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Scarinci I, Valente M, Pérez P. A machine learning-based model for a dose point kernel calculation. EJNMMI Phys 2023; 10:41. [PMID: 37358735 DOI: 10.1186/s40658-023-00560-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/13/2023] [Indexed: 06/27/2023] Open
Abstract
PURPOSE Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. METHODS DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with [Formula: see text]Y. RESULTS The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than [Formula: see text] in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than [Formula: see text] were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. CONCLUSION An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.
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Affiliation(s)
- Ignacio Scarinci
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina
| | - Mauro Valente
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina.
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina.
- Centro de Excelencia en Física e Ingeniería en Salud (CFIS) & Departamento de Ciencias Físicas, Universidad de la Frontera, Avenida Francisco Salazar 01145, 4811230, Temuco, Cautín, Chile.
| | - Pedro Pérez
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina
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Scarinci I, Valente M, Pérez P. A Machine Learning based model for a Dose Point Kernel calculation. RESEARCH SQUARE 2023:rs.3.rs-2419706. [PMID: 36711517 PMCID: PMC9882689 DOI: 10.21203/rs.3.rs-2419706/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
PURPOSE Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study shows applications of machine learning to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. METHODS DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Three machine learning (ML) algorithms were trained using the MC DPKs. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the ML sDPK approach was applied to a patient-specific case calculating the dose voxel kernels (DVK) for a hepatic radioembolization treatment with \(^{90}\)Y. RESULTS The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than \(10%\) in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than \(7 %\) were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. CONCLUSION An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required remarkable short computation times.
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Affiliation(s)
- Ignacio Scarinci
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina
| | - Mauro Valente
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina.,Centro de Excelencia en Física e Ingeniería en Salud (CFIS) & Departamento de Ciencias Físicas, Universidad de la Frontera, Avenida Francisco Salazar 01145, Temuco, 4811230, Cautín, Chile.,Corresponding author(s).
| | - Pedro Pérez
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina
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Bianchi A, Selva A, Rossignoli M, Pasquato F, Missiaggia M, Scifoni E, La Tessa C, Tommasino F, Conte V. Microdosimetry with a mini-TEPC in the spread-out Bragg peak of 148 MeV protons. Radiat Phys Chem Oxf Engl 1993 2022. [DOI: 10.1016/j.radphyschem.2022.110567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Bellinzona EV, Grzanka L, Attili A, Tommasino F, Friedrich T, Krämer M, Scholz M, Battistoni G, Embriaco A, Chiappara D, Cirrone GAP, Petringa G, Durante M, Scifoni E. Biological Impact of Target Fragments on Proton Treatment Plans: An Analysis Based on the Current Cross-Section Data and a Full Mixed Field Approach. Cancers (Basel) 2021; 13:cancers13194768. [PMID: 34638254 PMCID: PMC8507563 DOI: 10.3390/cancers13194768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/13/2021] [Accepted: 09/13/2021] [Indexed: 01/15/2023] Open
Abstract
Simple Summary Proton therapy is now an established external radiotherapy modality for cancer treatment. Clinical routine currently neglects the radiobiological impact of nuclear target fragments even if experimental evidences show a significant enhancement in cell-killing effect due to secondary particles. This paper quantifies the contribution of proton target fragments of different charge in different irradiation scenarios and compares the computationally predicted corrections to the overall biological dose with experimental data. Abstract Clinical routine in proton therapy currently neglects the radiobiological impact of nuclear target fragments generated by proton beams. This is partially due to the difficult characterization of the irradiation field. The detection of low energetic fragments, secondary protons and fragments, is in fact challenging due to their very short range. However, considering their low residual energy and therefore high LET, the possible contribution of such heavy particles to the overall biological effect could be not negligible. In this context, we performed a systematic analysis aimed at an explicit assessment of the RBE (relative biological effectiveness, i.e., the ratio of photon to proton physical dose needed to achieve the same biological effect) contribution of target fragments in the biological dose calculations of proton fields. The TOPAS Monte Carlo code has been used to characterize the radiation field, i.e., for the scoring of primary protons and fragments in an exemplary water target. TRiP98, in combination with LEM IV RBE tables, was then employed to evaluate the RBE with a mixed field approach accounting for fragments’ contributions. The results were compared with that obtained by considering only primary protons for the pristine beam and spread out Bragg peak (SOBP) irradiations, in order to estimate the relative weight of target fragments to the overall RBE. A sensitivity analysis of the secondary particles production cross-sections to the biological dose has been also carried out in this study. Finally, our modeling approach was applied to the analysis of a selection of cell survival and RBE data extracted from published in vitro studies. Our results indicate that, for high energy proton beams, the main contribution to the biological effect due to the secondary particles can be attributed to secondary protons, while the contribution of heavier fragments is mainly due to helium. The impact of target fragments on the biological dose is maximized in the entrance channels and for small α/β values. When applied to the description of survival data, model predictions including all fragments allowed better agreement to experimental data at high energies, while a minor effect was observed in the peak region. An improved description was also obtained when including the fragments’ contribution to describe RBE data. Overall, this analysis indicates that a minor contribution can be expected to the overall RBE resulting from target fragments. However, considering the fragmentation effects can improve the agreement with experimental data for high energy proton beams.
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Affiliation(s)
- Elettra Valentina Bellinzona
- Trento Institute for Fundamental Physics and Applications (TIFPA), National Institute for Nuclear Physics, (INFN), 38123 Trento, Italy; (E.V.B.); (F.T.)
- Department of Physics, University of Trento, 38123 Trento, Italy;
| | - Leszek Grzanka
- The Department of Radiation Research and Proton Radiotherapy, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Krakow, Poland;
| | - Andrea Attili
- “Roma Tre” Section, INFN—National Institute for Nuclear Physics, 00146 Roma, Italy;
| | - Francesco Tommasino
- Trento Institute for Fundamental Physics and Applications (TIFPA), National Institute for Nuclear Physics, (INFN), 38123 Trento, Italy; (E.V.B.); (F.T.)
- Department of Physics, University of Trento, 38123 Trento, Italy;
| | - Thomas Friedrich
- Department of Biophysics, GSI Helmholtzzentrum für Schwerionenforschung, 64291 Darmstadt, Germany; (T.F.); (M.K.); (M.S.); (M.D.)
| | - Michael Krämer
- Department of Biophysics, GSI Helmholtzzentrum für Schwerionenforschung, 64291 Darmstadt, Germany; (T.F.); (M.K.); (M.S.); (M.D.)
| | - Michael Scholz
- Department of Biophysics, GSI Helmholtzzentrum für Schwerionenforschung, 64291 Darmstadt, Germany; (T.F.); (M.K.); (M.S.); (M.D.)
| | | | - Alessia Embriaco
- “Pavia” Section, INFN—National Institute for Nuclear Physics, 6-27100 Pavia, Italy;
| | - Davide Chiappara
- Laboratori Nazionali del Sud, INFN—National Institute for Nuclear Physics, 95125 Catania, Italy; (D.C.); (G.A.P.C.); (G.P.)
| | - Giuseppe A. P. Cirrone
- Laboratori Nazionali del Sud, INFN—National Institute for Nuclear Physics, 95125 Catania, Italy; (D.C.); (G.A.P.C.); (G.P.)
| | - Giada Petringa
- Laboratori Nazionali del Sud, INFN—National Institute for Nuclear Physics, 95125 Catania, Italy; (D.C.); (G.A.P.C.); (G.P.)
| | - Marco Durante
- Department of Biophysics, GSI Helmholtzzentrum für Schwerionenforschung, 64291 Darmstadt, Germany; (T.F.); (M.K.); (M.S.); (M.D.)
- Institut für Physik Kondensierter Materie, Technische Universität, 64289 Darmstadt, Germany
| | - Emanuele Scifoni
- Trento Institute for Fundamental Physics and Applications (TIFPA), National Institute for Nuclear Physics, (INFN), 38123 Trento, Italy; (E.V.B.); (F.T.)
- Department of Physics, University of Trento, 38123 Trento, Italy;
- Correspondence:
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