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MIDOS: a novel stochastic model towards a treatment planning system for microsphere dosimetry in liver tumors

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Transarterial radioembolization (TARE) procedures treat liver tumors by injecting radioactive microspheres into the hepatic artery. Currently, there is a critical need to optimize TARE towards a personalized dosimetry approach. To this aim, we present a novel microsphere dosimetry (MIDOS) stochastic model to estimate the activity delivered to the tumor(s), normal liver, and lung.

Methods

MIDOS incorporates adult male/female liver computational phantoms with the hepatic arterial, hepatic portal venous, and hepatic venous vascular trees. Tumors can be placed in both models at user discretion. The perfusion of microspheres follows cluster patterns, and a Markov chain approach was applied to microsphere navigation, with the terminal location of microspheres determined to be in either normal hepatic parenchyma, hepatic tumor, or lung. A tumor uptake model was implemented to determine if microspheres get lodged in the tumor, and a probability was included in determining the shunt of microspheres to the lung. A sensitivity analysis of the model parameters was performed, and radiation segmentectomy/lobectomy procedures were simulated over a wide range of activity perfused. Then, the impact of using different microspheres, i.e., SIR-Sphere®, TheraSphere®, and QuiremSphere®, on the tumor-to-normal ratio (TNR), lung shunt fraction (LSF), and mean absorbed dose was analyzed.

Results

Highly vascularized tumors translated into increased TNR. Treatment results (TNR and LSF) were significantly more variable for microspheres with high particle load. In our scenarios with 1.5 GBq perfusion, TNR was maximum for TheraSphere® at calibration time in segmentectomy/lobar technique, for SIR-Sphere® at 1–3 days post-calibration, and regarding QuiremSphere® at 3 days post-calibration.

Conclusion

This novel approach is a decisive step towards develo** a personalized dosimetry framework for TARE. MIDOS assists in making clinical decisions in TARE treatment planning by assessing various delivery parameters and simulating different tumor uptakes. MIDOS offers evaluation of treatment outcomes, such as TNR and LSF, and quantitative scenario-specific decisions.

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Data Availability

The model employed and data utilized to show results in this paper are available upon reasonable request from the corresponding author, subject to privacy/ethical considerations.

References

  1. Bastiaannet R, Kappadath SC, Kunnen B, Braat AJAT, Lam MGEH, de Jong HWAM. The physics of radioembolization. EJNMMI Phys. 2018;5:22.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Kim SP, Cohalan C, Kopek N, Enger SA. A guide to 90Y radioembolization and its dosimetry. Phys Med. 2019;68:132–45.

    Article  PubMed  Google Scholar 

  3. Gulec SA, McGoron AJ. Radiomicrosphere dosimetry: principles and current state of the art. Semin Nucl Med. 2022;52:215–28.

    Article  PubMed  Google Scholar 

  4. Knight GM, Gordon AC, Gates V, Talwar A, Riaz A, Salem R, et al. Evolution of personalized dosimetry for radioembolization of hepatocellular carcinoma. J Vasc Interv Radiol. 2023;34:1214–25.

    Article  PubMed  Google Scholar 

  5. Kennedy AS, Coldwell D, Nutting C, Murthy R, Wertman DE, Loehr SP, et al. Resin 90Y-microsphere brachytherapy for unresectable colorectal liver metastases: modern USA experience. Int J Radiat Oncol Biol Phys. 2006;65:412–25.

    Article  CAS  PubMed  Google Scholar 

  6. Malhotra A, Liu DM, Talenfeld AD. Radiation segmentectomy and radiation lobectomy: a practical review of techniques. Tech Vasc Interv Radiol. 2019;22:49–57.

    Article  PubMed  Google Scholar 

  7. Westcott MA, Coldwell DM, Liu DM, Zikria JF. The development, commercialization, and clinical context of yttrium-90 radiolabeled resin and glass microspheres. Adv Radiat Oncol. 2016;1:351–64.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Weber M, Lam M, Chiesa C, Konijnenberg M, Cremonesi M, Flamen P, et al. EANM procedure guideline for the treatment of liver cancer and liver metastases with intra-arterial radioactive compounds. Eur J Nucl Med Mol Imaging. 2022;49:1682–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Reinders MTM, Smits MLJ, van Roekel C, Braat AJAT. Holmium-166 microsphere radioembolization of hepatic malignancies. Semin Nucl Med. 2019;49:237–43.

    Article  PubMed  Google Scholar 

  10. Ilhan H, Goritschan A, Paprottka P, Jakobs TF, Fendler WP, Todica A, et al. Predictive value of 99mTc-MAA SPECT for 90Y-labeled resin microsphere distribution in radioembolization of primary and secondary hepatic tumors. J Nucl Med. 2015;56:1654–60.

    Article  CAS  PubMed  Google Scholar 

  11. Haste P, Tann M, Persohn S, LaRoche T, Aaron V, Mauxion T, et al. Correlation of technetium-99m macroaggregated albumin and yttrium-90 glass microsphere biodistribution in hepatocellular carcinoma: a retrospective review of pretreatment single photon emission CT and posttreatment positron emission tomography/CT. J Vasc Interv Radiol. 2017;28:722-730.e1.

    Article  PubMed  Google Scholar 

  12. Bertolet A, Wehrenberg-Klee E, Bobic M, Grassberger C, Perl J, Paganetti H, et al. Pre- and post-treatment image-based dosimetry in 90Y-microsphere radioembolization using the TOPAS Monte Carlo toolkit. Phys Med Biol. 2021;66:244002.

    Article  CAS  Google Scholar 

  13. Villalobos A, Cheng B, Wagstaff W, Sethi I, Bercu Z, Schuster DM, et al. Tumor-to-normal ratio relationship between planning technetium-99 macroaggregated albumin and posttherapy yttrium-90 bremsstrahlung SPECT/CT. J Vasc Interv Radiol. 2021;32:752–60.

    Article  PubMed  Google Scholar 

  14. Young S, Chen T, Flanagan S, Golzarian J, Sanghvi T. Realized tumor to normal ratios in hepatocellular carcinoma patients undergoing transarterial radioembolization: a retrospective evaluation. Eur Radiol. 2022;32:4160–7.

    Article  CAS  PubMed  Google Scholar 

  15. Villalobos A, Arndt L, Cheng B, Dabbous H, Loya M, Majdalany B, et al. Yttrium-90 radiation segmentectomy of hepatocellular carcinoma: a comparative study of the effectiveness, safety, and dosimetry of glass vs. resin-based microspheres. J Vasc Interv Radiol. 2023;34:1226–34.

    Article  PubMed  Google Scholar 

  16. Pasciak AS, Abiola G, Liddell RP, Crookston N, Besharati S, Donahue D, et al. The number of microspheres in Y90 radioembolization directly affects normal tissue radiation exposure. Eur J Nucl Med Mol Imaging. 2020;47:816–27.

    Article  CAS  PubMed  Google Scholar 

  17. Maxwell AWP, Mendoza HG, Sellitti MJ, Camacho JC, Deipolyi AR, Ziv E, et al. Optimizing 90Y particle density improves outcomes after radioembolization. Cardiovasc Intervent Radiol. 2022;45:958–69.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Boas FE, Maxwell AWP. Beyond mean tumor dose: the importance of particle density in radioembolization. J Vasc Interv Radiol. 2023;34:1235–6.

    Article  PubMed  Google Scholar 

  19. Aramburu J, Antón R, Rodríguez-Fraile M, Sangro B, Bilbao JI. Computational fluid dynamics modeling of liver radioembolization: a review. Cardiovasc Intervent Radiol Springer. 2022;45:12–20.

    Article  Google Scholar 

  20. Toskich B, Lewandowski RJ. Computational modeling of radioembolization: how to calculate infinity. Cardiovasc Intervent Radiol. Springer; 2021. pp. 2020–1.

  21. Miller SR, Jernigan SR, Abraham RJ, Buckner GD. Comparison of bolus versus dual-syringe administration systems on glass yttrium-90 microsphere deposition in an in vitro microvascular hepatic tumor model. J Vasc Interv Radiol. 2023;34:11–20.

    Article  PubMed  Google Scholar 

  22. Crookston NR, Fung GSK, Frey EC. Development of a customizable hepatic arterial tree and particle transport model for use in treatment planning. IEEE Trans Radiat Plasma Med Sci. 2019;3:31–7.

    Article  PubMed  Google Scholar 

  23. Walrand S, Hesse M, Chiesa C, Lhommel R, Jamar F. The low hepatic toxicity per gray of 90Y glass microspheres is linked to their transport in the arterial tree favoring a nonuniform trap** as observed in posttherapy PET imaging. J Nucl Med. 2014;55:135–40.

    Article  CAS  PubMed  Google Scholar 

  24. Högberg J, Rizell M, Hultborn R, Svensson J, Henrikson O, Mölne J, et al. Simulation model of microsphere distribution for selective internal radiation therapy agrees with observations. Int J Radiat Oncol Biol Phys. 2016;96:414–21.

    Article  PubMed  Google Scholar 

  25. Pasciak AS, Bourgeois AC, Bradley YC. A microdosimetric analysis of absorbed dose to tumor as a function of number of microspheres per unit volume in 90Y Radioembolization. J Nucl Med. 2016;57:1020–6.

    Article  CAS  PubMed  Google Scholar 

  26. Correa-Alfonso CM, Withrow JD, Domal SJ, **ng S, Shin J, Grassberger C, et al. A mesh-based model of liver vasculature: implications for improved radiation dosimetry to liver parenchyma for radiopharmaceuticals. EJNMMI Phys. 2022;9:28.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lewandowski RJ, Minocha J, Memon K, Riaz A, Gates VL, Ryu RK, et al. Sustained safety and efficacy of extended-shelf-life 90Y glass microspheres: long-term follow-up in a 134-patient cohort. Eur J Nucl Med Mol Imaging. 2014;41:486–93.

    Article  CAS  PubMed  Google Scholar 

  28. Gulec SA, Mesoloras G, Stabin M. Dosimetric techniques in 90 Y-microsphere therapy of liver cancer: the MIRD equations for dose calculations. J Nucl Med. 2006;47.

  29. Campbell AM, Bailey IH, Burton MA. Analysis of the distribution of intra-arterial microspheres in human liver following hepatic yttrium-90 microsphere therapy. Phys Med Biol. 2000;45:1023–33.

    Article  CAS  PubMed  Google Scholar 

  30. Högberg J, Rizell M, Hultborn R, Svensson J, Henrikson O, Mölne J, et al. Heterogeneity of microsphere distribution in resected liver and tumour tissue following selective intrahepatic radiotherapy. EJNMMI Res. 2014;4.

  31. Högberg J, Rizell M, Hultborn R, Svensson J, Henrikson O, Mölne J, et al. Increased absorbed liver dose in selective internal radiation therapy (SIRT) correlates with increased sphere-cluster frequency and absorbed dose inhomogeneity. EJNMMI Phys. 2015;2:1–17.

    Article  Google Scholar 

  32. Kennedy AS, Nutting C, Coldwell D, Gaiser J, Drachenberg C. Pathologic response and microdosimetry of 90Y microspheres in man: review of four explanted whole livers. Int J Radiat Oncol Biol Phys. 2004;60:1552–63.

    Article  CAS  PubMed  Google Scholar 

  33. Schiro BJ, Amour ES, Harnain C, Gandhi RT. Management of high hepatopulmonary shunts in the setting of Y90 radioembolization. Tech Vasc Interv Radiol. 2019;22:58–62.

    Article  PubMed  Google Scholar 

  34. Riaz A, Awais R, Salem R. Side effects of yttrium-90 radioembolization. Front Oncol. 2014;4.

  35. Van Roekel C, Van Den Hoven AF, Bastiaannet R, Bruijnen RCG, Braat AJAT, De Keizer B, et al. Use of an anti-reflux catheter to improve tumor targeting for holmium-166 radioembolization-a prospective, within-patient randomized study. Eur J Nucl Med Mol Imaging. 2021;48:1658–68.

    Article  PubMed  Google Scholar 

  36. Reinders MTM, van Erpecum KJ, Smits MLJ, Braat AJAT, de Bruijne J, Bruijnen R, et al. Safety and efficacy of 166Ho radioembolization in hepatocellular carcinoma: the HEPAR Primary Study. J Nucl Med. 2022;63:1891–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. van Roekel C, Harlianto NI, Braat AJAT, Prince JF, van den Hoven AF, Bruijnen RCG, et al. Evaluation of the safety and feasibility of same-day holmium-166-radioembolization simulation and treatment of hepatic metastases. J Vasc Interv Radiol. 2020;31:1593–9.

    Article  PubMed  Google Scholar 

  38. Aramburu J, Antón R, Rivas A, Ramos JC, Sangro B, Bilbao JI. Computational assessment of the effects of the catheter type on particle–hemodynamics during liver radioembolization. J Biomech. 2016;49:3705–13.

    Article  PubMed  Google Scholar 

  39. Lertxundi U, Aramburu J, Rodríguez-Fraile M, Sangro B, Antón R. Computational study of the microsphere concentration in blood during radioembolization. Mathematics. 2022;10:4280.

    Article  Google Scholar 

  40. Aramburu J, Antón R, Rivas A, Ramos JC, Sangro B, Bilbao JI. The role of angled-tip microcatheter and microsphere injection velocity in liver radioembolization: a computational particle–hemodynamics study. Int J Numer Method Biomed Eng. 2017;33:e2895.

    Article  Google Scholar 

  41. Taebi A, Janibek N, Goldman R, Pillai R, Vu CT, Roncali E. The impact of injection distance to bifurcations on yttrium-90 distribution in liver cancer radioembolization. J Vasc Interv Radiol. 2022;33:668–77.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bomberna T, Koudehi GA, Claerebout C, Verslype C, Maleux G, Debbaut C. Transarterial drug delivery for liver cancer: numerical simulations and experimental validation of particle distribution in patient-specific livers. Expert Opin Drug Deliv. 2021;18:409–22.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Camilo M. Correa-Alfonso for his helpful comments during the conception and development of this project.

Funding

This work was supported by the Loeffler Team Science Seed Funding Program granted by the Department of Radiation Oncology of the Massachusetts General Hospital.

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Authors and Affiliations

  1. Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Carlos Huesa-Berral, Chris Beekman, Harald Paganetti & Alejandro Bertolet

  2. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Julia D. Withrow, Robert J. Dawson & Wesley E. Bolch

  3. Division of Interventional Radiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Eric Wehrenberg-Klee

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  1. Carlos Huesa-Berral
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Contributions

C.H.-B., E.W.-K., and A.B. were equally involved in the conceptualization and design of the study. J.D.W., R.J.D., C.B., H.P., and W.B. were responsible for the development of adult liver computational phantoms. C.H.-B. developed the stochastic model for liver radioembolization, under the supervision of E.W.-K. and A.B. All authors were involved in data analysis and interpretation. C.H.-B. wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript.

Corresponding authors

Correspondence to Carlos Huesa-Berral or Alejandro Bertolet.

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Competing interests

Eric Wehrenberg-Klee reports consulting fees from SIRTEX, Embolx, Avenge Biosciences, and Cytosite Bio. He has served on advisory boards for Delcath and Eisai. He is an IDMC member for Replimune. He receives clinical research funding from Boston Scientific. He is grant funded by NCI K08-245257.

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Huesa-Berral, C., Withrow, J.D., Dawson, R.J. et al. MIDOS: a novel stochastic model towards a treatment planning system for microsphere dosimetry in liver tumors. Eur J Nucl Med Mol Imaging 51, 1506–1515 (2024). https://doi.org/10.1007/s00259-023-06567-9

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