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Chatzipapas K, Dordevic M, Zivkovic S, Tran NH, Lampe N, Sakata D, Petrovic I, Ristic-Fira A, Shin WG, Zein S, Brown JMC, Kyriakou I, Emfietzoglou D, Guatelli S, Incerti S. Geant4-DNA simulation of human cancer cells irradiation with helium ion beams. Phys Med 2023; 112:102613. [PMID: 37356419 DOI: 10.1016/j.ejmp.2023.102613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/03/2023] [Accepted: 05/30/2023] [Indexed: 06/27/2023] Open
Abstract
PURPOSE This study aimed to develop a computational environment for the accurate simulation of human cancer cell irradiation using Geant4-DNA. New cell geometrical models were developed and irradiated by alpha particle beams to induce DNA damage. The proposed approach may help further investigation of the benefits of external alpha irradiation therapy. METHODS The Geant4-DNA Monte Carlo (MC) toolkit allows the simulation of cancer cell geometries that can be combined with accurate modelling of physical, physicochemical and chemical stages of liquid water irradiation, including radiolytic processes. Geant4-DNA is used to calculate direct and non-direct DNA damage yields, such as single and double strand breaks, produced by the deposition of energy or by the interaction of DNA with free radicals. RESULTS In this study, the "molecularDNA" example application of Geant4-DNA was used to quantify early DNA damage in human cancer cells upon irradiation with alpha particle beams, as a function of linear energy transfer (LET). The MC simulation results are compared to experimental data, as well as previously published simulation data. The simulation results agree well with the experimental data on DSB yields in the lower LET range, while the experimental data on DSB yields are lower than the results obtained with the "molecularDNA" example in the higher LET range. CONCLUSION This study explored and demonstrated the possibilities of the Geant4-DNA toolkit together with the "molecularDNA" example to simulate the helium beam irradiation of cancer cell lines, to quantify the early DNA damage, or even the following DNA damage response.
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Affiliation(s)
| | - Milos Dordevic
- Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11351 Vinca, Belgrade, Serbia.
| | - Sara Zivkovic
- Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11351 Vinca, Belgrade, Serbia
| | - Ngoc Hoang Tran
- University of Bordeaux, CNRS, LP2i, UMR5797, F-33170 Gradignan, France
| | | | - Dousatsu Sakata
- Division of Health Sciences, Osaka University, Osaka 565-0871, Japan
| | - Ivan Petrovic
- Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11351 Vinca, Belgrade, Serbia
| | - Aleksandra Ristic-Fira
- Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11351 Vinca, Belgrade, Serbia
| | - Wook-Geun Shin
- Physics Division, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114 MA, USA
| | - Sara Zein
- University of Bordeaux, CNRS, LP2i, UMR5797, F-33170 Gradignan, France
| | - Jeremy M C Brown
- Optical Sciences Centre, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ioanna Kyriakou
- Medical Physics Laboratory, Department of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Dimitris Emfietzoglou
- Medical Physics Laboratory, Department of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Susanna Guatelli
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Sebastien Incerti
- University of Bordeaux, CNRS, LP2i, UMR5797, F-33170 Gradignan, France
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Eleftheriadis V, Savvidis G, Paneta V, Chatzipapas K, Kagadis GC, Papadimitroulas P. A framework for prediction of personalized pediatric nuclear medical dosimetry based on Machine Learning and Monte Carlo techniques. Phys Med Biol 2023; 68. [PMID: 36921349 DOI: 10.1088/1361-6560/acc4a5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 03/15/2023] [Indexed: 03/17/2023]
Abstract
GOAL A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ considering personalized anatomical characteristics of any new pediatric patient.
Method: GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs. A simulated dosimetry database was developed for 28 pediatric phantoms (age range 2-17 years old, both genders) and 5 different radiopharmaceuticals. Machine Learning regression models were trained on the produced simulated dataset, with Leave One Out Cross Validation for the prediction model evaluation. Hyperparameter optimization and ensemble learning techniques for a variation of input features were applied for achieving the best predictive power, leading to the development of a SADR prediction toolkit for any new pediatric patient for the studied organs and radiopharmaceuticals.
Main results: SADR values for 30 organs of interest were calculated via Monte Carlo simulations for 28 pediatric phantoms for the cases of five radiopharmaceuticals. The relative percentage uncertainty in the extracted dose values per organ was lower than 2.7%. An internal dosimetry prediction toolkit which can accurately predict SADRs in 30 organs for five different radiopharmaceuticals, with mean absolute percentage error on the level of 8% was developed, with specific focus on pediatric patients, by using Machine Learning regression algorithms, Single or Multiple organ training and Artificial Intelligence ensemble techniques.
Conclusion: A large dosimetry simulated database was developed and utilized for the training of Machine Learning models. The developed predictive models provide very fast results (< 2 sec) with an accuracy >90% with respect to the ground truth of Monte Carlo, considering personalized anatomical characteristics and the biodistribution of each radiopharmaceutical. The proposed method is applicable to other medical dosimetry applications in different patients' populations.
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Affiliation(s)
| | | | | | | | - George C Kagadis
- Department of Medical Physics, University of Patras, School of Medicine, Patras, Rion, 26500, GREECE
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Sarrut D, Bała M, Bardiès M, Bert J, Chauvin M, Chatzipapas K, Dupont M, Etxebeste A, M Fanchon L, Jan S, Kayal G, S Kirov A, Kowalski P, Krzemien W, Labour J, Lenz M, Loudos G, Mehadji B, Ménard L, Morel C, Papadimitroulas P, Rafecas M, Salvadori J, Seiter D, Stockhoff M, Testa E, Trigila C, Pietrzyk U, Vandenberghe S, Verdier MA, Visvikis D, Ziemons K, Zvolský M, Roncali E. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol 2021; 66:10.1088/1361-6560/abf276. [PMID: 33770774 PMCID: PMC10549966 DOI: 10.1088/1361-6560/abf276] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/13/2022]
Abstract
Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.
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Affiliation(s)
- David Sarrut
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | | | - Manuel Bardiès
- Cancer Research Institute of Montpellier, U1194 INSERM/ICM/Montpellier University, 208 Av des Apothicaires, F-34298 Montpellier cedex 5, France
| | - Julien Bert
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Maxime Chauvin
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | | | | | - Ane Etxebeste
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Louise M Fanchon
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Sébastien Jan
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, F-91401, Orsay, France
| | - Gunjan Kayal
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Paweł Kowalski
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Joey Labour
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Mirjam Lenz
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | - George Loudos
- Bioemission Technology Solutions (BIOEMTECH), Alexandras Av. 116, Athens, Greece
| | | | - Laurent Ménard
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | | | | | - Magdalena Rafecas
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Julien Salvadori
- Department of Nuclear Medicine and Nancyclotep molecular imaging platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
| | - Daniel Seiter
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, United States of America
| | - Mariele Stockhoff
- Medical Image and Signal Processing (MEDISIP), Ghent University, Ghent, Belgium
| | - Etienne Testa
- Univ. Lyon, Univ. Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, F-69622, Villeurbanne, France
| | - Carlotta Trigila
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
| | - Uwe Pietrzyk
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | | | - Marc-Antoine Verdier
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | - Dimitris Visvikis
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Karl Ziemons
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
| | - Milan Zvolský
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
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