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Longitudinal monitoring of Apparent Diffusion Coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiotherapy on an MR-Linac at 1.5 T: a prospective feasibility study Cover

Longitudinal monitoring of Apparent Diffusion Coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiotherapy on an MR-Linac at 1.5 T: a prospective feasibility study

Open Access
|Jun 2023

References

  1. Mottet N, van den Bergh RCN, Briers E, van den Broeck T, Cumberbatch MG, de Santis M, et al. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 2021; 79: 243–62. doi: 10.1016/j.eururo.2020.09.042
  2. Pathmanathan AU, McNair HA, Schmidt MA, Brand DH, Delacroix L, Eccles CL, et al. Comparison of prostate delineation on multimodality imaging for MR-guided radiotherapy. Br J Radiol 2019; 92: 20180948. doi: 10.1259/bjr.20180948
  3. Dunlop A, Mitchell A, Tree A, Barnes H, Bower L, Chick J, et al. Daily adaptive radiotherapy for patients with prostate cancer using a high field MR-linac: initial clinical experiences and assessment of delivered doses compared to a C-arm linac. Clin Transl Radiat Oncol 2020; 23: 35–42. doi: 10.1016/j.ctro.2020.04.011
  4. Wegener D, Thome A, Paulsen F, Gani C, Boldt J, Butzer S, et al. First experience and prospective evaluation on feasibility and acute toxicity of online adaptive radiotherapy of the prostate bed as salvage treatment in patients with biochemically recurrent prostate cancer on a 1.5 T MR-linac. J Clin Med 2022; 11: 4651. doi: 10.3390/jcm11164651
  5. Grégoire V, Guckenberger M, Haustermans K, Lagendijk JJW, Ménard C, Pötter R, et al. Image-guidance in radiation therapy for better cure of cancer. Mol Oncol 2020; 14: 1470–91. doi: 10.1002/1878-0261.12751
  6. Decker G, Mürtz P, Gieseke J, Träber F, Block W, Sprinkart AM, et al. Intensity-modulated radiotherapy of the prostate: dynamic ADC monitoring by DWI at 3.0T. Radiother Oncol 2014; 113: 115–20. doi: 10.1016/j.radonc.2014.07.016
  7. Thorwarth D, Ege M, Nachbar M, Mönnich D, Gani C, Zips D, et al. Quantitative magnetic resonance imaging on hybrid magnetic resonance linear accelerators: Perspective on technical and clinical validation. Phys Imaging Radiat Oncol 2020; 16: 69–73. doi: 10.1016/j.phro.2020.09.007
  8. Almansour H, Afat S, Fritz V, Schick F, Nachbar M, Thorwarth D, et al. Prospective image quality and lesion assessment in the setting of MR-guided radiation therapy of prostate cancer on an MR-linac at 1.5 T: a comparison to a standard 3 T MRI. Cancers 2021; 13: 1533. doi: 10.3390/cancers13071533
  9. Wegener D, Zips D, Thorwarth D, Weiß J, Othman AE, Grosse U, et al. Precision of T2 TSE MRI-CT-image fusions based on gold fiducials and repetitive T2 TSE MRI-MRI-fusions for adaptive IGRT of prostate cancer by using phantom and patient data. Acta Oncologica 2019; 58: 88–94. doi: 10.1080/0284186X.2018.1518594
  10. Winkel D, Bol GH, Kroon PS, van Asselen B, Hackett SS, Werensteijn-Honingh AM, et al. Adaptive radiotherapy: The Elekta Unity MR-linac concept. Clin Transl Radiat Oncol 2019; 18: 54–9. doi: 10.1016/j.ctro.2019.04.001
  11. [Guideline program oncology. Interdisciplinary guideline of quality S3 for early detection, diagnosis and therapy of the various stages of prostate carcinoma.] [German]. Deutsche Krebsgesellschaft, D.K., AWMF. 2019; Long version 5.1: 345.
  12. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol 2012; 8: 23–34. doi: 10.20982/tqmp.08.1.p023
  13. Tamada T, Sone T, Jo Y, Toshimitsu S, Yamashita T, Yamamoto A, et al. Apparent diffusion coefficient values in peripheral and transition zones of the prostate: Comparison between normal and malignant prostatic tissues and correlation with histologic grade. J Magn Reson Imaging 2008; 28: 720–6. doi: 10.1002/jmri.21503
  14. van Schie MA, van Houdt PJ, Ghobadi G, Pos FJ, Walraven I, de Boer HCJ, et al. Quantitative MRI changes during weekly ultra-hypofractionated prostate cancer radiotherapy with integrated boost. Front Oncol 2019; 9: 1264. doi: 10.3389/fonc.2019.01264
  15. Wu S, Jiao Y, Zhang Y, Ren X, Li P, Yu Q, et al. Imaging-based individualized response prediction of carbon ion radiotherapy for prostate cancer patients. Cancer Manag Res 2019; 11: 9121–31. doi: 10.2147/CMAR.S214020
  16. Abdollahi H, Mofid B, Shiri I, Razzaghdoust A, Saadipoor A, Mahdavi A, et al. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med 2019; 124: 555–67. doi: 10.1007/s11547-018-0966-4
  17. Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al. PI-RADS prostate imaging – reporting and data system: 2015, version 2. Eur Urol 2016; 69: 16–40. doi: 10.1016/j.eururo.2015.08.052
  18. Thorwarth D, Low DA. Technical challenges of real-time adaptive MR-guided radiotherapy. Front Oncol 2021; 11: 634507. doi: 10.3389/fonc.2021.634507
  19. Boeke S, Mönnich D, van Timmeren JE, Balermpas P. MR-guided radiotherapy for head and neck cancer: current developments, perspectives, and challenges. Front Oncol 2021; 11: 616156. doi: 10.3389/fonc.2021.616156
  20. Boldrini L, Intven M, Bassetti M, Valentini V, Gani C. MR-guided radiotherapy for rectal cancer: current perspective on organ preservation. Front Oncol 2021; 11: 619852. doi: 10.3389/fonc.2021.619852
  21. Yang Y, Cao M, Sheng K, Gao Y, Chen A, Kamrava M, et al. Longitudinal diffusion MRI for treatment response assessment: preliminary experience using an MRI-guided tri-cobalt 60 radiotherapy system. Med Phys 2016; 43: 1369–73. doi: 10.1118/1.4942381
  22. Shaverdian N, Yang Y, Hu P, Hart S, Sheng K, Lamb J, et al. Feasibility evaluation of diffusion-weighted imaging using an integrated MRI-radiotherapy system for response assessment to neoadjuvant therapy in rectal cancer. Br J Radiol 2017; 90: 20160739. doi: 10.1259/bjr.20160739
  23. Lawrence LSP, Chan RW, Chen H, Keller B, Stewart J, Ruschin M, et al. Accuracy and precision of apparent diffusion coefficient measurements on a 1.5 T MR-Linac in central nervous system tumour patients. Radiother Oncol 2021; 164: 155–62. doi: 10.1016/j.radonc.2021.09.020
  24. Habrich J, Boeke S, Nachbar M, Nikolaou K, Schick F, Gani C, et al. Repeatability of diffusion-weighted magnetic resonance imaging in head and neck cancer at a 1.5 T MR-Linac. Radiother Oncol 2022; 174: 141–8. doi: 10.1016/j.radonc.2022.07.020
  25. Habrich J, Boeke S, Nachbar M, Nikolaou K, Schick F, Gani C, et al. Longitudinal correlations between intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI during radiotherapy in prostate cancer patients. Front Oncol 2022; 12: 897130. doi: 10.3389/fonc.2022.897130
  26. van der Heide UA, Houweling AC, Groenendaal G, Beets-Tan RG, Lambin P. Functional MRI for radiotherapy dose painting. Magn Reson Imaging 2012; 30: 1216–23. doi: 10.1016/j.mri.2012.04.010
  27. Kooreman ES, van Houdt PJ, Keesman R, Pos FJ, van Pelt VWJ, Nowee ME, et al. ADC measurements on the Unity MR-linac - A recommendation on behalf of the Elekta Unity MR-linac consortium. Radiother Oncol 2020; 153: 106–13. doi: 10.1016/j.radonc.2020.09.046
DOI: https://doi.org/10.2478/raon-2023-0020 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Language: English
Page range: 184 - 190
Submitted on: Jan 20, 2023
Accepted on: Mar 30, 2023
Published on: Jun 21, 2023
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2023 Haidara Almansour, Fritz Schick, Marcel Nachbar, Saif Afat, Victor Fritz, Daniela Thorwarth, Daniel Zips, Felix Bertram, Arndt-Christian Müller, Konstantin Nikolaou, Ahmed E Othman, Daniel Wegener, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.