A Bayesian hierarchical model for DCE-MRI to evaluate treatment response in a phase II study in advanced squamous cell carcinoma of the head and neck
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Pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) were used to assess the perfusion effects due to treatment response using a tyrosine kinase inhibitor. A Bayesian hierarchical model (BHM) is proposed, as an alternative to voxel-wise estimation procedures, to test for a treatment effect while explicitly modeling known sources of variability.
Materials and methods
Nine subjects from a randomized, blinded, placebo-controlled, multicenter, phase II study of lapatinib were examined before and after treatment. Kinetic parameters were estimated, with an extended compartmental model and subject-specific arterial input function, on a voxel-by-voxel basis.
The group treated with lapatinib had a decrease in median K trans of 0.17min−1, when averaged across all voxels in the tumor ROIs, compared with no change in the placebo group based on nonlinear regression. A hypothesis test of equality between pre- and posttreatment K trans could not be rejected against a one-sided alternative (P = 0.09). Equality between median K trans in placebo and lapatinib groups posttreatment could also not be rejected using the BHM (P = 0.32). Across all scans acquired in the study, estimates of K trans at one site were greater on average than those at the other site by including a site effect in the BHM. The inter-voxel variability is of similar order (within 15%) when compared to the inter-patient variability.
Though the study contained a small number of subjects and no significant difference was found, the Bayesian hierarchical model provided estimates of variability from known sources in the study and confidence intervals for all estimated parameters. We believe the BHM provides a straightforward and thorough interrogation of the imaging data at the level of voxels, patients or sites in this multicenter clinical study.
KeywordsBayesian Gadolinium Oncology Perfusion Permeability
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- 3.Cao Y, Popovtzer A, Li D, Chepeha D, Moyer JS, Prince ME, Worden F, Teknos T, Bradford C, Mukherji SK, Eisbruch A (2008) Early prediction of outcome in advanced head and neck cancer based on tumour blood volume alterations during therapy: a prospective study. Intern J Radiat Oncol Biol Phy 72(5): 1287–1290CrossRefGoogle Scholar
- 5.Wilmes LJ, Pallavicini MG, Fleming LM, Gibbs J, Wang D, Li KL, Partridge SC, Henry RG, Shalinsky DR, Hu-Lowe D, Park JW, McShane TM, Lu Y, Brasch RC, Hylton NM (2007) AG-013736, a novel inhibitor of VEGF receptor tyrosine kinases, inhibits breast cancer growth and decreases vascular permeability as detected by dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Imaging 25: 319–327PubMedCrossRefGoogle Scholar
- 13.Kim S, Quon H, Loevner LA, Rosen MA, Dougherty L, Kilger AM, Glickson JD, Poptani H (2007) Transcytolemmal water exchange in pharmacokinetic analysis of dynamic contrast-enhanced MRI data in squamous cell carcinoma of the head and neck. J Magn Reson Imaging 26(6): 1607–1617PubMedCrossRefGoogle Scholar
- 14.Newbold K, Castellano I, Charles-Edwards E, Mears D, Sohaib A, Leach M, Rhys-Evans P, Larke P, Fisher C, Harrington K, Nutting C (2009) An exploratory study into the role of dynamic contrast-enhanced magnetic resonance imaging or perfusion computer tomography for detection of intratumoral hypoxia in head-and-neck cancer. Intern J Radiat Oncol Biol Phys 74(1): 29–37CrossRefGoogle Scholar
- 22.Galbraith SM, Rustin GJ, Lodge MA, Taylor NJ, Stirling JJ, Jameson M, Thompson P, Hough D, Gumbrell L, Padhani AR (2002) Effects of 5,6-dimethylxanthane-4-acetic acid on human tumor microcirculation assessed by dynamic contrast-enhanced magnetic resonance imaging. J Clin Oncol 20(18): 3826–3840PubMedCrossRefGoogle Scholar
- 23.Evelhoch JE, LoRusso PM, He Z, DelProposto Z, Polin L, Corbett TH, Langmuir P, Wheeler C, Stone A, Leadbetter J, Ran AJ, Blakey DC, Waterton JC (2004) Magnetic resonance imaging measurements of the response of murine and human tumors to the vascular-targeting agent ZD6126. Clin Cancer Res 10: 3650–3657PubMedCrossRefGoogle Scholar
- 24.Galbraith SM, Maxwell RJ, Lodge MA, Tozer GM, Wilson J, Taylor NJ, Stirling JJ, Sena L, Padhani AR, Rustin GJS (2003) Combrestatin A4 phosphate has tumor antivascular activity in rat and man as demonstrated by dynamic magnetic resonance imaging. J Clin Oncol 21(15): 2831–2842PubMedCrossRefGoogle Scholar
- 25.Brown H, Prescott R (1999) Applied mixed models in medicine. Wiley, ChichesterGoogle Scholar
- 29.Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, Jackson A, Watson Y, Davies K, Jayson GC (2006) Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 56: 993– 1000PubMedCrossRefGoogle Scholar
- 32.Whitcher B, Schmid VJ (2010) dcemriS4: a package for medical image analysis. R package version 0.40Google Scholar
- 33.R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0Google Scholar
- 34.Gilks WR, Richardson S, Spiegelhalter DJ (1996) Markov chain Monte Carlo in practice. Chapman & Hall, LondonGoogle Scholar
- 37.Collins DJ, Padhani AR (2004) Dynamic magnetic resonance imaging of tumor perfusion. IEEE Eng Biol Med Magazine 65–83Google Scholar
- 38.del Campo JM, Sebastian P, Hitt R, Carracedo C, Lokanatha D, Bourhis J, Harrington K, Midwinter D, El Hariry I, Biswas-Baldwin N (2008) Effect of lapatinib monotherapy on apoptosis and proliferation: results of a phase II randomised study in patients with locally advanced squamous cell carcinoma of the head and neck (SCCHN). Ann Oncol 19(Suppl. 8):viii217–viii224. Abstract 6880Google Scholar