Nuclear Engineering and Technology

  • 제54권8호
  • /
  • Pages.3006-3016
  • /
  • 2022
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
권호 표지
DOI QR코드

DOI QR Code

Daily adaptive proton therapy: Feasibility study of detection of tumor variations based on tomographic imaging of prompt gamma emission from proton-boron fusion reaction

  • Choi, Min-Geon (Department of Biomedicine & Health Science and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea) ;
  • Law, Martin (Proton Therapy Pte Ltd.) ;
  • Djeng, Shin-Kien (Proton Therapy Pte Ltd.) ;
  • Kim, Moo-Sub (Department of Biomedicine & Health Science and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea) ;
  • Shin, Han-Back (Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University) ;
  • Choe, Bo-Young (Department of Biomedicine & Health Science and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea) ;
  • Yoon, Do-Kun (Department of Biomedicine & Health Science and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea) ;
  • Suh, Tae Suk (Department of Biomedicine & Health Science and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea)
  • 투고 : 2021.07.31
  • 심사 : 2022.03.04
  • 발행 : 2022.08.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, the images of specific prompt gamma (PG)-rays of 719 keV emitted from proton-boron reactions were analyzed using single-photon emission computed tomography (SPECT). Quantitative evaluation of the images verified the detection of anatomical changes in tumors, one of the important factors in daily adaptive proton therapy (DAPT) and verified the possibility of application of the PG-ray images to DAPT. Six scenarios were considered based on various sizes and locations compared to the reference virtual tumor to observe the anatomical alterations in the virtual tumor. Subsequently, PG-rays SPECT images were acquired using the modified ordered subset expectation-maximization algorithm, and these were evaluated using quantitative analysis methods. The results confirmed that the pixel range and location of the highest value of the normalized pixel in the PG-rays SPECT image profile changed according to the size and location of the virtual tumor. Moreover, the alterations in the virtual tumor size and location in the PG-rays SPECT images were similar to the true size and location alterations set in the phantom. Based on the above results, the tumor anatomical alterations in DAPT could be adequately detected and verified through SPECT imaging using the 719 keV PG-rays acquired during treatment.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Grant No. 2018R1A2B2005343 and No. 2017M3A9E2060428).

참고문헌

  1. D. Yan, F. Vicini, J. Wong, A. Martinez, Adaptive radiation therapy, Phys. Med. Biol. 42 (1997) 123. https://doi.org/10.1088/0031-9155/42/1/008
  2. J.-J. Sonke, M. Aznar, C. Rasch, Adaptive radiotherapy for anatomical changes, in: Book Adaptive Radiotherapy for Anatomical Changes, Elsevier, 2019, pp. 245-257.
  3. K.K. Brock, Adaptive radiotherapy: moving into the future, in: Book Adaptive Radiotherapy: Moving into the Future, NIH Public Access, 2019, p. 181.
  4. A. Lomax, Intensity modulated proton therapy and its sensitivity to treatment uncertainties 2: the potential effects of inter-fraction and inter-field motions, Phys. Med. Biol. 53 (2008) 1043. https://doi.org/10.1088/0031-9155/53/4/015
  5. A. Lomax, Intensity modulated proton therapy and its sensitivity to treatment uncertainties 1: the potential effects of calculational uncertainties, Phys. Med. Biol. 53 (2008) 1027. https://doi.org/10.1088/0031-9155/53/4/014
  6. H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Phys. Med. Biol. 57 (2012) R99. https://doi.org/10.1088/0031-9155/57/11/R99
  7. B. Cai, O.L. Green, R. Kashani, V.L. Rodriguez, S. Mutic, D. Yang, A practical implementation of physics quality assurance for photon adaptive radiotherapy, Z. Med. Phys. 28 (2018) 211-223. https://doi.org/10.1016/j.zemedi.2018.02.002
  8. O.L. Green, L.E. Henke, G.D. Hugo, Practical clinical workflows for online and offline adaptive radiation therapy, in: Book Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy, Elsevier, 2019, pp. 219-227.
  9. C.K. Glide-Hurst, P. Lee, A.D. Yock, J.R. Olsen, M. Cao, F. Siddiqui, W. Parker, A. Doemer, Y. Rong, A.U. Kishan, Adaptive radiation therapy (art) strategies and technical considerations: a state of the art review from nrg oncology, Int. J. Radiat. Oncol. Biol. Phys. 109 (2021) 1054-1075. https://doi.org/10.1016/j.ijrobp.2020.10.021
  10. D. Yan, D. Lockman, D. Brabbins, L. Tyburski, A. Martinez, An off-line strategy for constructing a patient-specific planning target volume in adaptive treatment process for prostate cancer, Int. J. Radiat. Oncol. Biol. Phys. 48 (2000) 289-302.
  11. D. Yan, J. Liang, Expected treatment dose construction and adaptive inverse planning optimization: implementation for offline head and neck cancer adaptive radiotherapy, Med. Phys. 40 (2013), 021719. https://doi.org/10.1118/1.4788659
  12. S. McGowan, N. Burnet, A. Lomax, Treatment planning optimisation in proton therapy, Br. J. Radiol. 86 (2013), 20120288-20120288. https://doi.org/10.1259/bjr.20120288
  13. F. Foroudi, J. Wong, T. Kron, A. Rolfo, A. Haworth, P. Roxby, J. Thomas, A. Herschtal, D. Pham, S. Williams, Online adaptive radiotherapy for muscle-invasive bladder cancer: results of a pilot study, Int. J. Radiat. Oncol. Biol. Phys. 81 (2011) 765-771. https://doi.org/10.1016/j.ijrobp.2010.06.061
  14. S. Lim-Reinders, B.M. Keller, S. Al-Ward, A. Sahgal, A. Kim, Online adaptive radiation therapy, Int. J. Radiat. Oncol. Biol. Phys. 99 (2017) 994-1003. https://doi.org/10.1016/j.ijrobp.2017.04.023
  15. L. Nenoff, M. Matter, A.G. Jarhall, C. Winterhalter, J. Gorgisyan, M. Josipovic, G.F. Persson, P.M. af Rosenschold, D.C. Weber, A.J. Lomax, Daily adaptive proton therapy: is it appropriate to use analytical dose calculations for plan adaption? Int. J. Radiat. Oncol. Biol. Phys. 107 (2020) 747-755. https://doi.org/10.1016/j.ijrobp.2020.03.036
  16. E.B. Villarroel, X. Geets, E. Sterpin, Online adaptive dose restoration in intensity modulated proton therapy of lung cancer to account for interfractional density changes, Phys. Imag. Radiat. Oncol. 15 (2020) 30-37. https://doi.org/10.1016/j.phro.2020.06.004
  17. A. Thummerer, B.A. de Jong, P. Zaffino, A. Meijers, G.G. Marmitt, J. Seco, R.J. Steenbakkers, J.A. Langendijk, S. Both, M.F. Spadea, Comparison of the suitability of CBCT-and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients, Phys. Med. Biol. 65 (2020) 235036. https://doi.org/10.1088/1361-6560/abb1d6
  18. A. Thummerer, P. Zaffino, A. Meijers, G.G. Marmitt, J. Seco, R.J. Steenbakkers, J.A. Langendijk, S. Both, M.F. Spadea, A.C. Knopf, Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy, Phys. Med. Biol. 65 (2020), 095002. https://doi.org/10.1088/1361-6560/ab7d54
  19. A. Hoffmann, B. Oborn, M. Moteabbed, S. Yan, T. Bortfeld, A. Knopf, H. Fuchs, D. Georg, J. Seco, M.F. Spadea, MR-guided proton therapy: a review and a preview, Radiat. Oncol. 15 (2020) 1-13. https://doi.org/10.1186/s13014-019-1449-z
  20. S. Acharya, C. Wang, S. Quesada, M.A. Gargone, O. Ates, J. Uh, M.J. Krasin, T.E. Merchant, C.-h. Hua, Adaptive proton therapy for pediatric patients: improving the quality of the delivered plan with on-treatment MRI, Int. J. Radiat. Oncol. Biol. Phys. 109 (2021) 242-251. https://doi.org/10.1016/j.ijrobp.2020.08.036
  21. P.J. Taddei, D. Mirkovic, J.D. Fontenot, A. Giebeler, Y. Zheng, D. Kornguth, R. Mohan, W.D. Newhauser, Stray radiation dose and second cancer risk for a pediatric patient receiving craniospinal irradiation with proton beams, Phys. Med. Biol. 54 (2009) 2259. https://doi.org/10.1088/0031-9155/54/8/001
  22. Y. An, J. Shan, S.H. Patel, W. Wong, S.E. Schild, X. Ding, M. Bues, W. Liu, Robust intensity-modulated proton therapy to reduce high linear energy transfer in organs at risk, Med. Phys. 44 (2017) 6138-6147. https://doi.org/10.1002/mp.12610
  23. S. Geninatti-Crich, A. Deagostino, A. Toppino, D. Alberti, P. Venturello, S. Aime, Boronated compounds for imaging guided BNCT applications, Anti-canc. Agents Med. Chem. (Form. Curr. Med. Chem.-Anti-Cancer Agents 12 (2012) 543-553. https://doi.org/10.2174/187152012800617786
  24. B.C. Das, D.P. Ojha, S. Das, T. Evans, Boron Compounds in Molecular Imaging, Boron-Based Compounds: Potential and Emerging Applications in Medicine, 2018, pp. 205-231.
  25. D.-K. Yoon, J.-Y. Jung, T.S. Suh, Application of proton boron fusion reaction to radiation therapy: a Monte Carlo simulation study, Appl. Phys. Lett. 105 (2014) 223507. https://doi.org/10.1063/1.4903345
  26. G. Cirrone, L. Manti, D. Margarone, G. Petringa, L. Giuffrida, A. Minopoli, A. Picciotto, G. Russo, F. Cammarata, P. Pisciotta, First experimental proof of Proton Boron Capture Therapy (PBCT) to enhance protontherapy effectiveness, Sci. Rep. 8 (2018) 1-15.
  27. D.-K. Yoon, N. Naganawa, M. Kimura, M.-G. Choi, M.-S. Kim, Y.-J. Kim, M.W.-M. Law, S.-K. Djeng, H.-B. Shin, B.-Y. Choe, Application of proton boron fusion to proton therapy: experimental verification to detect the alpha particles, Appl. Phys. Lett. 115 (2019) 223701. https://doi.org/10.1063/1.5128953
  28. P. Blaha, C. Feoli, S. Agosteo, M. Calvaruso, F.P. Cammarata, R. Catalano, M. Ciocca, G.A.P. Cirrone, V. Conte, G. Cuttone, The proton-boron reaction increases the radiobiological effectiveness of clinical low-and high-energy proton beams: novel experimental evidence and perspectives, Front. Oncol. 11 (2021).
  29. M.-S. Kim, M. Wai-Ming Law, S.-K. Djeng, H.-B. Shin, M.-G. Choi, Y.-J. Kim, B.-Y. Choe, T.S. Suh, D.-K. Yoon, Synergy effect of alpha particles by using natural boron in proton therapy: computational verification, AIP Adv. 9 (2019) 115017. https://doi.org/10.1063/1.5124322
  30. G. Petringa, G. Cirrone, C. Caliri, G. Cuttone, L. Giuffrida, G. La Rosa, R. Manna, L. Manti, V. Marchese, C. Marchetta, Prompt gamma-ray emission for future imaging applications in proton-boron fusion therapy, J. Instrum. 12 (2017) C03059. https://doi.org/10.1088/1748-0221/12/03/C03059
  31. H.-B. Shin, M.-S. Kim, S. Kim, K.B. Kim, J.-Y. Jung, D.-K. Yoon, T.S. Suh, Quantitative analysis of prompt gamma ray imaging during proton boron fusion therapy according to boron concentration, Oncotarget 9 (2018) 3089. https://doi.org/10.18632/oncotarget.23201
  32. M. Suzuki, Boron neutron capture therapy (BNCT): a unique role in radiotherapy with a view to entering the accelerator-based BNCT era, Int. J. Clin. Oncol. 25 (2020) 43-50. https://doi.org/10.1007/s10147-019-01480-4
  33. S.J. Frank, J.D. Cox, M. Gillin, R. Mohan, A.S. Garden, D.I. Rosenthal, G.B. Gunn, R.S. Weber, M.S. Kies, J.S. Lewin, Multifield optimization intensity modulated proton therapy for head and neck tumors: a translation to practice, Int. J. Radiat. Oncol. Biol. Phys. 89 (2014) 846-853. https://doi.org/10.1016/j.ijrobp.2014.04.019
  34. T. Tsurubuchi, M. Shirakawa, W. Kurosawa, K. Matsumoto, R. Ubagai, H. Umishio, Y. Suga, J. Yamazaki, A. Arakawa, Y. Maruyama, Evaluation of a novel boron-containing α-d-Mannopyranoside for BNCT, Cells 9 (2020) 1277. https://doi.org/10.3390/cells9051277
  35. F. Tommasino, M. Rovituso, S. Fabiano, S. Piffer, C. Manea, S. Lorentini, S. Lanzone, Z. Wang, M. Pasini, W. Burger, Proton beam characterization in the experimental room of the Trento Proton Therapy facility, Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 869 (2017) 15-20. https://doi.org/10.1016/j.nima.2017.06.017
  36. D.K. Yoon, J.Y. Jung, K. Jo Hong, K. Sil Lee, T. Suk Suh, GPU-based prompt gamma ray imaging from boron neutron capture therapy, Med. Phys. 42 (2015) 165-169.
  37. H.-B. Shin, M.-S. Kim, M. Law, S.-K. Djeng, M.-G. Choi, B.W. Choi, S. Kang, D.- W. Kim, T.S. Suh, D.-K. Yoon, Application of sigmoidal optimization to reconstruct nuclear medicine image: comparison with filtered back projection and iterative reconstruction method, Nucl. Eng. Technol. 53 (2021) 258-265. https://doi.org/10.1016/j.net.2020.06.029
  38. D.M. Minsky, A. Valda, A. Kreiner, S. Green, C. Wojnecki, Z. Ghani, First tomographic image of neutron capture rate in a BNCT facility, Appl. Radiat. Isot. 69 (2011) 1858-1861. https://doi.org/10.1016/j.apradiso.2011.01.030
  39. B. Hales, T. Katabuchi, N. Hayashizaki, K. Terada, M. Igashira, T. Kobayashi, Feasibility study of SPECT system for online dosimetry imaging in boron neutron capture therapy, Appl. Radiat. Isot. 88 (2014) 167-170. https://doi.org/10.1016/j.apradiso.2013.11.135
  40. A. Winkler, H. Koivunoro, V. Reijonen, I. Auterinen, S. Savolainen, Prompt gamma and neutron detection in BNCT utilizing a CdTe detector, Appl. Radiat. Isot. 106 (2015) 139-144. https://doi.org/10.1016/j.apradiso.2015.07.040