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Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer

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Abstract

Purpose

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314–2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known.

Procedures

Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment.

Results

Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining.

Conclusion

Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

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References

  1. Hermann P, Kotek J, Kubíček V, Lukeš I (2008) Gadolinium(III) complexes as MRI contrast agents: ligand design and properties of the complexes. Dalton Trans 23:3027–3047. https://doi.org/10.1039/B719704G

    Article  Google Scholar 

  2. Park JJ, Kim CK, Park SY et al (2014) Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffusion-weighted and dynamic contrast-enhanced MR imaging. Magn Reson Imaging 32:993–1000. https://doi.org/10.1016/j.mri.2014.05.009

    Article  PubMed  Google Scholar 

  3. Kang SR, Kim HW, Kim HS (2020) Evaluating the relationship between dynamic contrast-enhanced MRI (DCE-MRI) parameters and pathological characteristics in breast cancer. J Magn Reson Imaging 52:1360–1373. https://doi.org/10.1002/jmri.27241

    Article  PubMed  Google Scholar 

  4. Cheng Q, Huang J, Liang J et al (2020) The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Front Oncol 10:93. https://doi.org/10.3389/fonc.2020.00093

    Article  PubMed  PubMed Central  Google Scholar 

  5. O’Connor JPB, Jackson A, Parker GJM et al (2012) Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol 9:167–177. https://doi.org/10.1038/nrclinonc.2012.2

    Article  CAS  PubMed  Google Scholar 

  6. Duan C, Kallehauge JF, Bretthorst GL et al (2017) Are complex DCE-MRI models supported by clinical data? Magn Reson Med 77:1329–1339. https://doi.org/10.1002/mrm.26189

    Article  PubMed  Google Scholar 

  7. Inglese M, Ordidge KL, Honeyfield L et al (2019) Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models. Neuroradiology 61:1375–1386. https://doi.org/10.1007/s00234-019-02265-2

    Article  PubMed  PubMed Central  Google Scholar 

  8. Khalifa F, Soliman A, El-Baz A et al (2014) Models and methods for analyzing DCE-MRI: a review. Med Phys 41:124301. https://doi.org/10.1118/1.4898202

    Article  PubMed  Google Scholar 

  9. Huang W, Chen Y, Fedorov A et al (2016) The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge. Tomography (Ann Arbor, Mich) 2:56–66. https://doi.org/10.18383/j.tom.2015.00184

    Article  PubMed  Google Scholar 

  10. Yang C, Karczmar GS, Medved M, Stadler WM (2004) Estimating the arterial input function using two reference tissues in dynamic contrast-enhanced MRI studies: fundamental concepts and simulations. Magn Reson Med 52:1110–1117. https://doi.org/10.1002/mrm.20243

    Article  PubMed  Google Scholar 

  11. Pickup S, Zhou R, Glickson J (2003) MRI estimation of the arterial input function in mice. Acad Radiol 10:963–968. https://doi.org/10.1016/s1076-6332(03)00291-5

    Article  PubMed  Google Scholar 

  12. Li X, Welch EB, Arlinghaus LR et al (2011) A novel AIF tracking method and a comparison of DCE-MRI parameters using individual and population based AIFs in human breast cancer. Phys Med Biol 56:5753–5769. https://doi.org/10.1088/0031-9155/56/17/018

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fluckiger JU, Schabel MC, DiBella EVR (2010) Toward local arterial input functions in dynamic contrast-enhanced MRI. J Magn Reson Imaging 32:924–934. https://doi.org/10.1002/jmri.22339

    Article  PubMed  Google Scholar 

  14. (2012) QIBA-RSNA, QIBA profile: DCE-MRI quantification (DCEMRI-Q),. https://qibawiki.rsna.org/index.php/Profiles. https://qibawiki.rsna.org/images/1/1f/QIBA_DCE-MRI_Profile-Stage_1-Public_Comment.pdf. Accessed 12 Jan 2023

  15. Cao J, Pickup S, Clendenin C et al (2019) Dynamic contrast-enhanced mri detects responses to stroma-directed therapy in mouse models of pancreatic ductal adenocarcinoma. Clin Cancer Res 25:2314–2322. https://doi.org/10.1158/1078-0432.CCR-18-2276

    Article  CAS  PubMed  Google Scholar 

  16. Jacobetz MA, Chan DS, Neesse A et al (2013) Hyaluronan impairs vascular function and drug delivery in a mouse model of pancreatic cancer. Gut 62:112–120. https://doi.org/10.1136/gutjnl-2012-302529

    Article  CAS  PubMed  Google Scholar 

  17. Provenzano PP, Cuevas C, Chang AE et al (2012) Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell 21:418–429. https://doi.org/10.1016/j.ccr.2012.01.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Provenzano PP, Hingorani SR (2013) Hyaluronan, fluid pressure, and stromal resistance in pancreas cancer. Br J Cancer 108:1–8. https://doi.org/10.1038/bjc.2012.569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Thompson CB, Shepard HM, O’Connor PM et al (2010) Enzymatic depletion of tumor hyaluronan induces antitumor responses in preclinical animal models. Mol Cancer Ther 9:3052–3064. https://doi.org/10.1158/1535-7163.Mct-10-0470

    Article  CAS  PubMed  Google Scholar 

  20. Zhou R, Pickup S, Yankeelov TE et al (2004) Simultaneous measurement of arterial input function and tumor pharmacokinetics in mice by dynamic contrast enhanced imaging: effects of transcytolemmal water exchange. Magn Reson Med 52:248–257. https://doi.org/10.1002/mrm.20143

    Article  PubMed  Google Scholar 

  21. Loveless ME, Halliday J, Liess C et al (2012) A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magn Reson Med 67:226–236. https://doi.org/10.1002/mrm.22988

    Article  PubMed  Google Scholar 

  22. Yankeelov TE, Luci JJ, Lepage M et al (2005) Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging 23:519–529. https://doi.org/10.1016/j.mri.2005.02.013

    Article  CAS  PubMed  Google Scholar 

  23. Yankeelov TE, Cron GO, Addison CL et al (2007) Comparison of a reference region model with direct measurement of an AIF in the analysis of DCE-MRI data. Magn Reson Med 57:353–361. https://doi.org/10.1002/mrm.21131

    Article  PubMed  Google Scholar 

  24. Tofts PS (1997) Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging: JMRI 7:91–101. https://doi.org/10.1002/jmri.1880070113

    Article  CAS  PubMed  Google Scholar 

  25. Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging: JMRI 10:223–232. https://doi.org/10.1002/(sici)1522-2586(199909)10:3<223::aid-jmri2>3.0.co;2-s

    Article  CAS  PubMed  Google Scholar 

  26. Yankeelov TE, Rooney WD, Li X, Springer CS (2003) Variation of the relaxographic “shutter-speed” for transcytolemmal water exchange affects the CR bolus-tracking curve shape. Magn Reson Med 50:1151–1169. https://doi.org/10.1002/mrm.10624

    Article  PubMed  Google Scholar 

  27. Li X, Rooney WD, Springer CS (2005) A unified magnetic resonance imaging pharmacokinetic theory: intravascular and extracellular contrast reagents. Magn Reson Med 54:1351–1359. https://doi.org/10.1002/mrm.20684

    Article  CAS  PubMed  Google Scholar 

  28. Huang W, Li X, Chen Y et al (2014) Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol 7:153–166. https://doi.org/10.1593/tlo.13838

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kim S, Quon H, Loevner LA et al (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 26:1607–1617. https://doi.org/10.1002/jmri.21207

    Article  PubMed  Google Scholar 

  30. Huang W, Li X, Morris EA et al (2008) The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo. Proc Natl Acad Sci U S A 105:17943–17948. https://doi.org/10.1073/pnas.0711226105

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gillis A, Gray M, Burstein D (2002) Relaxivity and diffusion of gadolinium agents in cartilage. Magn Reson Med 48:1068–1071. https://doi.org/10.1002/mrm.10327

    Article  CAS  PubMed  Google Scholar 

  32. Donahue KM, Burstein D, Manning WJ, Gray ML (1994) Studies of Gd-DTPA relaxivity and proton exchange rates in tissue. Magn Reson Med 32:66–76. https://doi.org/10.1002/mrm.1910320110

    Article  CAS  PubMed  Google Scholar 

  33. Li X, Cai Y, Moloney B et al (2016) Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling. J Magn Reson 269:104–112. https://doi.org/10.1016/j.jmr.2016.05.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sherman MH, Yu RT, Engle DD et al (2014) Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell 159:80–93. https://doi.org/10.1016/j.cell.2014.08.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chauhan VP, Martin JD, Liu H et al (2013) Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels. Nat Commun 4:2516. https://doi.org/10.1038/ncomms3516

    Article  CAS  PubMed  Google Scholar 

  36. Li X, Priest RA, Woodward WJ et al (2013) Feasibility of shutter-speed DCE-MRI for improved prostate cancer detection. Magn Reson Med 69:171–178. https://doi.org/10.1002/mrm.24211

    Article  CAS  PubMed  Google Scholar 

  37. Buckley DL (2019) Shutter-speed dynamic contrast-enhanced MRI: Is it fit for purpose? Magn Reson Med 81:976–988. https://doi.org/10.1002/mrm.27456

    Article  PubMed  Google Scholar 

  38. Bai R, Wang B, Jia Y et al (2020) Shutter-speed DCE-MRI analyses of human glioblastoma multiforme (GBM) data. J Magn Reson Imaging 52:850–863. https://doi.org/10.1002/jmri.27118

    Article  PubMed  Google Scholar 

  39. Chawla S, Loevner LA, Kim SG et al (2018) Dynamic contrast-enhanced MRI–derived intracellular water lifetime (τi): a prognostic marker for patients with head and neck squamous cell carcinomas. AJNR Am J Neuroradiol 39:138–144. https://doi.org/10.3174/ajnr.A5440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Springer CS (2018) Using 1H2O MR to measure and map sodium pump activity in vivo. J Magn Reson 291:110–126. https://doi.org/10.1016/j.jmr.2018.02.018

    Article  CAS  PubMed  Google Scholar 

  41. Springer CS, Li X, Tudorica LA et al (2014) Intratumor mapping of intracellular water lifetime: metabolic images of breast cancer? NMR in Biomedicine 27:760–773. https://doi.org/10.1002/nbm.3111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Romanello Joaquim M, Furth EE, Fan Y et al (2022) DWI metrics differentiating benign intraductal papillary mucinous neoplasms from invasive pancreatic cancer: a study in GEM models. Cancers 14:4017. https://doi.org/10.3390/cancers14164017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lin W, Guo J, Rosen MA, Song HK (2008) Respiratory motion-compensated radial dynamic contrast-enhanced (DCE)-MRI of chest and abdominal lesions. Magn Reson Med 60:1135–1146. https://doi.org/10.1002/mrm.21740

    Article  PubMed  PubMed Central  Google Scholar 

  44. Heacock L, Gao Y, Heller SL et al (2017) Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity. J Magn Reson Imaging 45:1746–1752. https://doi.org/10.1002/jmri.25530

    Article  PubMed  Google Scholar 

  45. Pickup S, Romanello M, Gupta M et al (2022) Dynamic contrast-enhanced MRI in the abdomen of mice with high temporal and spatial resolution using stack-of-stars sampling and KWIC reconstruction. Tomography 8:2113–2128. https://doi.org/10.3390/tomography8050178

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cárdenas-Rodríguez J, Howison CM, Pagel MD (2013) A linear algorithm of the reference region model for DCE-MRI is robust and relaxes requirements for temporal resolution. Magn Reson Imaging 31:497–507. https://doi.org/10.1016/j.mri.2012.10.008

    Article  PubMed  Google Scholar 

  47. Paudyal R, Poptani H, Cai K et al (2013) Impact of transvascular and cellular-interstitial water exchange on dynamic contrast-enhanced magnetic resonance imaging estimates of blood to tissue transfer constant and blood plasma volume. J Magn Reson Imaging 37:435–444. https://doi.org/10.1002/jmri.23837

    Article  PubMed  Google Scholar 

  48. Zhang J, Kim S (2014) Uncertainty in MR tracer kinetic parameters and water exchange rates estimated from T1-weighted dynamic contrast enhanced MRI. Magn Reson Med 72:534–545. https://doi.org/10.1002/mrm.24927

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank the support from the Small Animal Imaging Facility (SAIF) of the Department of Radiology at the University of Pennsylvania.

Data Availability Statement

Image data will be shared at a data repository built for Penn Quantitative Imaging Resource for Pancreatic Cancer (https://pennpancreaticcancerimagingresource. github.io/data.html/).

Funding

This project is supported by NIH U24CA231858 (Penn Quantitative Imaging Resource for Pancreatic Cancer) and P30-CA-016520-42(Abramson Cancer Center).

Author information

Authors and Affiliations

  1. Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Jianbo Cao, Stephen Pickup, Mark Rosen & Rong Zhou

  2. Current address: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK

    Jianbo Cao

  3. Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Mark Rosen & Rong Zhou

  4. Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Mark Rosen & Rong Zhou

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  1. Jianbo Cao
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Contributions

Conceptualization, RZ, J.C.; methodology development, J.C., S.P., and R.Z.; software, S.P.; data analysis, J.C., S.P., and R.Z.; data curation, J.C., S.P.; writing—original draft preparation RZ and J.C; writing review and editing, RZ, JC, MR, and S.P.; visualization, J.C., S.P., supervision, R.Z.; funding acquisition, R.Z. and MR. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Rong Zhou.

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Institutional Review Board Statement

The animal study protocol was approved by the Animal Care and Use Committee of the University of Pennsylvania (Protocol # 806083 approved on 03/12/2017).

Informed Consent Statement

Not applicable (no human study involved).

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The authors declare no competing interests.

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Cao, J., Pickup, S., Rosen, M. et al. Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer. Mol Imaging Biol 25, 638–647 (2023). https://doi.org/10.1007/s11307-023-01824-7

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