Startseite Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model
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Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model

  • Kahina Bouchebbah EMAIL logo und Nabil Zougab
Veröffentlicht/Copyright: 2. Oktober 2024
Monte Carlo Methods and Applications
Aus der Zeitschrift Monte Carlo Methods and Applications Band 30 Heft 4

Abstract

Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) is becoming the most widely explored technique in clinical practice for tumor assessment. In acquiring DCE-MRI, a contrast agent (CA), also called tracer, is injected into the blood flow before or during the acquisition of a time series of T 1 -weighted images with fast imaging techniques. When the CA goes through the tissue, MR signal intensity measurements in voxels of the region of interest (ROI) are registered and used to calculate the CA concentration in each voxel. The Tofts models have become standard for the analysis of DCE-MRI and which express tissue CA concentration C ⁢ ( t ) as function of time t. The analysis of quantitative parameters in DCE-MRI provides the quantitative criterion as a reference rather than relying only on the shape of the DCE-curve, as it is used for diagnosis of prostate cancer (PCa). This study aim to provide a new thinking in quantitative analysis which may therefore improve diagnostic accuracy for detection of prostate cancer and could be used in patient baseline prediction and guide management. A hierarchical Bayesian model was built to estimate the values of the four pharmacokinetic parameters ( K trans , k ep , υ p , υ e ) for both prostate healthy and lesion tissues in the peripheral zone. This estimation is important because it help to understand the behavior of the CA in the body and how this latter reacts to the CA in order to emphasize the expectation or the absence of prostate lesion during the diagnosis step.

Keywords: Magnetic resonance imaging (MRI); prostate cancer; pharmacokinetic models; hierarchical Bayesian model; MCMC algorithm
MSC 2020: 62C10; 62F15; 65C05

Acknowledgements

We would like to express our gratitude to the administrator and medical staffs of the Radiology and Medical Imaging Department of the Chahids Mahmoudi Hospital, Tizi Ouzou, Algeria for permission and assistance in collecting and evaluating the DCE-MRI data samples. Notably, we are grateful to Ms. Yamina Oudane for anonymizing the provided clinical data, and to Dr. Farid Kechih for his great help in making and validating the needed ground truths.

References

[1] H. J. W. L. Aerts, N. A. W. van Riel and W. H. Backes, System identification theory in pharmacokinetic modeling of Dynamic Contrast-Enhanced MRI: Influence of contrast injection, Magn. Reson. Med. 59 (2008), 1111–1119. 10.1002/mrm.21575Suche in Google Scholar PubMed

[2] P. Armitage, C. Behrenbruch, M. Brady and N. Moore, Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast, Med. Image. Anal. 9 (2005), no. 4, 315–329. 10.1016/j.media.2005.01.001Suche in Google Scholar PubMed

[3] M. P. Aryal, T. N. Nagaraja, K. A. Keenan, H. Bagher-Ebadian, S. Panda, S. L. Brown, G. Cabral, J. D. Fenstermacher and J. R. Ewing, Dynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T, Magn. Reson. Med. 71 (2014), 2206–2214. 10.1002/mrm.24873Suche in Google Scholar PubMed PubMed Central

[4] T. Barrett, A. B. Gill, M. Y. Kataoka, A. N. Priest, I. Joubert, M. A. McLean, M. J. Graves, S. Stearn, D. J. Lomas, J. R. Griffiths, D. Neal, V. J. Gnanapragasam and E. Sala, DCE and DW MRI in monitoring response to androgen deprivation therapy in patients with prostate cancer: A feasibility study, Magn. Reson. Med. 67 (2012), no. 3, 778–785. 10.1002/mrm.23062Suche in Google Scholar PubMed

[5] J. E. Bennett, A. Racine-Poon and J. C. Wakefield, MCMC for nonlinear hierarchical models, Markov Chain Monte Carlo in Practice, Chapman and Hall, London (1996), 337–339. Suche in Google Scholar

[6] R. M. Berman, A. M. Brown, S. D. Chang, S. Sankineni, M. Kadakia, B. J. Wood, P. A. Pinto, P. L. Choyke and B. Turkbey, DCE MRI of prostate cancer, Abdom. Radiol. (NY) 41 (2016), 844–853. 10.1007/s00261-015-0589-3Suche in Google Scholar PubMed PubMed Central

[7] N. Betrouni and G. Tartare, ProstateAtlas SimDCE: A simulation tool for dynamic contrast enhanced imaging of prostate, IRBM 36 (2015), no. 3, 166–169. 10.1016/j.irbm.2015.01.015Suche in Google Scholar

[8] J. Chen, J. Yao and D. Thomasson, Automatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment, Med. Image. Comput. Comput. Assist. Interv. 11 (2008), 594–601. 10.1007/978-3-540-85988-8_71Suche in Google Scholar PubMed PubMed Central

[9] E. Cho, D. J. Chung, D. M. Yeo, D. Sohn, Y. Son, T. Kim and S.-T. Hahn, Optimal cut-off value of perfusion parameters for diagnosing prostate cancer and for assessing aggressiveness associated with Gleason score, Clinical Imag. 39 (2015), 834–840. 10.1016/j.clinimag.2015.04.020Suche in Google Scholar PubMed

[10] C. A. Cuenod and D. Balvay, Imagerie de la perfusion tissulaire et de la perméabilité, J. Radiol. Diagn. Interv. 94 (2013), 1184–1202. 10.1016/j.jradio.2013.08.011Suche in Google Scholar

[11] A. H. El Beltagi, A. H. Elsotouhy, A. M. Own, W. Abdelfattah, K. Nair and S. Vattoth, Functional magnetic resonance imaging of head and neck cancer: Performance and potential, Neuroradiology 32 (2019), 36–52. 10.1177/1971400918808546Suche in Google Scholar PubMed PubMed Central

[12] A. Fabijanska, A novel approach for quantification of time-intensity curves in a DCE-MRI image series with an application to prostate cancer, Comput. Biol. Med. 73 (2016), 119–130. 10.1016/j.compbiomed.2016.04.010Suche in Google Scholar PubMed

[13] F. Fornasa, Diffusion-weighted magnetic resonance imaging: What makes water run fast or slow?, J. Clinical Imag. Sci. 1 (2011), no. 2, 1–7. 10.4103/2156-7514.81294Suche in Google Scholar PubMed PubMed Central

[14] A. E. Gelfand and A. F. M. Smith, Sampling-based approaches to calculating marginal densities, J. Amer. Statist. Assoc. 85 (1990), 398–409. 10.1080/01621459.1990.10476213Suche in Google Scholar

[15] A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari and D. B. Rubin, Bayesian Data Analysis, 3rd ed., Chapman and Hall/CRC, New York, 2013. 10.1201/b16018Suche in Google Scholar

[16] S. Guljas̆, M. Bens̆ić, Z. K. Dupan, O. Pavloviić, V. Krajina, D. Pavokoviić, P. Takaic̆, M. Hrani and T. Salha, Dynamic Contrast Enhanced study in multiparametric examination of the prostate–Can we make better use of it?, Tomography 8 (2022), 1509–1521. 10.3390/tomography8030124Suche in Google Scholar PubMed PubMed Central

[17] S. Isebaert, F. De Keyzer, K. Haustermans, E. Lerut, T. Roskams, I. Roebben, H. V. Poppel, S. Joniau and R. Oyen, Evaluation of semi-quantitative dynamic contrast-enhanced MRI parameters for prostate cancer in correlation to whole-mount, histopathology, Eur. J. Radiol. 81 (2012), e217–e222. 10.1016/j.ejrad.2011.01.107Suche in Google Scholar PubMed

[18] P. Kozlowski, S. D. Chang, R. Meng, B. Mädler, R. Bell, E. C. Jones and S. Larry Goldenberg, Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T-quantitative correlation with biopsy, J. Magn. Reson. Imag. 28 (2010), no. 5, 621–628. 10.1016/j.mri.2010.03.011Suche in Google Scholar PubMed PubMed Central

[19] C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke and H. H. Schild, Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions?, Radiology 211 (1999), 101–110. 10.1148/radiology.211.1.r99ap38101Suche in Google Scholar PubMed

[20] J. Pintaske, P. Martirosian, H. Graf, G. Erb, K-P. Lodemann, C. D. Claussen and F. Schick, Relaxivity of gadopentetate dimeglumine (magnevist), gadobutrol (gadovist), and gadobenate dimeglumine (multiHance) in human blood plasma at 0.2, 1.5, and 3 Tesla, Invest. Radiol. 41 (2006), 213–221. 10.1097/01.rli.0000197668.44926.f7Suche in Google Scholar PubMed

[21] M. Plummer, Simulation-based Bayesian analysis, Annu. Rev. Stat. Appl. 10 (2023), 401–425. 10.1146/annurev-statistics-122121-040905Suche in Google Scholar

[22] P. Puech, A. Sufana Iancu, B. Renard, A. Villers and L. Lemaitre, Detecting prostate cancer with MRI: Why and how, Diagn. Interv. Imag. 93 (2012), 268–278. 10.1016/j.diii.2012.01.019Suche in Google Scholar

[23] R. Sanz-Requena, J. M. Prats-Montalbán, L. Martí-Bonmatí, A. Alberich-Bayarri, G. Garíca-Martí, R. Pérez and A. Ferrer, Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images, J. Magn. Reson. Imag. 42 (2015), no. 2, 477–487. 10.1002/jmri.24805Suche in Google Scholar

[24] N. Schieda, C. S. Lim, F. Zabihollahy, J. Abreu-Gomez, S. Krishna, S. Woo, G. Melkus, E. Ukwatta and B. Turkbey, Quantitative prostate MRI, J. Magn. Reson. Imag. 53 (2020), 1632–1645. 10.1002/jmri.27191Suche in Google Scholar

[25] A. Shukla-Dave, N. A. Obuchowski, T. L. Chenevert, S. Jambawalikar, L. H. Schwartz, D. Malyarenko, W. Huang, S. M. Noworolski, R. J. Young, M. S. Shiroishi, H. Kim, C. Coolens, H. Laue, C. Chung, M. Rosen, M. Boss and E. F. Jackson, Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of dwi and dce-mri derived biomarkers in multicenter oncology trials, J. Magn. Reson. Imag. 49 (2019), no. 7, e101–e121. 10.1002/jmri.26805Suche in Google Scholar

[26] S. P. Sourbron and D. L. Buckley, On the scope and interpretation of the Tofts models for DCE MRI, Magn. Reson. Med. 66 (2011), 735–745. 10.1002/mrm.22861Suche in Google Scholar

[27] S. P. Sourbron and D. L. Buckley, Tracer kinetic modeling in MRI: Estimating perfusion and capillary permeability, Phys. Med. Biol. 57 (2012), R1–R33. 10.1088/0031-9155/57/2/R1Suche in Google Scholar

[28] N. Sushentsev, L. Rundo, O. Blyuss, V. J. Gnanapragasam, E. Sala and T. Barrett, MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance, Sci. Rep. 11 (2021), Article ID 12917. 10.1038/s41598-021-92341-6Suche in Google Scholar

[29] P. S. Tofts, Modeling tracer kinetics in dynamic Gd-DTPA MR imaging, J. Magn. Reson. Imag. 7 (1997), 91–101. 10.1002/jmri.1880070113Suche in Google Scholar

[30] P. S. Tofts and A. G. Kermode, Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts, Magn. Reson. Med. 17 (1991), 357–367. 10.1002/mrm.1910170208Suche in Google Scholar

[31] P. S. Tofts, G. Brix, D. L. Buckley, J. L. Evelhoch, E. Henderson, M. V. Knopp, H. B. W. Larsson, T-Y. Lee, N. A. Mayr, G. J. M. Parker, R. E. Port, J. Taylor and R. M. Weisskoff, Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols, J. Magn. Reson. Imag. 10 (1999), 223–232. 10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-SSuche in Google Scholar

[32] S. Verma, B. Turkbey, N. Muradyan, A. Rajesh, F. Cornud, M. A. Haider, P. L. Choyke and M. Harisinghani, Overview of Dynamic Contrast Enhanced MRI in prostate cancer diagnosis and management, AJR Am. J. Roentgenol. 198 (2012), 1277–1288. 10.2214/AJR.12.8510Suche in Google Scholar PubMed PubMed Central

[33] J. C. Wakefield, The Bayesian analysis of population pharmacokinetic models, J. Amer. Statist. Assoc. 91 (1996), 62–75. 10.1080/01621459.1996.10476664Suche in Google Scholar

[34] J. C. Wakefield, L. Aarons and A. Racine-Poon, The Bayesian approach to population pharmacokinetic/pharmacodynamic modeling, Case Studies in Bayesian Statistics, Springer, New York (1999), 205–265. 10.1007/978-1-4612-1502-8_4Suche in Google Scholar

[35] C. Wei, B. Bowen Jin, M. Szewczyk-Bieda, S. Gandy, S. Lang, Y. Zhang, Z. Huang and G. Nabi, Quantitative parameters in dynamic contrast-enhanced magnetic resonance imaging for the detection and characterization of prostate cancer, Oncotarget 9 (2018), no. 22, 15997–16007. 10.18632/oncotarget.24652Suche in Google Scholar PubMed PubMed Central

[36] H. J. Weinmann, M. Laniado and W. Mutzel, Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers, Phys. Chem. Phys. Med. NMR. 16 (1984), no. 2, 167–172. Suche in Google Scholar

[37] T. E. Yankeelov and J. C. Gore, Dynamic contrast enhanced magnetic resonance imaging in oncology: Theory, data acquisition, analysis, and examples, Curr. Med. Imaging Rev. 3 (2009), 91–107. 10.2174/157340507780619179Suche in Google Scholar PubMed PubMed Central

[38] American Cancer Society, 2019, available at https://www.cancer.org/cancer/, accessed on 23 March 2023. Suche in Google Scholar

[39] QIBA Profile: DCE-MRI Quantification (DCEMRI-Q), 2017, http://qibawiki.rsna.org/images/1/1f/QIBA_DCE-MRI_Profile-Stage_1-Public_Comment.pdf, accessed on 16 January 2022. Suche in Google Scholar

[40] World Health Organization. Cancer fact sheets: Prostate cancer, 2012, available at https://gco.iarc.fr/today/data/pdf/fact-sheets/cancers/cancer-fact-sheets-19.pdf, accessed on 23 March 2023. Suche in Google Scholar
Received: 2023-11-09
Revised: 2024-09-09
Accepted: 2024-09-11
Published Online: 2024-10-02
Published in Print: 2024-12-01

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Investigating the ecological fallacy through sampling distributions constructed from finite populations
  3. Bernoulli factory: The 2𝚙-coin problem
  4. Randomized vector algorithm with iterative refinement for solving boundary integral equations
  5. Application of semiclassical approximation to stochastic differential equations
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  8. Effect of biaxial strain on the binding energies of adsorbed In and Al atoms on (001) surfaces of InAs and AlAs
  9. Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model
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https://doi.org/10.1515/mcma-2024-2018
Schlagwörter für diesen Artikel
Magnetic resonance imaging (MRI); prostate cancer; pharmacokinetic models; hierarchical Bayesian model; MCMC algorithm

Artikel in diesem Heft

  1. Frontmatter
  2. Investigating the ecological fallacy through sampling distributions constructed from finite populations
  3. Bernoulli factory: The 2𝚙-coin problem
  4. Randomized vector algorithm with iterative refinement for solving boundary integral equations
  5. Application of semiclassical approximation to stochastic differential equations
  6. Trapezoidal and Simpson’s methods with a random design
  7. Periodic INAR(1) model with Bell innovations distribution
  8. Effect of biaxial strain on the binding energies of adsorbed In and Al atoms on (001) surfaces of InAs and AlAs
  9. Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model
  10. Bayesian analysis of the COVID-19 pandemic using a Poisson process with change-points
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