Patient-specific dosimetry adapted to variable number of SPECT/CT time-points per cycle for [Formula: see text]Lu-DOTATATE therapy.

Dosimetry Internal radiotherapy Lu-DOTATATE Monte Carlo simulation Patient-specific SPECT/CT Single acquisition

Journal

EJNMMI physics
ISSN: 2197-7364
Titre abrégé: EJNMMI Phys
Pays: Germany
ID NLM: 101658952

Informations de publication

Date de publication:
16 May 2022
Historique:
received: 17 12 2021
accepted: 20 04 2022
entrez: 16 5 2022
pubmed: 17 5 2022
medline: 17 5 2022
Statut: epublish

Résumé

The number of SPECT/CT time-points is important for accurate patient dose estimation in peptide receptor radionuclide therapy. However, it may be limited by the patient's health and logistical reasons. Here,  an image-based dosimetric workflow adapted to the number of SPECT/CT acquisitions available throughout the treatment cycles was proposed, taking into account patient-specific pharmacokinetics and usable in clinic for all organs at risk. Thirteen patients with neuroendocrine tumors were treated with four injections of 7.4 GBq of [Formula: see text]Lu-DOTATATE. Three SPECT/CT images were acquired during the first cycle (1H, 24H and 96H or 144H post-injection) and a single acquisition (24H) for following cycles. Absorbed doses were estimated for kidneys (LK and RK), liver (L), spleen (S), and three surrogates of bone marrow (L2 to L4, L1 to L5 and T9 to L5) that were compared. 3D dose rate distributions were computed with Monte Carlo simulations. Voxel dose rates were averaged at the organ level. The obtained Time Dose-Rate Curves (TDRC) were fitted with a tri-exponential model and time-integrated. This method modeled patient-specific uptake and clearance phases observed at cycle 1. Obtained fitting parameters were reused for the following cycles, scaled to the measure organ dose rate at 24H. An alternative methodology was proposed when some acquisitions were missing based on population average TDRC (named STP-Inter). Seven other patients with three SPECT/CT acquisitions at cycles 1 and 4 were included to estimate the uncertainty of the proposed methods. Absorbed doses (in Gy) per cycle available were: 3.1 ± 1.1 (LK), 3.4 ± 1.5 (RK), 4.5 ± 2.8 (L), 4.6 ± 1.8 (S), 0.3 ± 0.2 (bone marrow). There was a significant difference between bone marrow surrogates (L2 to L4 and L1 to L5, Wilcoxon's test: p value < 0.05), and while depicting very doses, all three surrogates were significantly different than dose in background (p value < 0.01). At cycle 1, if the acquisition at 24H is missing and approximated, medians of percentages of dose difference (PDD) compared to the initial tri-exponential function were inferior to 3.3% for all organs. For cycles with one acquisition, the median errors were smaller with a late time-point. For STP-Inter, medians of PDD were inferior to 7.7% for all volumes, but it was shown to depend on the homogeneity of TDRC. The proposed workflow allows the estimation of organ doses, including bone marrow, from a variable number of time-points acquisitions for patients treated with [Formula: see text]Lu-DOTATATE.

Sections du résumé

BACKGROUND BACKGROUND
The number of SPECT/CT time-points is important for accurate patient dose estimation in peptide receptor radionuclide therapy. However, it may be limited by the patient's health and logistical reasons. Here,  an image-based dosimetric workflow adapted to the number of SPECT/CT acquisitions available throughout the treatment cycles was proposed, taking into account patient-specific pharmacokinetics and usable in clinic for all organs at risk.
METHODS METHODS
Thirteen patients with neuroendocrine tumors were treated with four injections of 7.4 GBq of [Formula: see text]Lu-DOTATATE. Three SPECT/CT images were acquired during the first cycle (1H, 24H and 96H or 144H post-injection) and a single acquisition (24H) for following cycles. Absorbed doses were estimated for kidneys (LK and RK), liver (L), spleen (S), and three surrogates of bone marrow (L2 to L4, L1 to L5 and T9 to L5) that were compared. 3D dose rate distributions were computed with Monte Carlo simulations. Voxel dose rates were averaged at the organ level. The obtained Time Dose-Rate Curves (TDRC) were fitted with a tri-exponential model and time-integrated. This method modeled patient-specific uptake and clearance phases observed at cycle 1. Obtained fitting parameters were reused for the following cycles, scaled to the measure organ dose rate at 24H. An alternative methodology was proposed when some acquisitions were missing based on population average TDRC (named STP-Inter). Seven other patients with three SPECT/CT acquisitions at cycles 1 and 4 were included to estimate the uncertainty of the proposed methods.
RESULTS RESULTS
Absorbed doses (in Gy) per cycle available were: 3.1 ± 1.1 (LK), 3.4 ± 1.5 (RK), 4.5 ± 2.8 (L), 4.6 ± 1.8 (S), 0.3 ± 0.2 (bone marrow). There was a significant difference between bone marrow surrogates (L2 to L4 and L1 to L5, Wilcoxon's test: p value < 0.05), and while depicting very doses, all three surrogates were significantly different than dose in background (p value < 0.01). At cycle 1, if the acquisition at 24H is missing and approximated, medians of percentages of dose difference (PDD) compared to the initial tri-exponential function were inferior to 3.3% for all organs. For cycles with one acquisition, the median errors were smaller with a late time-point. For STP-Inter, medians of PDD were inferior to 7.7% for all volumes, but it was shown to depend on the homogeneity of TDRC.
CONCLUSION CONCLUSIONS
The proposed workflow allows the estimation of organ doses, including bone marrow, from a variable number of time-points acquisitions for patients treated with [Formula: see text]Lu-DOTATATE.

Identifiants

pubmed: 35575946
doi: 10.1186/s40658-022-00462-2
pii: 10.1186/s40658-022-00462-2
pmc: PMC9110613
doi:

Types de publication

Journal Article

Langues

eng

Pagination

37

Informations de copyright

© 2022. The Author(s).

Références

Eur J Nucl Med Mol Imaging. 2017 Aug;44(9):1480-1489
pubmed: 28331954
Biomedicines. 2016 Nov 15;4(4):
pubmed: 28536392
Med Phys. 2018 May;45(5):2318-2324
pubmed: 29577338
Acta Oncol. 2012 Jan;51(1):86-96
pubmed: 21961497
Eur J Nucl Med Mol Imaging. 2010 Feb;37(2):212-25
pubmed: 19727718
EJNMMI Phys. 2017 Dec;4(1):6
pubmed: 28101733
Phys Med Biol. 2020 Dec 02;65(23):235015
pubmed: 32992308
EJNMMI Res. 2018 Nov 29;8(1):103
pubmed: 30498938
J Nucl Med. 2020 Jul;61(7):1030-1036
pubmed: 31806772
Acta Oncol. 2018 Apr;57(4):516-521
pubmed: 28920501
EJNMMI Phys. 2020 Jan 23;7(1):5
pubmed: 31975156
PLoS One. 2020 Aug 7;15(8):e0236466
pubmed: 32764764
Phys Med Biol. 2019 Aug 28;64(17):175006
pubmed: 31287093
J Nucl Med. 2013 Jan;54(1):33-41
pubmed: 23223392
J Nucl Med. 2018 Jan;59(1):75-81
pubmed: 28588150
EJNMMI Phys. 2018 Dec 20;5(1):33
pubmed: 30569328
J Nucl Med. 2020 Jul;61(7):1028-1029
pubmed: 31924721
Phys Med Biol. 2013 Mar 7;58(5):1303-14
pubmed: 23388109
EJNMMI Radiopharm Chem. 2019 Jul 15;4(1):13
pubmed: 31659496
J Nucl Med. 1993 Apr;34(4):689-94
pubmed: 8455089
Q J Nucl Med Mol Imaging. 2011 Feb;55(1):5-20
pubmed: 21386782
Eur J Nucl Med Mol Imaging. 2017 Aug;44(9):1490-1500
pubmed: 28361189
Eur J Nucl Med Mol Imaging. 2014 Oct;41(10):1976-88
pubmed: 24915892
J Nucl Med. 2016 Jan;57(1):151-62
pubmed: 26471692
EJNMMI Phys. 2020 Dec 9;7(1):73
pubmed: 33296054
Cancer. 2010 Feb 15;116(4 Suppl):1093-100
pubmed: 20127958
Med Phys. 2014 Jun;41(6):064301
pubmed: 24877844
Eur J Nucl Med Mol Imaging. 2009 Jul;36(7):1138-46
pubmed: 19247653
Med Phys. 2014 Sep;41(9):092501
pubmed: 25186410
J Nucl Med. 2015 Feb;56(2):177-82
pubmed: 25593115
J Nucl Med. 2012 Aug;53(8):1310-25
pubmed: 22743252
EJNMMI Phys. 2020 May 15;7(1):32
pubmed: 32415492
N Engl J Med. 2017 Jan 12;376(2):125-135
pubmed: 28076709
Eur J Nucl Med Mol Imaging. 2010 Jun;37(6):1238-50
pubmed: 20411259
J Nucl Med. 2019 Oct;60(10):1406-1413
pubmed: 30902877
J Nucl Med. 2021 Aug 1;62(8):1118-1125
pubmed: 33443063
EJNMMI Phys. 2020 Jun 17;7(1):41
pubmed: 32556844
J Nucl Med. 2020 Oct;61(10):1514-1519
pubmed: 32169912
J Nucl Med. 1999 Jan;40(1):3S-10S
pubmed: 9935082
Med Phys. 2021 Feb;48(2):556-568
pubmed: 33244792
Med Phys. 2020 Sep;47(9):4332-4339
pubmed: 32426853
Mol Imaging Biol. 2015 Oct;17(5):726-34
pubmed: 25790773
EJNMMI Phys. 2018 Jul 5;5(1):12
pubmed: 29974391
Med Phys. 2013 Nov;40(11):112503
pubmed: 24320462
Molecules. 2020 Sep 11;25(18):
pubmed: 32932783
Biomed Res Int. 2013;2013:935351
pubmed: 23865075

Auteurs

Laure Vergnaud (L)

CREATIS, CNRS UMR 5220, INSERM U 1044, Université de Lyon, INSA-Lyon, Université Lyon 1, Lyon, France. Laure.Vergnaud@creatis.insa-lyon.fr.
Centre de lutte contre le cancer Léon Bérard, Lyon, France. Laure.Vergnaud@creatis.insa-lyon.fr.

Anne-Laure Giraudet (AL)

Centre de lutte contre le cancer Léon Bérard, Lyon, France.

Aurélie Moreau (A)

Centre de lutte contre le cancer Léon Bérard, Lyon, France.

Julien Salvadori (J)

ICANS - Institut de cancérologie Strasbourg Europe, Strasbourg, France.

Alessio Imperiale (A)

ICANS - Institut de cancérologie Strasbourg Europe, Strasbourg, France.

Thomas Baudier (T)

CREATIS, CNRS UMR 5220, INSERM U 1044, Université de Lyon, INSA-Lyon, Université Lyon 1, Lyon, France.
Centre de lutte contre le cancer Léon Bérard, Lyon, France.

Jean-Noël Badel (JN)

Centre de lutte contre le cancer Léon Bérard, Lyon, France.

David Sarrut (D)

CREATIS, CNRS UMR 5220, INSERM U 1044, Université de Lyon, INSA-Lyon, Université Lyon 1, Lyon, France.
Centre de lutte contre le cancer Léon Bérard, Lyon, France.

Classifications MeSH