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LETTERS TO THE EDITOR

Gadolinium-based versus US microbubble contrast media have very different mechanisms of action, thus reflecting different tumor properties. The former distributes in the extracellular space (intravascular and extracellular, extravascular spaces), whereas the latter remains in the intravascular space and only reflects the vascular properties of HCC (1). The discordant appearance of HCC, especially at later phases of contrast-enhanced US compared to MR imaging, has been described (1,2). The importance of properly timed delayed phase imaging in MR imaging avoids misdiagnosis of intrahepatic cholangiocarcinoma as HCC (3), as it can have a very similar appearance to HCC at delayed contrast-enhanced US (4). For these reasons, technical recommendations based on experience with contrast-enhanced US of HCC imaging cannot and should not be simply transferred to MR imaging of HCC in our opinion. Disclosures of Conflicts of Interest: C.W. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: receives payment from Philips Healthcare for board membership on the Radiology Medical Advice Network. Other relationships: none to disclose. M.W.R. No relevant conflicts of interest to disclose. J.K.H. No relevant conflicts of interest to disclose. H.K.H. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: is a paid ad-hoc consultant for Bayer Healthcare. Other relationships: none to disclose. E.A.P. No relevant conflicts of interest to disclose. J.B. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: received a grant from Arqule and Bayer Shering Pharma; is a consultant on the advisory boards of Biocompatibles, Abbott, BMS, Chugai Pharma, Glaxo-Welcome, Imclone, Daiichi Sankyo, Kowa, Lilly, Novartis, Roche, Sanofi, Terumo, and Wako. Other relationships: none to disclose.

References 1. Wilson SR, Kim TK, Jang HJ, Burns PN. Enhancement patterns of focal liver masses: discordance between contrast-enhanced sonography and contrast-enhanced CT and MRI. AJR Am J Roentgenol 2007;189(1):W7– W12. 2. Burns PN, Wilson SR. Focal liver masses: enhancement patterns on contrast-enhanced images—concordance of US scans with CT

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scans and MR images. Radiology 2007;242(1): 162–174. 3. Rimola J, Forner A, Reig M, et al. Cholangiocarcinoma in cirrhosis: absence of contrast washout in delayed phases by magnetic resonance imaging avoids misdiagnosis of hepatocellular carcinoma. Hepatology 2009;50(3): 791–798. 4. Vilana R, Forner A, Bianchi L, et al. Intrahepatic peripheral cholangiocarcinoma in cirrhosis patients may display a vascular pattern similar to hepatocellular carcinoma on contrast-enhanced ultrasound. Hepatology 2010; 51(6):2020–2029.

Reproducibility of Dynamic Contrastenhanced MR Imaging From Isabelle Thomassin-Naggara, MD, PhD,* Charles-André Cuenod, MD, PhD,† and Daniel Balvay, PhD† Department of Radiology, Hôpital Tenon, Assistance Publique–Hôpitaux de Paris, Paris, France* e-mail: [email protected] Imaging Laboratory Research, Université Rene Descartes, UMR 970 INSERM, Paris, France† Editor: We read with much interest the article by Dr Heye and colleagues in the March 2013 issue of Radiology (1) regarding the reproducibility of dynamic contrast material–enhanced magnetic resonance (MR) imaging quantification in the female pelvis. The authors show that although Kety-Tofts compartmental models have been fairly standardized, there remains considerable variability in the evaluation of pharmacokinetic parameters depending on the software used. The principal cause for this variability appears to be the arbitrary selection of a theoretical arterial input function (AIF), well known to decrease reproducibility in extracellular extravascular volume fraction (ve), volume transfer constant (Ktrans), and fractional plasma volume estimates and dependent in part on variations in body weights (allometric theory) (2–5). Indeed, the authors report on a population of 15 patients with body weights ranging from

64.4 to 136.5 kg. One option to compensate for this limitation would be to correct the mean AIF for variations in body weight. The only software available that provides weight correction (CADvue; iCAD, Nashua, NH) allows for decreased variability in ve estimation but is still associated with a large variability in Ktrans. This article shows another limitation of using a theoretical AIF: decreased reproducibility between software packages. The similarities of the distribution between ve and the initial area under the gadolinium curve (iAUGC), both in a “U” pattern, suggest that the variability of ve is mainly due to the difficulties in translating contrast agent concentration into MR contrast enhancement. Indeed, iAUGC is not affected by technical variability in pharmacokinetic modelling so much as by signal calibration. Mean ve values follow directly the differences of signal calibration in mean iAUGC values. In our opinion, the study suffers from two main methodologic limitations. First, the relevance of the scaling factor (ie, signal calibration) should have been tested in a subpopulation for which large vessels are visible and where the ve should be equal to 100%. Bias and variability of ve in this region would allow comparison of the software. Second, as mean parameter values are reported differently between software packages, the resulting concordance (Bland-Altman and intraclass correlation coefficient), by definition, is expected to be low. Perhaps if only correlation had been tested, the authors would have noted consistency between software beyond a calibration factor. This analysis of correlation is very important because if no correlation exists, the significance of any software analysis in routine clinical use can be questioned. Disclosures of Conflicts of Interest: I.T. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: received payment for development of educational presentations from ESRMB; institution received travel/accommodations/ meeting expenses unrelated to activities listed from General Electric. Other relationships: none to disclose. C.A.C. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: re-

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LETTERS TO THE EDITOR

ceived travel/accommodations/meeting expenses from Guerbet and Schering. Other relationships: none to disclose. D.B. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: is a paid consultant for Intrasense. Other relationships: none to disclose.

References 1. Heye T, Davenport MS, Horvath JJ, et al. Reproducibility of dynamic contrast-enhanced MR imaging. I. Perfusion characteristics in the female pelvis by using mulitple computeraided diagnosis perfusion analysis solution. Radiology 2013;266(3):801–811. 2. Parker GJ, Roberts C, Macdonald A, et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 2006; 56(5):993–1000. 3. Port RE, Knopp MV, Brix G. Dynamic contrast-enhanced MRI using Gd-DTPA: interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magn Reson Med 2001;45(6):1030–1038. 4. Rijpkema M, Kaanders JH, Joosten FB, et al. Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors. J Magn Reson Imaging 2001;14(4):457–463. 5. West GB, Brown JH, Enquist B. A general model for the origin of allometric scaling laws in biology. Science 1997;276:122–126.

Response From Tobias J. Heye, MD, and Daniel T. Boll, MD Department of Radiology, Duke University Medical Center, DUMC 3808, Durham, NC 27710 e-mail: [email protected] We thank Dr Thomassin-Naggara and colleagues for their interest in our article focusing on the reproducibility of dynamic contrast-enhanced MR imaging among various postprocessing workstations. Dr Thomassin-Naggara and colleagues raise important questions, which we would like to address. We agree with the first point referring to the AIF. Differences in available AIF most likely caused the greatest variation in pharmacokinetic parameter output, as discussed in our article. We would like to

emphasize the scope of our article: to determine if different dynamic contrastenhanced MR imaging postprocessing solutions show sufficient agreement even when processing parameters were rigorously aligned based on available input options. We do agree with Dr Thomassin-Naggara and colleagues that conversion of signal into contrast agent concentration as indicated in our data seems to be another main source for variation. We would like to politely disagree with Dr Thomassin-Naggara and colleagues with regard to their last two points. As mentioned, our research set out to compare software solutions that are available to the end user and to test the degree of variability an end user would experience. The end user should not be expected to realize that mean values may not be reported in the same fashion. Furthermore, the aim of the study was to investigate the observed variation while identifying contributing factors through statistical means and not to align or correct the output of different workstations. In addition, the research sought not to align pharmacokinetic parameter output or determine a scaling factor to improve reproducibility among workstations. The normalization factor used in our study is a controversial step, as discussed, but mainly served as a means to allow for any offset correction by a decimal factor. Cleary, without a normalization factor, variation between pharmacokinetic parameter outputs would be substantially higher. With respect to investigating the correlation between different workstations, we would like to refer to an article by Bland and Altman (1). Although the methods suggested by Dr Thomassin-Naggara and colleagues may be suitable for future research and provide interesting insight into how each individual software handles the data, the tested workstations remain the proverbial “black boxes,” with possibly the next software update changing the internal algorithms in a way that renders any previous analysis outdated, as reported in the literature (2).

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Disclosures of Conflicts of Interest: T.J.H. No relevant conflicts of interest to disclose. D.T.B. No relevant conflicts of interest to disclose.

References 1. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 1986;346:1085–1087. 2. Goh V, Shastry M, Engledow A, et al. Commercial software upgrades may significantly alter perfusion CT parameter values in colorectal cancer. Eur Radiol 2011;21(4):744– 749.

Adenocarcinoma of the Esophagogastric Junction: How to Measure the Tumor Volume? From Nan Jiang, MD, Shunlin Guo, MD, Hao Yuan, MD, Zhongchun Zhou, MD, Yinzhong Wang, MD, and Junqiang Lei, MD Department of Radiology, The First Affiliated Hospital, Lanzhou University, 1 Donggang West Rd, 730000, Lanzhou, China e-mail: [email protected] Editor: We read with great interest the recent article by Li and colleagues in the October issue of Radiology (1), in which the authors reported a useful method for measuring the volume of the tumor for differentiating among stages N0–N3 disease in patients with adenocarcinoma of the esophagogastric junction. To our knowledge, it is the first time that gross tumor volume has been shown to be an independent risk factor for lymph node metastasis. However, there are a number of issues raised that require further clarification. The method used to define tumor volume is unclear. First, Li and colleagues state that to define the area of the tumor, we need to draw along the visible margins of the thickened walls; however, a thickened wall does not necessarily equate to the margin of tumor (2,3). It is also unclear which vascular phase should be used for these measurements. Was volume delineation as an example in figure 1 obtained in the portal venous phase? If so, why the choice of 621