain barrier, a fact that makes the authors’ test results seem fairly limited. Their studies of model performances, however, are nonetheless applicable in the assessment of new methods of brain chemotherapy, such as blood-brain barrier disruption strategies. There is also a keen interest in applying hemodynamic methods to assess radiation treatment effectiveness. Imaging techniques that focus on microcirculatory phenomena in tissue have created so much interest that a panel of experts recently dubbed them a "promise for the new millennium" (4).

In a broader sense, the value of the authors’ work is their sensible exploitation of the usability of various models of tissue perfusion. When ideally implemented in the clinical environment, the analytic details of a test assessment tool are hidden behind a user-friendly interface, and the final results are characterized by high sensitivity, high specificity, and high positive predictive value. The last two metrics are highly desirable because they make lesion characterization much more effective. The results of Cheong et al show that imaging studies of tissue hemodynamics are capable of providing high-quality results.

Future opportunities and challenges.—Of course, one set of preliminary results does not allow us to draw definite conclusions. Validation of these preliminary results in a broader clinical setting represents both an opportunity and a challenge for this method to gain wide acceptance. The confirmation of the authors’ findings in a larger sample and the establishment of the applicability of their technique to a variety of lesions would create opportunities to improve the quality of both tumor diagnosis and therapeutic assessment. It is desirable that this method prove to be immune to variables such as patient weight, the volume and timing of contrast medium injection, and variations in data acquisition with diverse hardware or imaging protocols that are common in everyday clinical practice.

Summary

In summary, the authors present encouraging data for the potential clinical value of tissue hemodynamic assessment with dynamic contrast-enhanced CT. These results are robust enough to warrant an optimistic forecast that further refinements of this technique will produce a useful tool for improving the diagnosis of tumors and the monitoring of cancer therapy outcomes.

FOOTNOTES

See also the article by Dennis Cheong et al in this issue.

REFERENCES

1. Lee TY. Functional CT: physiological models. Trends Biotechnol 2002; 20(suppl 8):S3-S10.[Medline]
2. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999; 10:223-232.[CrossRef][Medline]
3. Cheong LH, Lim CC, Koh TS. Dynamic contrast-enhanced CT imaging of intracranial meningioma: a comparison of distributed and compartmental tracer kinetics models—initial results. Radiology 2004; 232:921-930.[Abstract/Free Full Text]
4. Taylor JS, Tofts PS, Port R, et al. MR imaging of tumor microcirculation: promise for the new millennium. J Magn Reson Imaging 1999; 10:903-907.[CrossRef][Medline]

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