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Flux G, Gleisner KS, Bardies M. Physics for molecular radiotherapy - The battle between science and simplicity. Phys Med 2024; 124:103413. [PMID: 38880679 DOI: 10.1016/j.ejmp.2024.103413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/18/2024] Open
Affiliation(s)
- Glenn Flux
- Joint Dept of Physics, Royal Marsden Hospital & Institute of Cancer Research, Sutton, UK.
| | | | - Manuel Bardies
- Department of Nuclear Medicine, Institut du Cancer de Montpellier, Université de Montpellier, Montpellier, France; Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Montpellier, France
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Gomes CV, Mendes BM, Paixão L, Gnesin S, Müller C, van der Meulen NP, Strobel K, Fonseca TCF, Lima TVM. Comparison of the dosimetry of scandium-43 and scandium-44 patient organ doses in relation to commonly used gallium-68 for imaging neuroendocrine tumours. EJNMMI Phys 2024; 11:61. [PMID: 39004681 PMCID: PMC11247068 DOI: 10.1186/s40658-024-00669-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/05/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Several research groups have explored the potential of scandium radionuclides for theragnostic applications due to their longer half-lives and equal or similar coordination chemistry between their diagnostic and therapeutic counterparts, as well as lutetium-177 and terbium-161, respectively. Unlike the gallium-68/lutetium-177 pair, which may show different in-vivo uptake patterns, the use of scandium radioisotopes promises consistent behaviour between diagnostic and therapeutic radiopeptides. An advantage of scandium's longer half-life over gallium-68 is the ability to study radiopeptide uptake over extended periods and its suitability for centralized production and distribution. However, concerns arise from scandium-44's decay characteristics and scandium-43's high production costs. This study aimed to evaluate the dosimetric implications of using scandium radioisotopes with somatostatin analogues against gallium-68 for PET imaging of neuroendocrine tumours. METHODS Absorbed dose per injected activity (AD/IA) from the generated time-integrated activity curve (TIAC) were estimated using the radiopeptides [43/44/44mSc]Sc- and [68Ga]Ga-DOTATATE. The kidneys, liver, spleen, and red bone marrow (RBM) were selected for dose estimation studies. The EGSnrc and MCNP6.1 Monte Carlo (MC) codes were used with female (AF) and male (AM) ICRP phantoms. The results were compared to Olinda/EXM software, and the effective dose concentrations assessed, varying composition between the scandium radioisotopes. RESULTS Our findings showed good agreement between the MC codes, with - 3 ± 8% mean difference. Kidneys, liver, and spleen showed differences between the MC codes (min and max) in a range of - 4% to 8%. This was observed for both phantoms for all radiopeptides used in the study. Compared to Olinda/EXM the largest observed difference was for the RBM, of 21% for the AF and 16% for the AM for scandium- and gallium-based radiopeptides. Despite the differences, our findings showed a higher absorbed dose on [43/44Sc]Sc-DOTATATE compared to its 68Ga-based counterpart. CONCLUSION This study found that [43/44Sc]Sc-DOTATATE delivers a higher absorbed dose to organs at risk compared to [68Ga]Ga-DOTATATE, assuming equal distribution. This is due to the longer half-life of scandium radioisotopes compared to gallium-68. However, calculated doses are within acceptable ranges, making scandium radioisotopes a feasible replacement for gallium-68 in PET imaging, potentially offering enhanced diagnostic potential with later timepoint imaging.
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Affiliation(s)
- Carlos Vinícius Gomes
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012, Bern, Switzerland.
- Post-graduation Program in Nuclear Sciences and Techniques, Department of Nuclear Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil.
| | - Bruno Melo Mendes
- Nuclear Technology Development Center - CDTN/CNEN, Belo Horizonte, Brazil
| | - Lucas Paixão
- Department of Anatomy and Imaging, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Silvano Gnesin
- Institute of Radiation Physics, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Cristina Müller
- Center for Radiopharmaceutical Sciences, Paul Scherrer Institute (PSI), Villigen, Switzerland
| | - Nicholas P van der Meulen
- Center for Radiopharmaceutical Sciences, Paul Scherrer Institute (PSI), Villigen, Switzerland
- Laboratory of Radiochemistry, Paul Scherrer Institute (PSI), Villigen, Switzerland
| | - Klaus Strobel
- Institute of Radiology and Nuclear Medicine, Luzerner Kantonsspital - LUKS, Lucerne, Switzerland
| | - Telma Cristina Ferreira Fonseca
- Post-graduation Program in Nuclear Sciences and Techniques, Department of Nuclear Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Nuclear Technology Development Center - CDTN/CNEN, Belo Horizonte, Brazil
| | - Thiago Viana Miranda Lima
- Institute of Radiology and Nuclear Medicine, Luzerner Kantonsspital - LUKS, Lucerne, Switzerland
- Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
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Moraitis A, Küper A, Tran-Gia J, Eberlein U, Chen Y, Seifert R, Shi K, Kim M, Herrmann K, Fragoso Costa P, Kersting D. Future Perspectives of Artificial Intelligence in Bone Marrow Dosimetry and Individualized Radioligand Therapy. Semin Nucl Med 2024; 54:460-469. [PMID: 39013673 DOI: 10.1053/j.semnuclmed.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
Radioligand therapy is an emerging and effective treatment option for various types of malignancies, but may be intricately linked to hematological side effects such as anemia, lymphopenia or thrombocytopenia. The safety and efficacy of novel theranostic agents, targeting increasingly complex targets, can be well served by comprehensive dosimetry. However, optimization in patient management and patient selection based on risk-factors predicting adverse events and built upon reliable dose-response relations is still an open demand. In this context, artificial intelligence methods, especially machine learning and deep learning algorithms, may play a crucial role. This review provides an overview of upcoming opportunities for integrating artificial intelligence methods into the field of dosimetry in nuclear medicine by improving bone marrow and blood dosimetry accuracy, enabling early identification of potential hematological risk-factors, and allowing for adaptive treatment planning. It will further exemplify inspirational success stories from neighboring disciplines that may be translated to nuclear medicine practices, and will provide conceptual suggestions for future directions. In the future, we expect artificial intelligence-assisted (predictive) dosimetry combined with clinical parameters to pave the way towards truly personalized theranostics in radioligand therapy.
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Affiliation(s)
- Alexandros Moraitis
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| | - Alina Küper
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Uta Eberlein
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Yizhou Chen
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Robert Seifert
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Moon Kim
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pedro Fragoso Costa
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - David Kersting
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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Mansouri Z, Salimi Y, Akhavanallaf A, Shiri I, Teixeira EPA, Hou X, Beauregard JM, Rahmim A, Zaidi H. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [ 177Lu]Lu-DOTATATE radiopharmaceutical therapy. Eur J Nucl Med Mol Imaging 2024; 51:1516-1529. [PMID: 38267686 PMCID: PMC11043201 DOI: 10.1007/s00259-024-06618-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Eliluane Pirazzo Andrade Teixeira
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Xinchi Hou
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Mathieu Beauregard
- Cancer Research Centre and Department of Radiology and Nuclear Medicine, Université Laval, Quebec City, QC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Stokke C, Gnesin S, Tran-Gia J, Cicone F, Holm S, Cremonesi M, Blakkisrud J, Wendler T, Gillings N, Herrmann K, Mottaghy FM, Gear J. EANM guidance document: dosimetry for first-in-human studies and early phase clinical trials. Eur J Nucl Med Mol Imaging 2024; 51:1268-1286. [PMID: 38366197 PMCID: PMC10957710 DOI: 10.1007/s00259-024-06640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/04/2024] [Indexed: 02/18/2024]
Abstract
The numbers of diagnostic and therapeutic nuclear medicine agents under investigation are rapidly increasing. Both novel emitters and novel carrier molecules require careful selection of measurement procedures. This document provides guidance relevant to dosimetry for first-in human and early phase clinical trials of such novel agents. The guideline includes a short introduction to different emitters and carrier molecules, followed by recommendations on the methods for activity measurement, pharmacokinetic analyses, as well as absorbed dose calculations and uncertainty analyses. The optimal use of preclinical information and studies involving diagnostic analogues is discussed. Good practice reporting is emphasised, and relevant dosimetry parameters and method descriptions to be included are listed. Three examples of first-in-human dosimetry studies, both for diagnostic tracers and radionuclide therapies, are given.
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Affiliation(s)
- Caroline Stokke
- Department of Diagnostic Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
- Department of Physics, University of Oslo, Oslo, Norway.
| | - Silvano Gnesin
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Francesco Cicone
- Nuclear Medicine Unit, Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Søren Holm
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Marta Cremonesi
- Department of Medical Imaging and Radiation Sciences, European Institute of Oncology, IRCCS, Milan, Italy
| | - Johan Blakkisrud
- Department of Diagnostic Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Thomas Wendler
- Computer-Aided Medical Procedures and Augmented Reality, Technische Universität München, Munich, Germany
- Clinical Computational Medical Imaging Research, Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany
| | - Nic Gillings
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen, and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
- National Center for Tumor Diseases (NCT), NCT West, Heidelberg, Germany
| | - Felix M Mottaghy
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jonathan Gear
- Joint Department of Physics, Royal Marsden NHSFT & Institute of Cancer Research, Sutton, UK
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Hardiansyah D, Yousefzadeh-Nowshahr E, Kind F, Beer AJ, Ruf J, Glatting G, Mix M. Single-Time-Point Renal Dosimetry Using Nonlinear Mixed-Effects Modeling and Population-Based Model Selection in [ 177Lu]Lu-PSMA-617 Therapy. J Nucl Med 2024; 65:566-572. [PMID: 38423787 DOI: 10.2967/jnumed.123.266268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
The aim of this study was to investigate the accuracy of single-time-point (STP) renal dosimetry imaging using SPECT/CT data, a nonlinear mixed-effects (NLME) model, and a population-based model selection (PBMS) in a large population for 177Lu-labeled prostate-specific membrane antigen therapy. Methods: Biokinetic data (mean ± SD) of [177Lu]Lu-PSMA-617 in kidneys at time points 1 (1.8 ± 0.8 h), 2 (18.7 ± 0.9 h), 3 (42.6 ± 1.0 h), 4 (66.3 ± 0.9 h), and 5 (160.3 ± 24.2 h) after injection were obtained from 63 patients with metastatic castration-resistant prostate cancer using SPECT/CT. Thirteen functions were derived from various parameterizations of 1- to 5-exponential functions. The function's parameters were fitted in the NLME framework to the all-time-point (ATP) data. The PBMS NLME method was performed using the goodness-of-fit test and Akaike weight to select the best function fitting the data. The best function from ATP fitting was used to calculate the reference time-integrated activity and absorbed doses. In STP dosimetry, the parameters of a particular patient with STP data were fitted simultaneously to the STP data at different time points of that patient with ATP data of all other patients. The parameters from STP fitting were used to calculate the STP time-integrated activity and absorbed doses. Relative deviations (RDs) and root-mean-square errors (RMSEs) were used to analyze the accuracy of the calculated STP absorbed dose compared with the reference absorbed dose obtained from the best-fit ATP function. The performance of STP dosimetry using PBMS NLME modeling was compared with the Hänscheid and Madsen methods. Results: The function [Formula: see text] was selected as the best-fit ATP function, with an Akaike weight of 100%. For STP dosimetry, the STP measurement by SPECT/CT at time point 3 (42.6 ± 1.0 h) showed a relatively low mean RD of -4.4% ± 9.4% and median RD of -0.7%. Time point 3 had the lowest RMSE value compared with those at the other 4 time points. The RMSEs of the absorbed dose RDs for time points 1-5 were 23%, 16%, 10%, 20%, and 53%, respectively. The STP dosimetry using the PBMS NLME method outperformed the Hänscheid and Madsen methods for all investigated time points. Conclusion: Our results show that a single measurement of SPECT/CT at 2 d after injection might be used to calculate accurate kidney-absorbed doses using the NLME method and PBMS.
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Affiliation(s)
- Deni Hardiansyah
- Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia;
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Elham Yousefzadeh-Nowshahr
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Felix Kind
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
| | - Ambros J Beer
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Juri Ruf
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
| | - Gerhard Glatting
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
- Nuclear Medicine Division, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Science, Stellenbosch University, Cape Town, South Africa
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