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Response to characterization of orbital masses by multiparametric MRI
G Model EURR-7504; No. of Pages 2
ARTICLE IN PRESS European Journal of Radiology xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Correspondence Response to characterization of orbital masses by multiparametric MRI To the editor: We read with much interest the article by Dr Ro and colleagues in the February 2016 issue of the European Journal of Radiology [1] regarding the characterization of orbital masses using multiparametric MRI. The authors showed that dynamic contrast enhancement (DCE) was a helpful tool in association with diffusionweighted imaging when differentiating malignant orbital lesions from benign masses, and should therefore be included in routine diagnostic protocols for orbital imaging. Reportedly, DCE was very useful when scanning numerous different organs, and this technique seemed promising for the exploration of orbital diseases. This study is the first one to report quantitative analysis of DCE in the characterization of orbital masses. Questions surrounding the variability of DCE results remain, due to results calculated from differing parameters such as the type of MRI device and the magnetic field, the post treatment software, or the pharmaceutical model applied [2–4]. Some technical points may improve the performance of DCE in the characterization of orbital lesions, ultimately allowing for reproducibility among future studies at different centers in accordance with QIBA guidelines and the literature [5]. One key point to improve the model proposed in this article is the arterial input function (AIF) selection, since the DCE perfusion model is very sensitive to this parameter. Theoretical arterial input function (AIF) is well known to decrease reproducibility in extracellular extravascular volume fraction (Ve), volume transfer constant (Ktrans) and fractional plasma volume (Vp). Subsequently, a careful selection of the best AIF available should be the first step before DCE analysis [6]. Another way to improve the DCE model rests on the phamacokinetic calculation. Our colleagues selected a single compartment model as opposed to the stronger, more robust Tofts-Kety twocompartment model. Moreover, studies evaluating orbital and ocular perfusion are lacking in the literature, and no study evaluated the best fitted pharmaceutical model for this organ. Orbital vascular anatomy and blood supply is complex with multiple anastomoses between branches arising from the external carotid artery and from the internal carotid artery. Orbital and ocular masses arise from numerous different locations, such as from the extraocular muscles, the lacrimal glands, orbital fat, the optic nerve, or the eyeball itself. All these locations are functionally and anatomically disparate and have different histological formations, thus the probability of one unique perfusion pharmaceutical model fitting
DOIs of original articles: http://dx.doi.org/10.1016/j.ejrad.2015.11.041, http:// dx.doi.org/10.1016/j.ejrad.2016.06.020
all the compartments remains low [7]. It could be interesting in the future to develop pharmaceutical models fitted to the different orbital compartments. Determining an absolute reference to establish ROC curves is both interesting and relevant to clinical practice. Acquiring quantitative data with DCE will make for more precise statistical analyses, especially multivariate analysis, to determine which parameter could be the most informative in the characterization of orbital masses. A potential limitation of this study is the sample of orbital masses and the constitution of the benign and malignant groups. Only one type of benign and malignant masses was included, and the total number of patients remained low. Ocular melanomas, which should be strictly considered ocular and not orbital masses represented half of the malignant lesions, which lent them a strong weight during analysis and may have caused a bias. Among the benign orbital masses, one orbital vein thrombosis, one pyocele, one mucocele, one hematoma, and one cyst were included despite the absence of a tissular compartment, thus preventing any possibility of perfusion analysis. In conclusion, this study reported very interesting results of quantitative analysis of DCE in orbital masses. The results should support larger scale, prospective studies to better characterize orbital masses with DCE in the future as suggested recently by Purohit [8]. Conflicts of interest None. References [1] S.-R. Ro, P. Asbach, E. Siebert, E. Bertelmann, B. Hamm, K. Erb-Eigner, Characterization of orbital masses by multiparametric MRI, Eur. J. Radiol. 85 (2016) 324–336, http://dx.doi.org/10.1016/j.ejrad.2015.11.041. [2] L. Beuzit, P.-A. Eliat, V. Brun, J.-C. Ferré, Y. Gandon, E. Bannier, H. Saint-Jalmes, Dynamic contrast-enhanced MRI: study of inter-software accuracy and reproducibility using simulated and clinical data, J. Magn. Reson. Imaging JMRI (2015), http://dx.doi.org/10.1002/jmri.25101. [3] H. Wang, Z. Su, H. Ye, X. Xu, Z. Sun, L. Li, F. Duan, Y. Song, T. Lambrou, L. Ma, Reproducibility of dynamic contrast-enhanced MRI in renal cell carcinoma: a prospective analysis on intra- and interobserver and scan-rescan performance of pharmacokinetic parameters, Medicine (Baltimore) 94 (2015) e1529, http:// dx.doi.org/10.1097/MD.0000000000001529. [4] V.L. Nguyen, W.H. Backes, M.E. Kooi, M.C.J. Wishaupt, F.A.M.V.I. Hellenthal, E.M.H. Bosboom, R.J. van der Geest, G.W.H. Schurink, T. Leiner, Quantification of abdominal aortic aneurysm wall enhancement with dynamic contrast-enhanced MRI: feasibility, reproducibility, and initial experience, J. Magn. Reson. Imaging JMRI 39 (2014) 1449–1456, http://dx.doi.org/10.1002/ jmri.24302. [5] M.O. Leach, K.M. Brindle, J.L. Evelhoch, J.R. Griffiths, M.R. Horsman, A. Jackson, G.C. Jayson, I.R. Judson, M.V. Knopp, R.J. Maxwell, D. McIntyre, A.R. Padhani, P. Price, R. Rathbone, G.J. Rustin, P.S. Tofts, G.M. Tozer, W. Vennart, J.C. Waterton, S.R. Williams, P. Workman, The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations, Br. J. Cancer 92 (2005) 1599–1610, http://dx.doi. org/10.1038/sj.bjc.6602550.
http://dx.doi.org/10.1016/j.ejrad.2016.06.018 0720-048X/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: A. Lecler, et al., Response to characterization of orbital masses by multiparametric MRI, Eur J Radiol (2016), http://dx.doi.org/10.1016/j.ejrad.2016.06.018
G Model EURR-7504; No. of Pages 2 2
ARTICLE IN PRESS Correspondence / European Journal of Radiology xxx (2016) xxx–xxx
[6] H.-L.M. Cheng, Investigation and optimization of parameter accuracy in dynamic contrast-enhanced MRI, J. Magn. Reson. Imaging 28 (2008) 736–743, http://dx.doi.org/10.1002/jmri.21489. [7] D. Balvay, F. Frouin, G. Calmon, B. Bessoud, E. Kahn, N. Siauve, O. Clément, C.A. Cuenod, New criteria for assessing fit quality in dynamic contrast-enhanced T1-weighted MRI for perfusion and permeability imaging, Magn. Reson. Med. 54 (2005) 868–877, http://dx.doi.org/10.1002/mrm.20650. [8] B.S. Purohit, M.I. Vargas, A. Ailianou, L. Merlini, P.-A. Poletti, A. Platon, B.M. Delattre, O. Rager, K. Burkhardt, M. Becker, Orbital tumours and tumour-like lesions: exploring the armamentarium of multiparametric imaging, Insights Imaging 7 (2016) 43–68, http://dx.doi.org/10.1007/s13244-015-0443-8.
A. Lecler a,b,∗ a Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France b Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France
L. Fournier b,c Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France c Radiology Department, Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France b
∗ Corresponding
author at: Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, 25 rue Manin, 75019 Paris, France. E-mail address: [email protected] (A. Lecler) 26 April 2016
D. Balvay Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France
Please cite this article in press as: A. Lecler, et al., Response to characterization of orbital masses by multiparametric MRI, Eur J Radiol (2016), http://dx.doi.org/10.1016/j.ejrad.2016.06.018
ARTICLE IN PRESS European Journal of Radiology xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Correspondence Response to characterization of orbital masses by multiparametric MRI To the editor: We read with much interest the article by Dr Ro and colleagues in the February 2016 issue of the European Journal of Radiology [1] regarding the characterization of orbital masses using multiparametric MRI. The authors showed that dynamic contrast enhancement (DCE) was a helpful tool in association with diffusionweighted imaging when differentiating malignant orbital lesions from benign masses, and should therefore be included in routine diagnostic protocols for orbital imaging. Reportedly, DCE was very useful when scanning numerous different organs, and this technique seemed promising for the exploration of orbital diseases. This study is the first one to report quantitative analysis of DCE in the characterization of orbital masses. Questions surrounding the variability of DCE results remain, due to results calculated from differing parameters such as the type of MRI device and the magnetic field, the post treatment software, or the pharmaceutical model applied [2–4]. Some technical points may improve the performance of DCE in the characterization of orbital lesions, ultimately allowing for reproducibility among future studies at different centers in accordance with QIBA guidelines and the literature [5]. One key point to improve the model proposed in this article is the arterial input function (AIF) selection, since the DCE perfusion model is very sensitive to this parameter. Theoretical arterial input function (AIF) is well known to decrease reproducibility in extracellular extravascular volume fraction (Ve), volume transfer constant (Ktrans) and fractional plasma volume (Vp). Subsequently, a careful selection of the best AIF available should be the first step before DCE analysis [6]. Another way to improve the DCE model rests on the phamacokinetic calculation. Our colleagues selected a single compartment model as opposed to the stronger, more robust Tofts-Kety twocompartment model. Moreover, studies evaluating orbital and ocular perfusion are lacking in the literature, and no study evaluated the best fitted pharmaceutical model for this organ. Orbital vascular anatomy and blood supply is complex with multiple anastomoses between branches arising from the external carotid artery and from the internal carotid artery. Orbital and ocular masses arise from numerous different locations, such as from the extraocular muscles, the lacrimal glands, orbital fat, the optic nerve, or the eyeball itself. All these locations are functionally and anatomically disparate and have different histological formations, thus the probability of one unique perfusion pharmaceutical model fitting
DOIs of original articles: http://dx.doi.org/10.1016/j.ejrad.2015.11.041, http:// dx.doi.org/10.1016/j.ejrad.2016.06.020
all the compartments remains low [7]. It could be interesting in the future to develop pharmaceutical models fitted to the different orbital compartments. Determining an absolute reference to establish ROC curves is both interesting and relevant to clinical practice. Acquiring quantitative data with DCE will make for more precise statistical analyses, especially multivariate analysis, to determine which parameter could be the most informative in the characterization of orbital masses. A potential limitation of this study is the sample of orbital masses and the constitution of the benign and malignant groups. Only one type of benign and malignant masses was included, and the total number of patients remained low. Ocular melanomas, which should be strictly considered ocular and not orbital masses represented half of the malignant lesions, which lent them a strong weight during analysis and may have caused a bias. Among the benign orbital masses, one orbital vein thrombosis, one pyocele, one mucocele, one hematoma, and one cyst were included despite the absence of a tissular compartment, thus preventing any possibility of perfusion analysis. In conclusion, this study reported very interesting results of quantitative analysis of DCE in orbital masses. The results should support larger scale, prospective studies to better characterize orbital masses with DCE in the future as suggested recently by Purohit [8]. Conflicts of interest None. References [1] S.-R. Ro, P. Asbach, E. Siebert, E. Bertelmann, B. Hamm, K. Erb-Eigner, Characterization of orbital masses by multiparametric MRI, Eur. J. Radiol. 85 (2016) 324–336, http://dx.doi.org/10.1016/j.ejrad.2015.11.041. [2] L. Beuzit, P.-A. Eliat, V. Brun, J.-C. Ferré, Y. Gandon, E. Bannier, H. Saint-Jalmes, Dynamic contrast-enhanced MRI: study of inter-software accuracy and reproducibility using simulated and clinical data, J. Magn. Reson. Imaging JMRI (2015), http://dx.doi.org/10.1002/jmri.25101. [3] H. Wang, Z. Su, H. Ye, X. Xu, Z. Sun, L. Li, F. Duan, Y. Song, T. Lambrou, L. Ma, Reproducibility of dynamic contrast-enhanced MRI in renal cell carcinoma: a prospective analysis on intra- and interobserver and scan-rescan performance of pharmacokinetic parameters, Medicine (Baltimore) 94 (2015) e1529, http:// dx.doi.org/10.1097/MD.0000000000001529. [4] V.L. Nguyen, W.H. Backes, M.E. Kooi, M.C.J. Wishaupt, F.A.M.V.I. Hellenthal, E.M.H. Bosboom, R.J. van der Geest, G.W.H. Schurink, T. Leiner, Quantification of abdominal aortic aneurysm wall enhancement with dynamic contrast-enhanced MRI: feasibility, reproducibility, and initial experience, J. Magn. Reson. Imaging JMRI 39 (2014) 1449–1456, http://dx.doi.org/10.1002/ jmri.24302. [5] M.O. Leach, K.M. Brindle, J.L. Evelhoch, J.R. Griffiths, M.R. Horsman, A. Jackson, G.C. Jayson, I.R. Judson, M.V. Knopp, R.J. Maxwell, D. McIntyre, A.R. Padhani, P. Price, R. Rathbone, G.J. Rustin, P.S. Tofts, G.M. Tozer, W. Vennart, J.C. Waterton, S.R. Williams, P. Workman, The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations, Br. J. Cancer 92 (2005) 1599–1610, http://dx.doi. org/10.1038/sj.bjc.6602550.
http://dx.doi.org/10.1016/j.ejrad.2016.06.018 0720-048X/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: A. Lecler, et al., Response to characterization of orbital masses by multiparametric MRI, Eur J Radiol (2016), http://dx.doi.org/10.1016/j.ejrad.2016.06.018
G Model EURR-7504; No. of Pages 2 2
ARTICLE IN PRESS Correspondence / European Journal of Radiology xxx (2016) xxx–xxx
[6] H.-L.M. Cheng, Investigation and optimization of parameter accuracy in dynamic contrast-enhanced MRI, J. Magn. Reson. Imaging 28 (2008) 736–743, http://dx.doi.org/10.1002/jmri.21489. [7] D. Balvay, F. Frouin, G. Calmon, B. Bessoud, E. Kahn, N. Siauve, O. Clément, C.A. Cuenod, New criteria for assessing fit quality in dynamic contrast-enhanced T1-weighted MRI for perfusion and permeability imaging, Magn. Reson. Med. 54 (2005) 868–877, http://dx.doi.org/10.1002/mrm.20650. [8] B.S. Purohit, M.I. Vargas, A. Ailianou, L. Merlini, P.-A. Poletti, A. Platon, B.M. Delattre, O. Rager, K. Burkhardt, M. Becker, Orbital tumours and tumour-like lesions: exploring the armamentarium of multiparametric imaging, Insights Imaging 7 (2016) 43–68, http://dx.doi.org/10.1007/s13244-015-0443-8.
A. Lecler a,b,∗ a Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France b Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France
L. Fournier b,c Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France c Radiology Department, Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France b
∗ Corresponding
author at: Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, 25 rue Manin, 75019 Paris, France. E-mail address: [email protected] (A. Lecler) 26 April 2016
D. Balvay Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France
Please cite this article in press as: A. Lecler, et al., Response to characterization of orbital masses by multiparametric MRI, Eur J Radiol (2016), http://dx.doi.org/10.1016/j.ejrad.2016.06.018