A bayesian hierarchical model for the analysis of a longitudinal dynamic contrast-enhanced MRI oncology study

Magnetic Resonance in Medicine

Volumn 61, Issue 1, 2009, Pages 163-174

  Schmid, Volker J ^a   Whitcher, Brandon ^a,c   Padhani, Anwar R ^b   Jane Taylor, N ^b   Yang, Guang Zhong ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b Paul Strickland Scanner Centre   (United Kingdom)

^c HAMMERSMITH HOSPITAL   (United Kingdom)

Author keywords

Breast cancer; Clinical trial; Drug development; Mixed effects model; Treatment response

Indexed keywords

CHEMOTHERAPY; DISEASES; HIERARCHICAL SYSTEMS; KINETIC PARAMETERS; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MEDICAL APPLICATIONS; MONTE CARLO METHODS; ONCOLOGY; REGRESSION ANALYSIS;

BREAST CANCER; CLINICAL TRIAL; DRUG DEVELOPMENT; MIXED EFFECTS MODELS; TREATMENT RESPONSE;

PARAMETER ESTIMATION;

CYCLOPHOSPHAMIDE; EPIRUBICIN; FLUOROURACIL;

ARTICLE; BAYES THEOREM; BREAST CANCER; CANCER CHEMOTHERAPY; CLINICAL ARTICLE; DIAGNOSTIC IMAGING; FEMALE; HUMAN; IMAGE ANALYSIS; KINETICS; LONGITUDINAL STUDY; MATHEMATICAL MODEL; MONTE CARLO METHOD; MULTIPLE CYCLE TREATMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC;

EID: 60349086203     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.21807     Document Type: Article

References (41)

1. Padhani AR, Yarnold J, Regan J, Husband JE. Dynamic MRI of breast hardness following radiation treatment. J Mag Reson Imag 2003;17:427-434.
2. Collins DJ, Padhani AR. Dynamic magnetic resonance imaging of tumor perfusion. IEEE Eng Biol Med Mag 2004;65-83.
3. 1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. J Mag Reson Imag 1999;10:223-232.
4. Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer-Verlag; 2000.
5. Brown H, Prescott R. Applied mixed models in medicine. Chichester, UK: Wiley; 1999.
6. Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling for group analysis of fMRI. NeuroImage 2003;20:1052-1063.
7. Friston KJ, Stephan KE, Lund TE, Morcom A, Kiebel S. Mixed-effects and fMRI studies. NeuroImage 2005;24:244-252.
8. Roche A, Mériaux S, Keller M, Thirion B. Mixed-effect statistics for group analysis in fMRI: A non-parametric maximum likelihood approach. NeuroImage 2007;38:501-510.
9. Fahrmeir L, Lang S. Bayesian inference for generalized additive mixed models based on Markov random field priors. Appl Stat 2001;50:201-220.
10. Fahrmeir L, Tutz G. Multivariate statistical modelling based on generalized linear models, 2nd ed. New York: Springer; 2001.
11. Tibshirani R. Regression shrinkage and selection via the lasso. J Rl Stat Soc Ser B 1996;58:267-288.
12. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis, 2nd ed. Boca Raton, Florida: Chapman & Hall/CRC; 2003.
13. Wikle CK. Hierarchical models in environmental science. Int Stat Rev 2002;71:181-199.
14. Kety S. Blood-tissue exchange methods. Theory of blood-tissue exchange and its application to measurement of blood flow. Methods Med Res 1960;8:223-227.
15. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging, part 1: Fundamental concepts. Magn Reson Med 1984;17:357-367.
16. Larsson HB, Tofts PS. Measurement of the blood-brain barrier permeability and leakage space using dynamic Gd-DTPA scanning-A comparison of methods. Mag Reson Med 1992;24:174-176.
17. Weinmann HJ, Laniado M, Mutzel W. Pharmacokinetics of Gd-DTPA/ dimeglumine after intraveneous injection into healthy volunteers. Physiol Chem Phys Med NMR 1984;16:167-172.
18. Fritz-Hansen T, Rostrup E, Larsson HBW, Søndergaard L, Ring P, Hen- riksen O. Measurement of the arterial concentration of Gd-DTPA using MRI: A step toward quantitative perfusion imaging. Mag Reson Med 1996;36:225-231.
19. 1- weighted dynamic contrast-enhanced MRI. In: Jackson A, Buckley DL, Parker GJM, editors, Dynamic contrast-enhanced magnetic resonance imaging in oncology. Berlin: Springer; 2005; pp. 69-80.
20. Genovese CR. A Bayesian time-course model for functional magnetic resonance imaging data (with discussion). J Am Stat Assoc 2000;95:691-719.
21. Friston KJ, Penny W, Phillips C, Hinton G, Ashburner J. Classical and Bayesian inference in neuroimaging: Theory. NeuroImage 2002;16:465-483.
22. Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM. Characterization and pro-pogation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003;50:1077-1088.
23. Hastie TJ, Tibshirani RJ. Generalized additive models. London/Boca Raton: Chapman & Hall/CRC, 1990.
24. Gelman A, Stern H, Carlin J, Rubin D. Bayesian data analysis. Chapman & Hall/CRC Press, London/Boca Raton, 2003.
25. Schmid VJ, Whitcher B, Padhani AR, Taylor NJ, Yang GZ. Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging. IEEE Trans Med Imag 2006;25:1627-1636.
26. Clayton DG. Generalized linearmixed models. In: Gilks WR, Richardson S, Spiegelhalter DJ, editors, Markov chain monte carlo in practice. Boca Raton/London, Chapman & Hall, 1996;275-301.
27. Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, Vol. 1. New York: Wiley; 2nd edition, 1994.
28. Parker G, Suckling J, Tanner S, Padhani A, Husband J, Leach M. MRIW: parametric analysis software forcontrast-enhanced dynamic MR imaging in cancer. Radiographics 1998;18:497-506.
29. Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, Vol. 2. New York: Wiley; 1995.
30. Ah-See MLW, Makris A, Taylor NJ, Burcombe RJ, Harrison M, Stirling JJ, Richman PI, Leach MO, Padhani AR. Does vascular imaging with MRI predict response to neoadjuvant chemotherapy in primary breast cancer? J Clin Oncol (Meeting Abstracts) 2004;22:582.
31. Parker G, Suckling J, Tanner S, Padhani A, Revell P, Husband J, Leach M. Probing tumor microvascularity by measurement, analysis and display of contrast agent uptake kinetics. J Magn Res Imaging 1997;7:564-574.
32. d'Arcy J, Collins D, Padhani A, Walker-Samuel S, Suckling J, Leach M. Informatics in radiology (infoRAD): Magnetic resonance imaging workbench: analysis and visualization of dynamic contrast-enhanced MR imaging data. Radiographics 2006;26:621-632.
33. Leach MO, Brindle KM, Evelhoch JL, Griffiths JR, Horsman MR, Jackson A, Jayson GC, Judson IR, Knopp MV, Maxwell RJ, McIntyre D, Padhani AR, Price P, Rathbone R, Rustin GJ, Tofts PS, Tozer GM, Vennart W, Waterton JC, Williams SR, Workman P. The assessment of antian-giogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 2005;92:1599-1610.
34. Gilks WR, Richardson S, Spiegelhalter DJ, editors. Markov chain Monte Carlo in practice. London: Chapman & Hall; 1996.
35. Liu J, Wong W, Kong A. Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes. Biometrika 1994;81:27-40.
36. Schmid VJ, Whitcher B, Yang GZ, Taylor NJ, Padhani AR. Statistical analysis of pharmacokinetical models in dynamic contrast-enhanced magnetic resonance imaging. In Duncan J, Gerig G, editors, Medical imaging computing and computer-assisted intervention-MICCAI2005, number 3750 in lecture notes in computer science. Berlin: Springer, 2005; pp. 886-893.
37. Silverman BW. Density estimation. London: Chapman & Hall; 1986.
38. Beresford MJ, Padhani AR, Taylor NJ, Ah-See ML, Stirling JJ, Makris A, d'Arcy JA, Collins DJ. Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRI. J Magn Reson Imaging 2006;24:1316-1325.
39. St. Lawrence KS, Lee TY. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain. I. Theoretical derivation. J Cereb Blood Flow Metabol 1998;18:1365-1377.
40. Port RE, Knopp MV, Hoffmann U, Milker-Zabel S, Brix G. Multicompartment analysis of gadolinium chelate kinetics: Blood-tissue exchange in mammary tumors as monitored by dynamic MR imaging. J Magn Reson Imaging 1999;10:233-241.
41. Rue H. Fast sampling of Gaussian Markov random fields. J R Stat Soc Ser B 2001;63:325-338.