Adaptive radiation therapy in practice: classification and technical insights from Polish Society of Medical Physics experts

• Autorzy: Piotrowski Tomasz , Zawadzka Anna , Winiecki Janusz , Dybek Marcin , Skrobała Agnieszka

Identyfikatory

DOI

10.2478/pjmpe-2025-0020

• Języki publikacji: EN

Abstrakty

EN

This educational review provides a comprehensive overview of adaptive radiation therapy (ART), an advanced approach to adjusting radiotherapy plans in response to anatomical or physiological changes during treatment. It classifies ART into three methods, i.e. offline, online, and real-time, distinguished by the timing of plan modifications relative to the treatment session. The review emphasises the importance of monitoring discrepancies between planned and delivered doses through advanced imaging and dose-tracking techniques, and how timely adaptation can exploit this information to maintain target coverage and minimise unnecessary exposure of healthy tissues. Key enabling technologies are discussed, including high-quality on-board imaging modalities (CT, cone-beam CT, MRI, PET) for accurate visualisation of current anatomy, deformable image registration and automated contour propagation for efficient mapping of anatomical changes, and reliable dose recalculation on images from the treatment machine to ensure dosimetric accuracy of adapted plans. Equally important, rigorous quality assurance (QA) protocols are outlined, including validation of image registration accuracy (to verify deformable alignment), end-to-end testing of the entire adaptive workflow, patient-specific plan verification via independent dose checks or in vivo dosimetry, and thorough risk analysis (such as failure mode and effects analysis) to anticipate potential errors. Clear adaptation criteria and multidisciplinary team oversight are recommended to ensure that adaptive interventions fulfil the promise of improved tumour control and reduced toxicity without compromising patient safety.

Słowa kluczowe

EN

adaptive radiation therapy; offline adaptation; online adaptation; real-time adaptation

• Wydawca: Polskie Towarzystwo Fizyki Medycznej
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2025
• Tom: Vol. 31, Iss. 3
• Strony: 171--177
• Opis fizyczny: Bibliogr. 40 poz., rys.

Twórcy

autor

Piotrowski Tomasz

• Department of Electroradiology, Poznan University of Medical Sciences, Poznan, Poland
• Department of Medical Physics, Greater Poland Cancer Centre, Poznan, Poland
• Department of Biomedical Physics, Adam Mickiewicz University in Poznan, Poznan, Poland

• https://orcid.org/0000-0002-8894-7412

autor

Zawadzka Anna

• Department of Medical Physics. Maria Skłodowska Curie Memorial Cancer Centre and Institute of Oncology in Warsaw, Warszawa, Poland

• https://orcid.org/0000-0003-0757-0869

autor

Winiecki Janusz

• Department of Medical Physics, Oncology Centre, Bydgoszcz, Poland
• Department of Oncology and Brachytherapy, Nicolaus Copernicus University, Torun, Poland

• https://orcid.org/0000-0001-9330-1547

autor

Dybek Marcin

• Department of Radiotherapy, Katowice Oncology Centre, Katowice, Poland

autor

Skrobała Agnieszka

• Department of Electroradiology, Poznan University of Medical Sciences, Poznan, Poland
• Department of Medical Physics, Greater Poland Cancer Centre, Poznan, Poland

Bibliografia

• 1. Hernandez V, et al. What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiother Oncol. 2020;153:26-33. doi: 10.1016/j.radonc.2020.09.038
• 2. Jaffray D. Image-guided radiotherapy: from current concept to future perspectives. Nat Rev Clin Oncol. 2012;9:688-699. doi: 10.1038/nrclinonc.2012.194
• 3. McDonald BA, et al. Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation. Front Oncol. 2023;12:1086258. doi: 10.3389/fonc.2022.1086258
• 4. Cheung J, et al. Dose Recalculation and the Dose-Guided Radiation Therapy (DGRT) Process Using Megavoltage Cone-Beam CT. Int J Radiat Oncol Biol Phys. 2009;74(2):583-592. doi: 10.1016/j.ijrobp.2008.12.034
• 5. Glide-Hurst CK, et al. Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology. Int J Radiat Oncol Biol Phys. 2021;109(4):1054-1075. doi: 10.1016/j.ijrobp.2020.10.021
• 6. Aristophanous M, et al. Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection. Int J Radiat Oncol Biol Phys. 2024;119(5):1557-1568. doi: 10.1016/j.ijrobp.2024.02.016
• 7. Green OL, et al. Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy. Semin Radiat Oncol. 2019;29(3):219-227. doi: 10.1016/j.semradonc.2019.02.004
• 8. Lu L, et al. A Review of Online Adaptive Radiation Therapy. Appl Rad Oncol. 2024;5(1): (online ahead of print). doi: 10.37549/ARO-D-24-00037
• 9. Kisling K et al. Clinical commissioning of an adaptive radiotherapy platform: Results and recommendations. J Appl Clin Med Phys. 2022;23(12):e13801. doi: 10.1002/acm2.13801.
• 10. Hindmarsh J, et al. A dosimetric comparison of helical tomotherapy treatment delivery with real-time adaptation and no motion correction. Phys Imaging Radiat Oncol. 2025;34:100741. doi: 10.1016/j.phro.2025.100741
• 11. Czajkowski P, et al. Registration methods in radiotherapy. Rep Pract Oncol Radiother. 2019;24:28-34. doi: 10.1016/j.rpor.2018.09.004
• 12. Lavrova E, et al. Adaptive Radiation Therapy: A Review of CT-based Techniques. Radiol Imaging Cancer. 2023;5(4):e230011. doi: 10.1148/rycan.230011
• 13. Posiewnik M, et al. A review of cone-beam CT applications for adaptive radiotherapy of prostate cancer. Phys Med. 2019;59:13-21. doi: 10.1016/j.ejmp.2019.02.014
• 14. Ruchala K, et al. Megavoltage CT on a tomotherapy system. Phys Med Biol. 1999;44(10):2597-2621. doi: 10.1088/0031-9155/44/10/316
• 15. Tegtmeier RC, et al. Characterization of imaging performance of a novel helical kVCT for use in image-guided and adaptive radiotherapy. J Appl Clin Med Phys. 2022;23(6):e13648. doi: 10.1002/acm2.13648
• 16. Ng J, et al. MRI-LINAC: A transformative technology in radiation oncology. Front Oncol. 2023;13:1117874. doi: 10.3389/fonc.2023.1117874
• 17. Oderinde OM, et al. The technical design and concept of a PET/CT linac for biology-guided radiotherapy. Clin Transl Radiat Oncol. 2021;29:106-112. doi: 10.1016/j.ctro.2021.04.003
• 18. Onal C, et al. Dosimetric evaluation of ultra fractionated dose escalation with simultaneous integrated boost to intraprostatic lesion using 1.5-Tesla MR-Linac in localized prostate cancer. Rep Pract Oncol Radiother. 2024;29(1):10-20. doi: 10.5603/rpor.99358.
• 19. Ladbury C, et al. Biology-Guided Radiation Therapy: An Evolving Treatment Paradigm. Surg Oncol Clin N Am. 2023;32(3):553-568. doi: 10.1016/j.soc.2023.02.006
• 20. Datta A, et al. Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions. Clin Oncol. 2018; 30(11):702-710. doi: 10.1016/j.clon.2018.08.005
• 21. Wu C, et al. Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system. Phys Med Biol. 2023;68(18): (epub ahead of print). doi: 10.1088/1361-6560/acf3cc
• 22. Chen GP, et al. Clinical Implementation and Initial Experience of Real-Time Motion Tracking With Jaws and Multileaf Collimator During Helical Tomotherapy Delivery. Pract Radiat Oncol. 2021;11(5):e486–e495. doi: 10.1016/j.prro.2021.01.010
• 23. Giacometti V, et al. An evaluation of techniques for dose calculation on cone-beam computed tomography. Br J Radiol. 2019; 92(1096):20180383. doi: 10.1259/bjr.20180383
• 24. Richter A, et al. Investigation of the usability of cone-beam CT data sets for dose calculation. Radiat Oncol. 2008;3:42. doi: 10.1186/1748-717X-3-42
• 25. Hu Y, et al. Synthetic CT generation based on CBCT using improved vision transformer CycleGAN. Sci Rep. 2024;14:11455. doi: 10.1038/s41598-024-61492-7
• 26. Hsu SH, et al. Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer. Front Oncol. 2022;12:969463. doi: 10.3389/fonc.2022.969463
• 27. Acquah IK, et al. A systematic review of deep learning techniques for generating synthetic CT images from MRI data. Pol J Med Phys Eng. 2025;31(1):20-38. doi: 10.2478/pjmpe-2025-0003
• 28. Simon A, et al. Roles of deformable image registration in adaptive RT: From contour propagation to dose monitoring. Proc IEEE Eng Med Biol Soc. 2015;2015:5215-5218. doi: 10.1109/EMBC.2015.7319567
• 29. Brock KK, et al. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys. 2017;44(7):e43-e76. doi: 10.1002/mp.12256
• 30. Latifi K, et al. Practical quantification of image registration accuracy following the AAPM TG-132 report framework. J Appl Clin Med Phys. 2018; 19(4):125-133. doi: 10.1002/acm2.12348
• 31. Zhong H, et al. Deformable dose accumulation is required for adaptive radiotherapy practice. J Appl Clin Med Phys. 2024;25(8):e14457. doi: 10.1002/acm2.14457
• 32. Lim SY, et al. Dose accumulation of daily adaptive plans to decide optimal plan adaptation strategy for head-and-neck patients treated with MR-Linac. Med Dosim. 2022;47(1):103-109. doi: 10.1016/j.meddos.2021.08.005
• 33. Klein EE, et al. Task Group 142 report: quality assurance of medical accelerators. Med Phys. 2009 36(9):4197-4212. doi: 10.1118/1.3190392
• 34. de Pooter J, et al. Reference dosimetry in MRI-linacs: evaluation of available protocols and data to establish a Code of Practice. Phys Med Biol. 2021;66(5):05TR02. doi: 10.1088/1361-6560/ab9efe
• 35. Schreiner LJ. End-to-end QA in image-guided and adaptive radiation therapy. J Phys Conf Ser. 2019;1305:012062. doi: 10.1088/1742-6596/1305/1/012062
• 36. Nishioka S, et al. Identifying risk characteristics using failure mode and effect analysis for risk management in online MR-guided adaptive radiation therapy. Phys Imaging Radiat Oncol. 2022;23:1-7. doi: 10.1016/j.phro.2022.06.002
• 37. Wegener S, et al. Re-evaluation of the prospective risk analysis for AI-driven CBCT-based online adaptive radiotherapy after one year of clinical experience. Z Med Phys. 2024; 34(3):397-407. doi: 10.1016/j.zemedi.2024.05.001
• 38. Zhao X, et al. Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos. J Appl Clin Med Phys. 2023;24(2):e13876. doi: 10.1002/acm2.13876
• 39. Olaciregui-Ruiz I, et al. Automatic dosimetric verification of online adapted plans on the Unity MR-Linac using 3D EPID dosimetry. Radiother Oncol. 2021;157:241-246. doi: 10.1016/j.radonc.2021.01.037
• 40. Tan X, et al. Fractional dose verification of intensity-modulated radiotherapy for cervical cancer based on exit fluences and log files. J Rad Res Appl Sci. 2023;16(1):100489. doi: 10.1016/j.jrras.2022.100489