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Assessment of Lung Cancer Perfusion by Using Patlak Analysis: What Do We Measure?

PET Center, Institute of Radiopharmacy, Research Center Dresden-Rossendorf, PO Box 510119, D-01314 Dresden, Germany
e-mail: j.van_den_hoff{at}fzd.de

Editor:

In the May 2006 issue of Radiology, Dr Ng and colleagues (1) report on quantitative measurements of lung cancer perfusion. The authors repeatedly state that the measured quantities reflect local blood volume (rbv) and permeability (K), and C_t = rbv · b_t + K · b_t · dt is referred to as Patlak analysis, citing the classical work (2); C_t and b_t are the concentration of contrast material within tissue and blood, respectively, and dt indicates change with respect to time. Since the dynamic investigations lasted only 90 seconds, it might be possible to neglect backflow from tissue in order to arrive at this equation (tolerating variable parameter estimation bias), but I would avoid calling this a Patlak analysis (which deals with a different situation).

The parameter K is erroneously and/or inaccurately referred to as "permeability." Moreover, the article seems to equate K with a "quantitative perfusion index," which would be wrong: Within the limits of the approximation used, K = E(F) · F, and the extraction fraction, E, is dependent on blood flow, F, as well as on details of the (patho-)physiological state of the tumor (which might vary massively across the tumor). The parameter K would only be suitable to quantitatively access perfusion if E 1, that is, if one deals with a freely diffusible substance. The contrary seems to be the case according to figure A1: Assuming that units (not specified in the figure) are minutes (x-axis) and milliliters per 100 mL (y-axis)—these units are used elsewhere in the article—one gets an approximate slope K of 9 mL/min/100 mL, which is off by a factor of 10 or more when compared with typical perfusion values. In other words, extraction is actually very small, implying that K is more or less flow-independent (and approximately equal to the permeability surface area product [PS]).

In conclusion, it is of course a valid strategy to evaluate empirically derived quantitative parameters for a phenomenological description of tumor pathophysiology. But I believe that it is not helpful to assign physiological interpretations to the parameters without taking great care. In the present case, K probably is essentially flow-independent and approximately equal to the PS of the contrast agent in the tumor—hence unsuitable even for qualitative assessment of perfusion.

	References

TOP References References

 

1. Ng QS, Goh V, Fichte H, et al. Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor quantitative measurements. Radiology 2006;239(2):547–553.[Abstract/Free Full Text]
2. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 1983;3(1):1–7.[Medline]

Response

Vicky Goh, MA, MRCP, FRCR^*, Ernst Klotz, Dipl Phys, and Quan Sing Ng, MBBS, MRCP

^* Paul Strickland Scanner Centre, Mount Vernon Hospital, Rickmansworth Rd, Northwood, Middlesex HA6 2RN, England
e-mail: vicky.goh{at}paulstrickland-scannercentre.org.uk
Marie Curie Research Wing, Mount Vernon Hospital, Rickmansworth Rd, Northwood, Middlesex HA6 2RN, England
Siemens Medical Solutions, Forchheim, Germany

Professor van den Hoff has raised the important question of how perfusion parameters have been defined in computed tomography (CT) practice. There is indeed a need for clarification and consensus for "perfusion CT" terms, similar to those suggested for dynamic contrast material–enhanced magnetic resonance imaging (1), but in common parlance "perfusion CT" has become synonymous with dynamic contrast-enhanced CT, and the term has been adopted increasingly to describe the acquisition of a multitude of quantitative hemodynamic parameters—not just blood flow but also blood volume, transit time, and permeability measurements (2). Our use of the term "perfusion" in its generic form, referring to "parameters characterizing perfusion" and not "flow per unit tissue volume or mass" (3), reflects current practice.

We fully agree with Professor van den Hoff that the transfer constant, K^trans (1), that we determined in our study is close to the PS. Indeed, by using the relationship: K^trans = (1 – e^–PS/F)F, and typical flow values (F) of 30–100 mL per 100 mL/min for bronchial carcinoma (4), K^trans differed by less than 15% from PS. As clearly stated in the abstract of our article, we were not suggesting in any way that we were measuring blood flow, but only blood volume and what we called "permeability." We concede that using this short form was slightly slapdash, but others also have used the same term to denote the quantity PS (5).

We chose the experimental design in such a way that back flow from the extravascular compartment could essentially be neglected. This was mainly achieved by using a triple-phasic bolus infusion to keep the intravascular concentration at a constant high level. This would not have been the case with a traditional bolus approach. Therefore it was possible to apply the linearization transformation, part of the original Patlak approach, to obtain robust parameter estimates despite the small number of temporal sampling points. The high quality of fit retrospectively justifies this approximation. We accept that this is different from the original use, however it has been applied successfully in previous CT studies (5).

We believe that our technique provides a practical approach to assessing tumor vascularity. The ability to scan a large tumor entirely overcomes the sampling problems of current single-level techniques and provides a better representation of tumor heterogeneity.

	References

TOP References References

 

1. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusible tracer: standardized quantities and symbols. J Magn Reson Imaging 1999;10:223–232.[CrossRef][Medline]
2. Miles KA, Griffiths MR. Perfusion CT: a worthwhile enhancement? Br J Radiol 2003;76:220–231.[Free Full Text]
3. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991;64:409–412.[Abstract/Free Full Text]
4. Kiessling F, Boese J, Corvinus C, et al. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterization and treatment monitoring? Eur Radiol 2004;14:1226–1233.
5. Miles KA, Kelley BB. CT measurements of capillary permeability within nodal masses: a potential technique for assessing the activity of lymphoma. Br J Radiol 1997;70:74–79.[Abstract]

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