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Comparative Study of Dosimetry Methodology and Software Tools for Personalized Radionuclide Therapy

Madhulika Mehrotra

Abstract


Purpose: The aim of this review article is to collate the detailed insight of different dosimetry methodology and non-commercial /commercial dosimetry software tools, along with clinical study explored by specific authors, published in recent peer-review journals. The present work is segmented in three sections: i) Literature review of various dosimetry methodologies to evaluate absorbed dose in personalized radionuclide/ radiopharmaceutical therapy. ii) Technical as well as comparative information related to commercial dosimetry software tools used in radiopharmaceutical therapy (RPT). iii) Clinical review to compile the data of patient study for patient-specific dosimetry in internal radionuclide therapy.

Methods: Our study is based on latest available articles, to compile the information of upcoming dataset of newer methods to calculate absorbed dose, quantitative comparison of non-commercial / commercially available dosimetry software tools and recent study on patients who were clinically studied for targeted radionuclide therapy. To integrate the software based dosimetry tools in clinical routine; our department is planning to purchase few dosimetry software, henceforth a detailed survey is performed for recent articles published between 2018-2021 and other articles related to our work.

Results: The analysis of current review is categorized in three sections: i) Literature review for different calculation techniques for assessment of personalized internal radionuclide therapy, detail information of traditional and modern methods to calculate absorbed dose were gathered. With new updated dosimetry evaluation methods; more accurate, personalized and fast calculations are possible in clinical practice. ii) Technical review on different non-commercial / commercial software tools used in clinical routine, gives the first hand information of advantages and limitations of different software. The comparative study of different software is a step to achieve successes in performing the clinical practice for patient specific internal radionuclide therapy in our department. iii) Clinical review of the data, of patient study performed by various authors selected in our work gives the guideline to set the protocol to perform radionuclide therapy in clinical routine.

Conclusions: The objective of the present review is to compare the results generated by different non-commercial / commercial dosimetry software toolkits. The objectives of this work is not to provide the ranking or to recommend a given dosimetry methodology or software tools. However, encouraging results obtained in terms of absorbed doses were generally consistent between the different software toolkits. In absorbed dose calculations along with the harmonization process of different dosimetry methods and software tools, there are critical steps that should be deeply investigated on real cases based on voxel level or organ level calculations. The study provides the information of the most adequate computation technique and the methodology for the clinical or research application. Finally the outcome of the present study includes classification of various techniques mostly practiced in clinical routine, ranging from the less advance to personalized and the most accurate.


Keywords


Radiopharmaceutical Therapy (RPT), Personalized Radionuclide Therapy, Dosimetry software, Dosimetry methodology

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References


Li T, Ao EC, Lambert B, Brans B, Vandenberghe S, Mok GS. Quantitative imaging for targeted radionuclide therapy dosimetry-technical review. Theranostics. 2017;7(18): 4551.

Hamburg MA, Collins FS. The path to personalized medicine. New England Journal of Medicine. 2010;363(4):301-304.

Ljungberg M, Sjögreen GK. Personalized dosimetry for radionuclide therapy using molecular imaging tools. Biomedicines. 2016; 4(4): 25.

Bolch WE, Bouchet LG, Robertson JS, et al. MIRD pamphlet No. 17: the dosimetry of nonuniform activity distributions-radionuclide S values at the voxel level. Medical Internal Radiation Dose Committee. J Nucl Med.1999; 40: 11S-36S.

Besemer AE, Yang YM, Grudzinski JJ, Hall LT, Bednarz BP. Development and validation of RAPID: a patient-specific Monte Carlo three-dimensional internal dosimetry platform. Cancer Biother Radiopharm. 2018; 33: 155-65.

Ljungberg M, Gleisner KS. 3-D image-based dosimetry in radionuclide therapy. IEEE Trans Radiat Plasma Med Sci. 2018; 2: 527-40.

Marquis H, Deidda D, Gillman A. Theranostic SPECT reconstruction for improved resolution: application to radionuclide therapy dosimetry. EJNMMI physics. 2021; 8(1): 1-7.

Sarrut D, Bala M, Bardies M, et al. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Physics in Medicine & Biology. 2021.

Giammarile F, Muylle K, Delgado Bolton R, Kunikowska J, Haberkorn U, Oyen W. Dosimetry in clinical radionuclide therapy: the devil is in the detail. Eur J Nucl Med Mol Imaging. 2017; 44(12): 1–3.

Shiri I, Arabi H, Geramifar P, et al. Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network. European journal of nuclear medicine and molecular imaging. 2020; 47: 2533-2548.

Xiang H, Lim H, Fessler JA, Dewaraja YK. A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions. European journal of nuclear medicine and molecular imaging. 2020; 1-2.

Dong X, Lei Y, Wang T, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body PET imaging. Phys Med Biol. 2020; 65: 055011.

Sanaat A, Arabi H, Mainta I, Garibotto V, Zaidi H. Projection Space Implementation of Deep Learning–Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space. Journal of Nuclear Medicine. 2020; 61(9): 1388-96.

Zaharchuk G. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. Eur J Nucl Med Mol Imaging. 2019; 46: 2700–7.

Shiri I, AmirMozafari Sabet K, Arabi H, et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J Nucl Cardiol. 2020; 1-9.

Xie T, Zaidi H. Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks. Eur Radiol. 2019; 29: 6805–15.

Seo H, Badiei KhuzaniM, Vasudevan V, et al. Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med Phys. 2020; 47: e148–e67.

Yonekura Y, Mattsson S, Flux G, et al. ICRP Publication 140: Radiological Protection in Therapy with Radiopharmaceuticals. Ann ICRP. 2019; 48(1): 5–95.

Strosberg J, El-Haddad G, Wolin E, et al. Phase 3 Trial of 177Lu-Dotatate for Midgut Neuroendocrine Tumors. N Engl J Med. 2017; 376(2): 125–135.

Stabin MG, Madsen MT, Zaidi H. Personalized dosimetry is a must for appropriate molecular radiotherapy. Medical physics, 2019; 46(11): 4713-4716.

Bodei L, Mueller-Brand J, Pavel ME, et al. The joint IAEA, EANM, and SNMMI practical guidance on peptide receptor radionuclide therapy (PRRNT) in neuroendocrine tumours. Eur J Nucl Med Mol Imaging. 2013; 40(5): 800–16.

Sundlöv A, Sjögreen-Gleisner K, Svensson J, et al. Individualised 177Lu-DOTATATE treatment of neuroendocrine tumours based on kidney dosimetry. Eur J Nucl Med Mol Imaging. 2017; 44:1480–1489.

Gosewisch A, Delker A, Tattenberg S, et al. Patient-specific image-based bone marrow Octreotate inLu-177-DOTA, Try3-Octreotate and Lu-177-DKFZ-PSMA-617 therapy: investigation of a new hybridimage approach. EJNMMI Res. 2018; 8: 76.

Garske-Román U, Sandström M, Fröss BK, et al. Prospective observational study of177Lu-DOTA-octreotate therapy in 200 patients with advanced metastasized neuroendocrine tumours (NETs): feasibility and impact of a dosimetry-guided study protocol on outcome and toxicity. Eur J Nucl MedMol Imaging. 2018; 45: 970–988.

Svensson J, Rydén T, Hagmarker L, Hemmingsson J, Wängberg B, Bernhardt P. A novel planar image-based method for bone marrow dosimetry in 177Lu-DOTATATE treatment correlates with haematological toxicity. EJNMMI Phys. 2016; 3: 21.

Sanders JC, Kuwert T, Hornegger J, Ritt P. Quantitative SPECT/CT imaging of 177Lu with In vivo validation in patients undergoing peptide receptor radionuclide therapy. Mol Imaging Biol.2015; 17: 585–593.

Hänscheid H, Lapa C, Buck AK, Lassmann M, Werner RA. Dose mapping after endoradiotherapy with 177Lu-DOTATATE/-TOC by one single measurement after four days. J Nucl Med. 2018; 59: 75–81.

Bailey DL, Hennessy TM, Willowson KP, et al. In vivo quantification of 177Lu with planar whole-body and SPECT/CT gamma camera imaging. EJNMMI Phys. 2015; 2: 20.

Xie T, Bolch WE, Lee C, Zaidi H. Pediatric radiation dosimetry for positron-emitting radionuclides using anthropomorphic phantoms. Med Phys. 2013; 40: 102502.

Stabin MG, Siegel JA. Physical models and dose factors for use in internal dose assessment. Health Phys. 2003; 85: 294–310.

Xiao Y, Roncali E, Hobbs R, et al. Toward Individualized Voxel-Level Dosimetry for Radiopharmaceutical Therapy. International journal of radiation oncology, biology, physics. 2021; 109(4): 902-4.

Giap HB, Macey DJ, Bayouth JE, Boyer AL. Validation of a dose-point kernel convolution technique for internal dosimetry. Phys Med Biol. 1995; 40: 365-81.

Liu A, Williams LE, Wong JY, Raubitschek AA. Monte Carlo-assisted voxel source kernel method (MAVSK) for internal beta dosimetry. Nucl Med Biol. 1998; 25: 423-33.

Furhang EE, Chui CS, Sgouros G. A Monte Carlo approach to patient-specific dosimetry. Med Phys. 1996; 23: 1523-9.

Sundlöv A, Gustafsson J, Brolin G, et al. Feasibility of simplifying renal dosimetry in 177Lu peptide receptor radionuclide therapy. EJNMMI Phys [Internet]. 2018;5(1).

Marin G, Vanderlinden B, Karfis I, et al. A dosimetry procedure for organs-at-risk in 177Lu peptide receptor radionuclide therapy of patients with neuroendocrine tumours. Phys Med. 2018; 56: 41–9.

Del Prete M, Arsenault F, Saighi N, et al. Accuracy and reproducibility of simplified QSPECT dosimetry for personalized 177Lu-octreotate PRRT. EJNMMI Phys. 2018; 5(1): 25.

Grimes J, Uribe C, Celler A. JADA: a graphical user interface for comprehensive internal dose assessment in nuclear medicine. Med Phys. 2013; 40(7): 072501.

Johnson TK, McClure D, McCourt S. MABDOSE II: Validation of a general purpose dose estimation code. Med Phys. 1999; 26(7): 1396–403.

Kletting P, Schimmel S, Hänscheid H, et al. The NUKDOS software for treatment planning in molecular radiotherapy. Z Für Med Phys. 2015; 25(3): 264–74.

Li T, Zhu L, Lu Z, Song N, Lin K-H, Mok GSP. BIGDOSE: software for 3D personalized targeted radionuclide therapy dosimetry. Quant Imaging Med Surg. 2020;10(1):160–70.

Prideaux AR, Song H, Hobbs RF, et al. Three-dimensional radiobiologic dosimetry: Application of radiobiologic modeling to patient-specific 3-dimensional imaging–based internal dosimetry. J Nucl Med. 2007; 48: 1008-16.

Stabin MG, Sparks RB, Crowe E. OLINDA/EXM: the second-generation personal computer software for internal dose assessment in nuclear medicine. J Nucl Med. 2005; 46(6): 1023–7.

Stabin MG, Siegel JA. RADAR Dose Estimate Report: A Compendium of Radiopharmaceutical Dose Estimates Based on OLINDA/EXM Version 2.0. J Nucl Med. 2018; 59(1): 154–160.

Grassi E, Fioroni F, Ferri V, et al. Quantitative comparison between the commercial software STRATOS(®) by Philips and a homemade software for voxel-dosimetry in radiopeptide therapy. Phys Med. 2015; 31(1): 72–9.

Marcatili S, Pettinato C, Daniels S, et al. Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy. Phys Med Biol. 2013; 58(8): 2491–508.

Kost SD, Dewaraja YK, Abramson RG, Stabin MG. VIDA: a voxel-based dosimetry method for targeted radionuclide therapy using Geant4. Cancer Biother Radiopharm. 2015; 30: 16-26.

Hippeläinen ET, Tenhunen MJ, Maenpaa HO, Heikkonen JJ, Sohlberg AO. Dosimetry software Hermes Internal Radiation Dosimetry: from quantitative image reconstruction to voxel-level absorbed dose distribution. Nucl Med Commun. 2017; 38: 357-65.

PLANET® Dose [Software]. DOSIsoft SA. Available from: https://www.dosisoft.com/products/planet-dose/

Gupta A, Lee MS, Kim JH, Lee DS, Lee JS. Preclinical Voxel-Based Dosimetry in Theranostics: a Review. Nuclear medicine and molecular imaging, 2020; 54(2):86-97.

Bardiès M. Relevance and implementation of patient-specific dosimetry in targeted radionuclide therapy. In BIO Web of Conferences. 2019; 14: 07001.

Sapienza MT, Willegaignon J. Radionuclide therapy: current status and prospects for internal dosimetry in individualized therapeutic planning. Clinics, 2019; 74.

Ljungberg M, Gleisner KS. 3-D image-based dosimetry in radionuclide therapy. IEEE Transactions on Radiation and Plasma Medical Sciences, 2018; 2(6): 527-540.

Okamoto S, Shiga T, Tamaki N. Clinical Perspectives of Theranostics. Molecules. 2021; 26(8): 2232.

Stabin MG. Fundamentals of nuclear medicine dosimetry. Springer Science & Business Media; 2008; 9–31.

Snyder WS, Ford MR, Warner GG, Watson SB. “S,” Absorbed Dose per Unit Cumulated Activity for Selected Radionuclides and Organs. MIRD Pamphlet No 11. Soc Nucl Med. 1975.

Snyder WS, Ford MR, Warner GG. Estimates of Specific Absorbed Fractions for Photon Sources Uniformly Distributed in Various Organs of a Heterogeneous Phantom. MIRD Pamphlet No. 5, revised. Soc Nucl Med. 1978.

Bolch WE, Eckerman KF, Sgouros G, Thomas SR. MIRD pamphlet no. 21: a generalized schema for radiopharmaceutical dosimetry—standardization of nomenclature. J Nucl Med. 2009; 50: 477–84.

Grimes J, Celler A. Comparison of internal dose estimates obtained using organ-level, voxel S value, and Monte Carlo techniques. Med Phys. 2014; 41: 092501.

Siegel JA, Thomas SR, Stubbs JB, et al. MIRD pamphlet no. 16: techniques for quantitative radiopharmaceutical biodistribution data acquisition and analysis for use in human radiation dose estimates. J Nucl Med. 1999; 40: 37S–61S.

Del PM, Buteau FA, Beauregard JM. Personalized 177Lu-octreotate peptide receptor radionuclide therapy of neuroendocrine tumours: a simulation study. Eur J Nucl Med Mol Imaging. 2017; 44: 1490–500.

Jackson PA, Beauregard JM, Hofman MS, Kron T, Hogg A, Hicks RJ. An automated voxelized dosimetry tool for radionuclide therapy based on serial quantitative SPECT/CT imaging. Med Phys. 2013; 40: 112503.

Bailey DL, Hennessy TM, Willowson KP, et al. In vivo quantification of 177Lu with planar whole-body and SPECT/CT gamma camera imaging. EJNMMI Phys. 2015; 2: 20.

Dieudonné A, Hobbs RF, Bolch WE, Sgouros G, Gardin I. Fine-resolution voxel S values for constructing absorbed dose distributions at variable voxel size. Journal of nuclear medicine. 2010; 51(10): 1600-7.

Loudos G, Tsougos I, Boukis S, et al. A radionuclide dosimetry toolkit based on material-specific Monte Carlo dose kernels. Nucl Med Commun. 2009; 30: 504–12.

Dieudonné A, Hobbs RF, Lebtahi R, et al. Study of the impact of tissue density heterogeneities on 3-dimensional abdominal dosimetry: comparison between dose kernel convolution and direct Monte Carlo methods. J Nucl Med. 2012; 54: 236–44.

Hippeläinen E, Tenhunen M, Sohlberg A. Fast voxel-level dosimetry for 177Lu labelled peptide treatments. Phys Med Biol. 2015; 60: 6685–700.

Rogers DW. Low energy electron transport with EGS. Nucl. Instrum. Meth. Phys. Res. 1984; 227(3): 535-548.

Yoriyaz H, Stabin MG, dos Santos A. Monte Carlo MCNP-4B–based absorbed dose distribution estimates for patient-specific dosimetry. Journal of Nuclear Medicine. 2001; 42(4): 662-9.

Sempau J, Acosta E, Baro J, Fernández-Varea JM, Salvat F. An algorithm for Monte Carlo simulation of coupled electron-photon transport. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 1997; 132(3): 377-90.

Ferrer L, Chouin N, Bitar A, Lisbona A, Bardiès M. Implementing dosimetry in GATE: Dose-point kernel validation with GEANT4 4.8.1. Cancer biotherapy & radiopharmaceuticals. 2007; 22(1): 125-9.

Akhavanallaf A, Shiri I, Arabi H, Zaidi H. Whole-body voxel-based internal dosimetry using deep learning. European Journal of Nuclear Medicine and Molecular Imaging. 2021; 48(3): 670-82.

Papadimitroulas P, Erwin WD, Iliadou V, Kostou T, Loudos G, Kagadis GC. A personalized, Monte Carlo-based method for internal dosimetric evaluation of radiopharmaceuticals in children. Medical physics. 2018; 45(8): 3939-3949.

Grimes J, Celler A. Comparison of internal dose estimates obtained using organ-level, voxel S value, and Monte Carlo techniques. Medical physics. 2014; 41(9): 092501.

Huizing DMV, Verheij M, Stokkel MPM. Dosimetry methods and clinical applications in peptide receptor radionuclide therapy for neuroendocrine tumours: a literature review. EJNMMI research. 2018; 8(1): 1-11.

Finocchiaro D, Berenato S, Bertolini V, et al. Comparison of different calculation techniques for absorbed dose assessment in patient specific peptide receptor radionuclide therapy. Plos one. 2020; 15(8): e0236466.

Mora-Ramirez E, Santoro L, Cassol E, et al. Comparison of commercial dosimetric software platforms in patients treated with 177Lu-DOTATATE for peptide receptor radionuclide therapy. Medical Physics. 2020; 47(9): 4602-4615.

GE Healthcare. Dosimetry Toolkit Package: Organ Dose Estimates for Radio-Isotope Therapy Treatment Planning Purposes; 2011.

Huizing DM, Peters SM, Versleijen MW, et al. A head-to-head comparison between two commercial software packages for hybrid dosimetry after peptide receptor radionuclide therapy. EJNMMI physics. 2020; 7: 1-19.

Santoro L, Pitalot L, Trauchessec D, et al. Clinical implementation of PLANET® Dose for dosimetric assessment after [177 Lu] Lu-DOTA-TATE: comparison with Dosimetry Toolkit® and OLINDA/EXM® V1. 0. EJNMMI research. 2021; 11(1): 1-17.

The RADAR site [Internet]. Available from: http://www.doseinfo-radar.com/RADAROver.html

Stabin M, Farmer A. OLINDA/EXM 2.0: the new generation dosimetry modeling code. J NuclMed. 2012; 53: 585.

Gardin I, Fdhila M, Desbordes P, Smadja J, Lebtahi R, Dieudonne A. Predictive value of dosimetry indices for treatment response in liver cancer patients treated with yttrium 90 microspheres using a random forest algorithm.J Nucl Med. 2017; 58: 197.

Pasciak AS, Bourgeois AC, Bradley YC. A comparison of techniques for 90Y PET/CT image-based dosimetry following radioembolization with resin microspheres. Front Oncol. 2014; 4: 1–10.

Dieudonne A, Hobbs RF, Bolch WE, Sgouros G, Gardin I. Fine-resolution voxel S values for constructing absorbed dose distributions at variable voxel size. J Nucl Med. 2010; 51: 1600–1607.

Dieudonne A, Hobbs RF, Lebtahi R, et al. Study of the impact of tissue density heterogeneities on 3-dimensional abdominal dosimetry: comparison between dose kernel convolution and direct MonteCarlo methods. J Nucl Med. 2013; 54: 236–243.

MIM SurePlanTM MRT [Software]. MIM software Inc. Available from: https://www.mimsoftware.com/resources/brochures

Sarrut D, Halty A, Badel JN, Ferrer L, Bardiès M. Voxel-based multimodel fitting method for modeling time activity curves in SPECT images. Med Phys. 2017; 44: 6280–6288.

Mok G, Li T. High performance virtual CT for enhanced targeted radionuclide therapy dosimetry. J Nucl Med. 2017; 58: 1303-3.

Mok GS, Dewaraja YK. Recent advances in voxel-based targeted radionuclide therapy dosimetry. Quantitative Imaging in Medicine and Surgery. 2021; 11(2): 483.

Ljungberg M, Sjögreen-Gleisner K. The accuracy of absorbed dose estimates in tumours determined by quantitative SPECT: a Monte Carlo study. Acta Oncol. 2011; 50: 981–9.

Lanconelli N, Pacilio M, Meo S, Lo BF, Di Dia A, Torres Aroche L, et al. A free database of radionuclide voxel S values for the dosimetry of nonuniform activity distributions. Phys Med Biol. 2012; 57: 517–33.

Lin H, Jing J, Cai J, Xu L. A voxel-dose algorithm of heterogeneous activity distribution for Monte-Carlo simulation of radionuclide therapy dosimetry. Cancer Biother Radiopharm. 2012; 27: 344–52.

Sanchez-Garcia M, Gardin I, Lebtahi R, Dieudonné A. A new approach for dose calculation in targeted radionuclide therapy (TRT) based on collapsed cone superposition: validation with 90Y. Phys Med Biol. 2014; 59: 4769–84.

Jan S, Benoit D, Becheva E, et al. GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys Med Biol. 2011; 56: 881–901.

Papadimitroulas P, Loudos G, Nikiforidis GC, Kagadis GC. A dose point kernel database using GATE Monte Carlo simulation toolkit for nuclear medicine applications: comparison with other Monte Carlo codes. Med Phys. 2012; 39: 5238–5247.

Papadimitroulas P. Dosimetry applications in GATE Monte Carlo toolkit. Phys Med. 2017; 41: 136–140.

Agostinelli S, Allison J, Amako K, Apostolakis J, Araujo H, Arce P et al. Geant4: a simulation toolkit. Nucl. Instrum. Methods. 2003; 506(3): 250–303.

Zaker N, Kotasidis F, Garibotto V, Zaidi H. Assessment of lesion detectability in dynamic whole-body PET imaging using compartmental and Patlak parametric mapping. Clin Nucl Med. 2020; 45: e221–e31.

Fahrni G, Karakatsanis N, Di Domenicantonio G, Garibotto V, Zaidi H. Does whole-body Patlak 18F-FDG PET imaging improve lesion detectability in clinical oncology? Eur Radiol. 2019; 29: 4812–21.

Graves SA, Hobbs RF. Dosimetry for Optimized, Personalized Radiopharmaceutical Therapy. In Seminars in Radiation Oncology 2021; 31(1): 37-44.




DOI: https://doi.org/10.37591/rrjomst.v10i3.2716

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