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A Quantitative Digital Subtraction Angiography Technique for Characterizing Reduction in Hepatic Arterial Blood Flow During Transarterial Embolization

  • Laboratory Investigation
  • Embolisation (arterial)
  • Published:
CardioVascular and Interventional Radiology Aims and scope Submit manuscript

Abstract

Objective

There is no standardized and objective method for determining the optimal treatment endpoint (sub-stasis) during transarterial embolization. The objective of this study was to demonstrate the feasibility of using a quantitative digital subtraction angiography (qDSA) technique to characterize intra-procedural changes in hepatic arterial blood flow velocity in response to transarterial embolization in an in vivo porcine model.

Materials and Methods

Eight domestic swine underwent bland transarterial embolizations to partial- and sub-stasis angiographic endpoints with intraprocedural DSA acquisitions. Embolized lobes were assessed on histopathology for ischemic damage and tissue embolic particle density. Analysis of target vessels used qDSA and a commercially available color-coded DSA (ccDSA) tool to calculate blood flow velocities and time-to-peak, respectively.

Results

Blood flow velocities calculated using qDSA showed a statistically significant difference (p < 0.01) between partial- and sub-stasis endpoints, whereas time-to-peak calculated using ccDSA did not show a significant difference. During the course of embolizations, the average correlation with volume of particles delivered was larger for qDSA (− 0.86) than ccDSA (0.36). There was a statistically smaller mean squared error (p < 0.01) and larger coefficient of determination (p < 0.01) for qDSA compared to ccDSA. On pathology, the degree of embolization as calculated by qDSA had a moderate, positive correlation (p < 0.01) with the tissue embolic particle density of ischemic regions within the embolized lobe.

Conclusions

qDSA was able to quantitatively discriminate angiographic embolization endpoints and, compared to a commercially available ccDSA method, improve intra-procedural characterization of blood flow changes. Additionally, the qDSA endpoints correlated with tissue-level changes.

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References

  1. Geschwind J-FH, Ramsey DE, Cleffken B, et al. Transcatheter arterial chemoembolization of liver tumors: effects of embolization protocol on injectable volume of chemotherapy and subsequent arterial patency. Cardiovasc Interv Radiol. 2003;26(2):111–7.

    Article  Google Scholar 

  2. Rhee TK, Young JY, Larson AC, et al. Effect of transcatheter arterial embolization on levels of hypoxia-inducible factor-1α in rabbit VX2 liver tumors. J Vasc Interv Radiol. 2007;18(5):639–45.

    Article  Google Scholar 

  3. Shim J, Park J, Kim J, et al. Association between increment of serum VEGF level and prognosis after transcatheter arterial chemoembolization in hepatocellular carcinoma patients. Cancer Sci. 2008;99(10):2037–44.

    CAS  PubMed  Google Scholar 

  4. Kobayashi N, Ishii M, Ueno Y, et al. Co-expression of Bcl-2 protein and vascular endothelial growth factor in hepatocellular carcinomas treated by chemoembolization. Liver. 1999;19(1):25–31.

    Article  CAS  Google Scholar 

  5. Sergio A, Cristofori C, Cardin R, et al. Transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC): the role of angiogenesis and invasiveness. Am J Gastroenterol. 2008;103(4):ajg200850181.

    Article  Google Scholar 

  6. Jin B, Wang D, Lewandowski RJ, et al. Chemoembolization endpoints: effect on survival among patients with hepatocellular carcinoma. AJR Am J Roentgenol. 2011;196(4):919–28.

    Article  Google Scholar 

  7. Lewandowski RJ, Wang D, Gehl J, et al. A comparison of chemoembolization endpoints using angiographic versus transcatheter intraarterial perfusion/MR imaging monitoring. J Vasc Interv Radiol. 2007;18(10):1249–57.

    Article  Google Scholar 

  8. Gaba RC, Wang D, Lewandowski RJ, et al. Four-dimensional transcatheter intraarterial perfusion MR imaging for monitoring chemoembolization of hepatocellular carcinoma: preliminary results. J Vasc Interv Radiol. 2008;19(11):1589–95.

    Article  Google Scholar 

  9. Larson AC, Wang D, Atassi B, et al. Transcatheter intraarterial perfusion: MR monitoring of chemoembolization for hepatocellular carcinoma—feasibility of initial clinical translation. Radiology. 2008;246(3):964–71.

    Article  Google Scholar 

  10. Lin EY, Lee R-C, Guo W-Y, et al. Three-dimensional quantitative color-coding analysis of hepatic arterial flow change during chemoembolization of hepatocellular carcinoma. J Vasc Interv Radiol. 2018;29:1362–8.

    Article  Google Scholar 

  11. Ionita CN, Garcia VL, Bednarek DR, et al. Effect of injection technique on temporal parametric imaging derived from digital subtraction angiography in patient specific phantoms. Proc SPIE Int Soc Opt Eng. 2014;13(9038):90380L.

    Google Scholar 

  12. Shpilfoygel SD, Close RA, Valentino DJ, et al. X-ray videodensitometric methods for blood flow and velocity measurement: a critical review of literature. Med Phys. 2000;27:2008–23.

    Article  CAS  Google Scholar 

  13. Kennedy AS, Kleinstreuer C, Basciano CA, et al. Computer modeling of yttrium-90—microsphere transport in the hepatic arterial tree to improve clinical outcomes. Int J Radiat Oncol Biol Phys. 2010;76:631–7.

    Article  CAS  Google Scholar 

  14. Wu Y, Shaughnessy G, Hoffman CA, et al. Quantification of blood velocity with 4D digital subtraction angiography using the shifted least-squares method. Am J Neuroradiol. 2018;39(10):1871–7.

    Article  CAS  Google Scholar 

  15. Bates D, Machler M, Bolker B, et al. Fitting linear mixed-effects models using lme4. J Stat Softw 2015;67(1). https://doi.org/10.18637/jss.v067.i01.

  16. Dunn OJ, Clark V. Correlation coefficients measured on the same individuals. J Am Stat Assoc. 1969;64:366–77.

    Article  Google Scholar 

  17. R Core Team. R A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2019.

    Google Scholar 

  18. Chen C-W, Hsu L-S, Weng J-C, et al. Assessment of small hepatocellular carcinoma: perfusion quantification and time-concentration curve evaluation using color-coded and quantitative digital subtraction angiography. Medicine. 2018;97:e13392.

    Article  Google Scholar 

  19. Lin Y-Y, Lee R-C, Guo W-Y, et al. Quantitative real-time fluoroscopy analysis on measurement of the hepatic arterial flow during transcatheter arterial chemoembolization of hepatocellular carcinoma: comparison with quantitative digital subtraction angiography analysis. Cardio Vasc Interv Radiol. 2016;39:1557–63.

    Article  Google Scholar 

  20. Meram E, Harari C, Shaughnessy G, et al. Quantitative 4D-digital subtraction angiography to assess changes in hepatic arterial flow during transarterial embolization: a feasibility study in a swine model. J Vasc Interv Radiol. 2019;30(8):1286–92.

    Article  Google Scholar 

  21. Meram E, Shaughnessy G, Longhurst C, et al. Optimization of quantitative 4D-digital subtraction angiography in a porcine liver model. Eur Radiol Exp. 2020;4:1–10.

    Article  Google Scholar 

  22. Ghibes P, Hefferman G, Nikolaou K, et al. Quantitative evaluation of peripheral arterial blood flow using peri-interventional fluoroscopic parameters: an in vivo study evaluating feasibility and clinical utility. Biomed Res Int. 2020;2020:9526790.

    Article  Google Scholar 

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Acknowledgements

Sarvesh Periyasamy is supported by an MD-Ph.D Graduate Fellowship through the University of Wisconsin School of Medicine and Public Health, Department of Radiology, an NCI Ruth L. Kirschstein NRSA Fellowship 1F30CA250408-01, and an MSTP NIH Grant T32GM008692. Carson Hoffman is supported by the Radiological Sciences Training Grant (NIH Grant T32CA009206 through the National Cancer Institute). Portions of the image processing were performed on a GPU donated by the NVIDIA Corporation.

Funding

This study was not supported by any funding.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Wisconsin – Madison, 1310-C WIMR, 1111 Highland Avenue, Madison, WI, 53705, USA

    Sarvesh Periyasamy

  2. Department of Medical Physics, University of Wisconsin – Madison, Madison, WI, USA

    Carson A. Hoffman & Michael A. Speidel

  3. Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison, Madison, WI, USA

    Colin Longhurst

  4. Department of Radiology, University of Wisconsin – Madison, Madison, WI, USA

    Sarvesh Periyasamy, Georgia C. Schefelker, Orhan S. Ozkan & Paul F. Laeseke

Authors
  1. Sarvesh Periyasamy
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  2. Carson A. Hoffman
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  4. Georgia C. Schefelker
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  6. Michael A. Speidel
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  7. Paul F. Laeseke
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Corresponding author

Correspondence to Sarvesh Periyasamy.

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Conflict of interest

Author Michael A. Speidel received a grant from Siemens Healthineers. Author Paul F. Laeseke is a consultant for Neuwave Medical/Ethicon, a consultant and shareholder for Elucent Medical, a shareholder for Histosonics, and a shareholder for McGinley Orthopeadic Innovations. For the remaining authors, no other conflicts were declared.

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.

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Periyasamy, S., Hoffman, C.A., Longhurst, C. et al. A Quantitative Digital Subtraction Angiography Technique for Characterizing Reduction in Hepatic Arterial Blood Flow During Transarterial Embolization. Cardiovasc Intervent Radiol 44, 310–317 (2021). https://doi.org/10.1007/s00270-020-02640-0

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  • DOI: https://doi.org/10.1007/s00270-020-02640-0

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