{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T21:40:11Z","timestamp":1732743611777,"version":"3.29.0"},"reference-count":40,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"Abstract<\/jats:title>\n 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 \n \n \n \n T<\/m:mi>\n 1<\/m:mn>\n <\/m:msub>\n <\/m:math>\n \n {T_{1}}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-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 \n \n \n \n C<\/m:mi>\n \u2062<\/m:mo>\n \n (<\/m:mo>\n t<\/m:mi>\n )<\/m:mo>\n <\/m:mrow>\n <\/m:mrow>\n <\/m:math>\n \n {C(t)}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula> as function of time t<\/jats:italic>. 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 (\n \n \n \n K<\/m:mi>\n trans<\/m:mi>\n <\/m:msub>\n <\/m:math>\n \n {K_{\\mathrm{trans}}}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula>, \n \n \n \n k<\/m:mi>\n ep<\/m:mi>\n <\/m:msub>\n <\/m:math>\n \n {k_{\\mathrm{ep}}}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula>, \n \n \n \n \u03c5<\/m:mi>\n p<\/m:mi>\n <\/m:msub>\n <\/m:math>\n \n {\\upsilon_{\\mathrm{p}}}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula>, \n \n \n \n \u03c5<\/m:mi>\n e<\/m:mi>\n <\/m:msub>\n <\/m:math>\n \n {\\upsilon_{\\mathrm{e}}}<\/jats:tex-math>\n <\/jats:alternatives>\n <\/jats:inline-formula>) 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.<\/jats:p>","DOI":"10.1515\/mcma-2024-2018","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T16:40:42Z","timestamp":1727800842000},"page":"437-448","source":"Crossref","is-referenced-by-count":0,"title":["Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T<\/i>\n 1<\/sub>-weighted MRI using a three level hierarchical Bayesian model"],"prefix":"10.1515","volume":"30","author":[{"given":"Kahina","family":"Bouchebbah","sequence":"first","affiliation":[{"name":"Bejaia University , Faculty of Exact Sciences , Department of Operational Research , Bejaia 06000 , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabil","family":"Zougab","sequence":"additional","affiliation":[{"name":"Bejaia University , Faculty of Technology , Department of Electrical Engineering ; and Bejaia University, Faculty of Technology, LaMOS Laboratory , Bejaia 06000 , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"2024112721095506572_j_mcma-2024-2018_ref_001","doi-asserted-by":"crossref","unstructured":"H. J. W. L. Aerts, N. A. W. van Riel and W. H. Backes,\nSystem identification theory in pharmacokinetic modeling of Dynamic Contrast-Enhanced MRI: Influence of contrast injection,\nMagn. Reson. Med. 59 (2008), 1111\u20131119.","DOI":"10.1002\/mrm.21575"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_002","doi-asserted-by":"crossref","unstructured":"P. Armitage, C. Behrenbruch, M. Brady and N. Moore,\nExtracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast,\nMed. Image. Anal. 9 (2005), no. 4, 315\u2013329.","DOI":"10.1016\/j.media.2005.01.001"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_003","doi-asserted-by":"crossref","unstructured":"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,\nDynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T,\nMagn. Reson. Med. 71 (2014), 2206\u20132214.","DOI":"10.1002\/mrm.24873"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_004","doi-asserted-by":"crossref","unstructured":"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,\nDCE and DW MRI in monitoring response to androgen deprivation therapy in patients with prostate cancer: A feasibility study,\nMagn. Reson. Med. 67 (2012), no. 3, 778\u2013785.","DOI":"10.1002\/mrm.23062"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_005","unstructured":"J. E. Bennett, A. Racine-Poon and J. C. Wakefield,\nMCMC for nonlinear hierarchical models,\nMarkov Chain Monte Carlo in Practice,\nChapman and Hall, London (1996), 337\u2013339."},{"key":"2024112721095506572_j_mcma-2024-2018_ref_006","doi-asserted-by":"crossref","unstructured":"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,\nDCE MRI of prostate cancer,\nAbdom. Radiol. (NY) 41 (2016), 844\u2013853.","DOI":"10.1007\/s00261-015-0589-3"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_007","doi-asserted-by":"crossref","unstructured":"N. Betrouni and G. Tartare,\nProstateAtlas SimDCE: A simulation tool for dynamic contrast enhanced imaging of prostate,\nIRBM 36 (2015), no. 3, 166\u2013169.","DOI":"10.1016\/j.irbm.2015.01.015"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_008","doi-asserted-by":"crossref","unstructured":"J. Chen, J. Yao and D. Thomasson,\nAutomatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment,\nMed. Image. Comput. Comput. Assist. Interv. 11 (2008), 594\u2013601.","DOI":"10.1007\/978-3-540-85988-8_71"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_009","doi-asserted-by":"crossref","unstructured":"E. Cho, D. J. Chung, D. M. Yeo, D. Sohn, Y. Son, T. Kim and S.-T. Hahn,\nOptimal cut-off value of perfusion parameters for diagnosing prostate cancer and for assessing aggressiveness associated with Gleason score,\nClinical Imag. 39 (2015), 834\u2013840.","DOI":"10.1016\/j.clinimag.2015.04.020"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_010","doi-asserted-by":"crossref","unstructured":"C. A. Cuenod and D. Balvay,\nImagerie de la perfusion tissulaire et de la perm\u00e9abilit\u00e9,\nJ. Radiol. Diagn. Interv. 94 (2013), 1184\u20131202.","DOI":"10.1016\/j.jradio.2013.08.011"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_011","doi-asserted-by":"crossref","unstructured":"A. H. El Beltagi, A. H. Elsotouhy, A. M. Own, W. Abdelfattah, K. Nair and S. Vattoth,\nFunctional magnetic resonance imaging of head and neck cancer: Performance and potential,\nNeuroradiology 32 (2019), 36\u201352.","DOI":"10.1177\/1971400918808546"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_012","doi-asserted-by":"crossref","unstructured":"A. Fabijanska,\nA novel approach for quantification of time-intensity curves in a DCE-MRI image series with an application to prostate cancer,\nComput. Biol. Med. 73 (2016), 119\u2013130.","DOI":"10.1016\/j.compbiomed.2016.04.010"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_013","doi-asserted-by":"crossref","unstructured":"F. Fornasa,\nDiffusion-weighted magnetic resonance imaging: What makes water run fast or slow?,\nJ. Clinical Imag. Sci. 1 (2011), no. 2, 1\u20137.","DOI":"10.4103\/2156-7514.81294"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_014","doi-asserted-by":"crossref","unstructured":"A. E. Gelfand and A. F. M. Smith,\nSampling-based approaches to calculating marginal densities,\nJ. Amer. Statist. Assoc. 85 (1990), 398\u2013409.","DOI":"10.1080\/01621459.1990.10476213"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_015","doi-asserted-by":"crossref","unstructured":"A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari and D. B. Rubin,\nBayesian Data Analysis, 3rd ed.,\nChapman and Hall\/CRC, New York, 2013.","DOI":"10.1201\/b16018"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_016","doi-asserted-by":"crossref","unstructured":"S. Guljas\u0306, M. Bens\u0306i\u0107, Z. K. Dupan, O. Pavlovii\u0107, V. Krajina, D. Pavokovii\u0107, P. Takaic\u0306, M. Hrani and T. Salha,\nDynamic Contrast Enhanced study in multiparametric examination of the prostate\u2013Can we make better use of it?,\nTomography 8 (2022), 1509\u20131521.","DOI":"10.3390\/tomography8030124"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_017","doi-asserted-by":"crossref","unstructured":"S. Isebaert, F. De Keyzer, K. Haustermans, E. Lerut, T. Roskams, I. Roebben, H. V. Poppel, S. Joniau and R. Oyen,\nEvaluation of semi-quantitative dynamic contrast-enhanced MRI parameters for prostate cancer in correlation to whole-mount, histopathology,\nEur. J. Radiol. 81 (2012), e217\u2013e222.","DOI":"10.1016\/j.ejrad.2011.01.107"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_018","doi-asserted-by":"crossref","unstructured":"P. Kozlowski, S. D. Chang, R. Meng, B. M\u00e4dler, R. Bell, E. C. Jones and S. Larry Goldenberg,\nCombined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T-quantitative correlation with biopsy,\nJ. Magn. Reson. Imag. 28 (2010), no. 5, 621\u2013628.","DOI":"10.1016\/j.mri.2010.03.011"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_019","doi-asserted-by":"crossref","unstructured":"C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke and H. H. Schild,\nDynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions?,\nRadiology 211 (1999), 101\u2013110.","DOI":"10.1148\/radiology.211.1.r99ap38101"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_020","doi-asserted-by":"crossref","unstructured":"J. Pintaske, P. Martirosian, H. Graf, G. Erb, K-P. Lodemann, C. D. Claussen and F. Schick,\nRelaxivity of gadopentetate dimeglumine (magnevist), gadobutrol (gadovist), and gadobenate dimeglumine (multiHance) in human blood plasma at 0.2, 1.5, and 3 Tesla,\nInvest. Radiol. 41 (2006), 213\u2013221.","DOI":"10.1097\/01.rli.0000197668.44926.f7"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_021","doi-asserted-by":"crossref","unstructured":"M. Plummer,\nSimulation-based Bayesian analysis,\nAnnu. Rev. Stat. Appl. 10 (2023), 401\u2013425.","DOI":"10.1146\/annurev-statistics-122121-040905"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_022","doi-asserted-by":"crossref","unstructured":"P. Puech, A. Sufana Iancu, B. Renard, A. Villers and L. Lemaitre,\nDetecting prostate cancer with MRI: Why and how,\nDiagn. Interv. Imag. 93 (2012), 268\u2013278.","DOI":"10.1016\/j.diii.2012.01.019"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_023","doi-asserted-by":"crossref","unstructured":"R. Sanz-Requena, J. M. Prats-Montalb\u00e1n, L. Mart\u00ed-Bonmat\u00ed, A. Alberich-Bayarri, G. Gar\u00edca-Mart\u00ed, R. P\u00e9rez and A. Ferrer,\nAutomatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images,\nJ. Magn. Reson. Imag. 42 (2015), no. 2, 477\u2013487.","DOI":"10.1002\/jmri.24805"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_024","doi-asserted-by":"crossref","unstructured":"N. Schieda, C. S. Lim, F. Zabihollahy, J. Abreu-Gomez, S. Krishna, S. Woo, G. Melkus, E. Ukwatta and B. Turkbey,\nQuantitative prostate MRI,\nJ. Magn. Reson. Imag. 53 (2020), 1632\u20131645.","DOI":"10.1002\/jmri.27191"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_025","doi-asserted-by":"crossref","unstructured":"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,\nQuantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of dwi and dce-mri derived biomarkers in multicenter oncology trials,\nJ. Magn. Reson. Imag. 49 (2019), no. 7, e101\u2013e121.","DOI":"10.1002\/jmri.26805"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_026","doi-asserted-by":"crossref","unstructured":"S. P. Sourbron and D. L. Buckley,\nOn the scope and interpretation of the Tofts models for DCE MRI,\nMagn. Reson. Med. 66 (2011), 735\u2013745.","DOI":"10.1002\/mrm.22861"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_027","doi-asserted-by":"crossref","unstructured":"S. P. Sourbron and D. L. Buckley,\nTracer kinetic modeling in MRI: Estimating perfusion and capillary permeability,\nPhys. Med. Biol. 57 (2012), R1\u2013R33.","DOI":"10.1088\/0031-9155\/57\/2\/R1"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_028","doi-asserted-by":"crossref","unstructured":"N. Sushentsev, L. Rundo, O. Blyuss, V. J. Gnanapragasam, E. Sala and T. Barrett,\nMRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance,\nSci. Rep. 11 (2021), Article ID 12917.","DOI":"10.1038\/s41598-021-92341-6"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_029","doi-asserted-by":"crossref","unstructured":"P. S. Tofts,\nModeling tracer kinetics in dynamic Gd-DTPA MR imaging,\nJ. Magn. Reson. Imag. 7 (1997), 91\u2013101.","DOI":"10.1002\/jmri.1880070113"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_030","doi-asserted-by":"crossref","unstructured":"P. S. Tofts and A. G. Kermode,\nMeasurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts,\nMagn. Reson. Med. 17 (1991), 357\u2013367.","DOI":"10.1002\/mrm.1910170208"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_031","doi-asserted-by":"crossref","unstructured":"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,\nEstimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols,\nJ. Magn. Reson. Imag. 10 (1999), 223\u2013232.","DOI":"10.1002\/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_032","doi-asserted-by":"crossref","unstructured":"S. Verma, B. Turkbey, N. Muradyan, A. Rajesh, F. Cornud, M. A. Haider, P. L. Choyke and M. Harisinghani,\nOverview of Dynamic Contrast Enhanced MRI in prostate cancer diagnosis and management,\nAJR Am. J. Roentgenol. 198 (2012), 1277\u20131288.","DOI":"10.2214\/AJR.12.8510"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_033","doi-asserted-by":"crossref","unstructured":"J. C. Wakefield,\nThe Bayesian analysis of population pharmacokinetic models,\nJ. Amer. Statist. Assoc. 91 (1996), 62\u201375.","DOI":"10.1080\/01621459.1996.10476664"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_034","doi-asserted-by":"crossref","unstructured":"J. C. Wakefield, L. Aarons and A. Racine-Poon,\nThe Bayesian approach to population pharmacokinetic\/pharmacodynamic modeling,\nCase Studies in Bayesian Statistics,\nSpringer, New York (1999), 205\u2013265.","DOI":"10.1007\/978-1-4612-1502-8_4"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_035","doi-asserted-by":"crossref","unstructured":"C. Wei, B. Bowen Jin, M. Szewczyk-Bieda, S. Gandy, S. Lang, Y. Zhang, Z. Huang and G. Nabi,\nQuantitative parameters in dynamic contrast-enhanced magnetic resonance imaging for the detection and characterization of prostate cancer,\nOncotarget 9 (2018), no. 22, 15997\u201316007.","DOI":"10.18632\/oncotarget.24652"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_036","unstructured":"H. J. Weinmann, M. Laniado and W. Mutzel,\nPharmacokinetics of GdDTPA\/dimeglumine after intravenous injection into healthy volunteers,\nPhys. Chem. Phys. Med. NMR. 16 (1984), no. 2, 167\u2013172."},{"key":"2024112721095506572_j_mcma-2024-2018_ref_037","doi-asserted-by":"crossref","unstructured":"T. E. Yankeelov and J. C. Gore,\nDynamic contrast enhanced magnetic resonance imaging in oncology: Theory, data acquisition, analysis, and examples,\nCurr. Med. Imaging Rev. 3 (2009), 91\u2013107.","DOI":"10.2174\/157340507780619179"},{"key":"2024112721095506572_j_mcma-2024-2018_ref_038","unstructured":"American Cancer Society, 2019, available at https:\/\/www.cancer.org\/cancer\/, accessed on 23 March 2023."},{"key":"2024112721095506572_j_mcma-2024-2018_ref_039","unstructured":"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."},{"key":"2024112721095506572_j_mcma-2024-2018_ref_040","unstructured":"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."}],"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2018\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T21:10:23Z","timestamp":1732741823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2018\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,2]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,11,14]]},"published-print":{"date-parts":[[2024,12,1]]}},"alternative-id":["10.1515\/mcma-2024-2018"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2024-2018","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"type":"print","value":"0929-9629"},{"type":"electronic","value":"1569-3961"}],"subject":[],"published":{"date-parts":[[2024,10,2]]}}}