Skip to main content
Log in

Treatment Planning: comparing techniques and standards

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Purpose

To highlight merits and pitfalls of treatment plan comparison between photon-based conventional radiation therapy (XRT) and particle-based (proton and carbon ions) therapy (PT).

Methods

The physical and dosimetric rationales for the use of PT will be explored looking at their advantages and disadvantages with a specific focus on sensitivity to range and biological uncertainties. Next, the analysis will focus on linear energy transfer (LET) and its correlation with relative biological effectiveness (RBE), particularly within the context of plan optimization and evaluation phases in proton therapy. This examination aims to address the impact of the LET/RBE interplay on radiobiological uncertainties.

Results

There is a wide literature of planning studies comparing PT and the most advanced XRT techniques. Purely dosimetric advantages are translated into normal tissue complication probability (NTCP) variation to drive patient optimal allocation, but the impact of range uncertainty needs to be accounted for, together with the consequent need for plan adaptation and organ motion management. RBE modelling and entailed clinical effects are currently central in PT research, while new degrees of freedom are being introduced with LET-based plan optimization and evaluation.

Conclusions

PT shows many dosimetric advantages but emphasizes challenges like range uncertainty. RBE modelling plays a crucial role, and LET-based optimization introduces new possibilities. Addressing uncertainties and embracing innovation are vital for enhancing PT’s efficacy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The data supporting the findings of this study can be accessed from the corresponding author upon a reasonable request. However, please note that restrictions may apply due to the potential inclusion of information that could compromise the privacy of participants.

References

  1. Rackwitz T, Debus J. Clinical applications of proton and carbon ion therapy, Seminars in Oncology, vol. 46, no. 3. W.B. Saunders, pp. 226–232, Jun. 2019, https://doi.org/10.1053/j.seminoncol.2019.07.005.

  2. Paganetti H, et al. Roadmap: Proton therapy physics and biology. Phys Med Biol. 2021;66(5). https://doi.org/10.1088/1361-6560/abcd16.

  3. Durante M, Paganetti H. Nuclear physics in particle therapy: a review. Rep Prog Phys. 2016;79(9). https://doi.org/10.1088/0034-4885/79/9/096702.

  4. Paganetti H. Mechanisms and review of clinical evidence of variations in relative Biological effectiveness in Proton Therapy. Int J Radiat Oncol Biol Phys. 2022;112(1):222–36. https://doi.org/10.1016/j.ijrobp.2021.08.015.

    Article  Google Scholar 

  5. Kramer M, Scholz M. Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose. Phys Med Biol. 2000;45(11):3319–30. https://doi.org/10.1088/0031-9155/45/11/314.

    Article  Google Scholar 

  6. Inaniwa T, et al. Treatment planning for a scanned carbon beam with a modified microdosimetric kinetic model. Phys Med Biol. 2010;55(22):6721–37. https://doi.org/10.1088/0031-9155/55/22/008.

    Article  Google Scholar 

  7. Tinganelli W, Durante M. Carbon ion radiobiology, vol. 12, no. 10. 2020.

  8. Visser S, et al. Robustness assessment of clinical adaptive proton and photon radiotherapy for oesophageal cancer in the model-based approach. Radiother Oncol. 2022;177:197–204. https://doi.org/10.1016/j.radonc.2022.11.001.

    Article  Google Scholar 

  9. Hansen CR et al. Evaluation of decentralised model-based selection of head and neck cancer patients for a proton treatment study. DAHANCA 35, Radiother. Oncol, no. xxxx, p. 109812, 2023, https://doi.org/10.1016/j.radonc.2023.109812.

  10. Vai A, et al. Proton Radiation Therapy for Nasopharyngeal Cancer patients: dosimetric and NTCP evaluation supporting clinical decision. Cancers (Basel). 2022;14(5):1–12. https://doi.org/10.3390/cancers14051109.

    Article  Google Scholar 

  11. Tambas M et al. November., First experience with model-based selection of head and neck cancer patients for proton therapy, Radiother. Oncol, vol. 151, no. 2017, pp. 206–213, 2020, https://doi.org/10.1016/j.radonc.2020.07.056.

  12. Mirandola A, et al. A patient selection Approach based on NTCP Models and DVH parameters for definitive Proton Therapy in locally Advanced Sinonasal Cancer patients. Cancers (Basel). 2022;14(11). https://doi.org/10.3390/cancers14112678.

  13. Stick LB, et al. Radiation-Induced toxicity risks in Photon Versus Proton Therapy for Synchronous bilateral breast Cancer. Int J Part Ther. 2022;8(4):1–13. https://doi.org/10.14338/IJPT-21-00023.1.

    Article  Google Scholar 

  14. Scorsetti M, et al. Intensity modulated proton therapy compared to volumetric modulated arc therapy in the irradiation of young female patients with hodgkin’s lymphoma. Assessment of risk of toxicity and secondary cancer induction. Radiat Oncol. 2020;15(1):1–12. https://doi.org/10.1186/s13014-020-1462-2.

    Article  Google Scholar 

  15. Boersma LJ, et al. Model-based selection for Proton Therapy in breast Cancer: development of the National Indication Protocol for Proton Therapy and First Clinical experiences. Clin Oncol. 2022;34(4):247–57. https://doi.org/10.1016/j.clon.2021.12.007.

    Article  Google Scholar 

  16. Berrington de González A, et al. The Pediatric Proton and Photon Therapy Comparison Cohort: Study Design for a Multicenter Retrospective Cohort to investigate subsequent cancers after Pediatric Radiation Therapy. Adv Radiat Oncol. 2023;8(6). https://doi.org/10.1016/j.adro.2023.101273.

  17. Hamming-Vrieze O, et al. Impact of setup and range uncertainties on TCP and NTCP following VMAT or IMPT of oropharyngeal cancer patients. Phys Med Biol. 2019;64(9). https://doi.org/10.1088/1361-6560/ab1459.

  18. Paganetti H, Botas P, Sharp GC, Winey B. Adaptive proton therapy. Phys Med Biol. 2021;66(22). https://doi.org/10.1088/1361-6560/ac344f.

  19. Fossati P, et al. Dose prescription in carbon ion radiotherapy: a planning study to compare NIRS and LEM approaches with a clinically-oriented strategy. Phys Med Biol. 2012;57(22):7543–54. https://doi.org/10.1088/0031-9155/57/22/7543.

    Article  Google Scholar 

  20. Paganetti H, et al. Report of the AAPM TG-256 on the relative biological effectiveness of proton beams in radiation therapy. Med Phys. 2019;46(3):e53–78. https://doi.org/10.1002/mp.13390.

    Article  Google Scholar 

  21. Molinelli S et al. RBE-weighted dose in carbon ion therapy for ACC patients: Impact of the RBE model translation on treatment outcomes, Radiother. Oncol, vol. 141, no. xxxx, pp. 227–233, 2019, https://doi.org/10.1016/j.radonc.2019.08.022.

  22. Molinelli S, et al. How LEM-based RBE and dose-averaged LET affected clinical outcomes of sacral chordoma patients treated with carbon ion radiotherapy: RBE and LET in carbon ion therapy for sacral chordoma. Radiother Oncol. 2021;163:209–14. https://doi.org/10.1016/j.radonc.2021.08.024.

    Article  Google Scholar 

  23. Molinelli S, et al. Dose prescription in carbon ion radiotherapy: how to compare two different RBE-weighted dose calculation systems. Radiother Oncol. 2016;120(2):307–12. https://doi.org/10.1016/j.radonc.2016.05.031.

    Article  Google Scholar 

  24. Dale JE, et al. Optic nerve constraints for carbon ion RT at CNAO – reporting and relating outcome to European and Japanese RBE. Radiother Oncol. 2019;140:175–81. https://doi.org/10.1016/j.radonc.2019.06.028.

    Article  Google Scholar 

  25. Dale JE et al. Brainstem NTCP and Dose Constraints for Carbon Ion RT—Application and Translation From Japanese to European RBE-Weighted Dose, Front. Oncol, vol. 10, no. November, pp. 1–11, 2020, https://doi.org/10.3389/fonc.2020.531344.

  26. Deng W, et al. A critical review of LET-based intensity- modulated proton therapy plan evaluation and optimization for head and neck cancer management. Int J Part Ther. 2021;8(1):36–49. https://doi.org/10.14338/IJPT-20-00049.1.

    Article  Google Scholar 

  27. Hahn C, et al. Comparing biological effectiveness guided plan optimization strategies for cranial proton therapy: potential and challenges. Radiat Oncol. 2022;17(1):1–13. https://doi.org/10.1186/s13014-022-02143-x.

    Article  MathSciNet  Google Scholar 

  28. Magro G, et al. Dosimetric validation of a GPU-based dose engine for a fast in silico patient-specific quality assurance program in light ion beam therapy. Med Phys. 2022;49(12):7802–14. https://doi.org/10.1002/mp.16002.

    Article  Google Scholar 

  29. Kalholm F, Grzanka L, Traneus E, Bassler N. A systematic review on the usage of averaged LET in radiation biology for particle therapy. Radiother Oncol. 2021;161:211–21. https://doi.org/10.1016/j.radonc.2021.04.007.

    Article  Google Scholar 

  30. Unkelbach J, et al. Robust radiotherapy planning. Phys Med Biol. 2018;63(22). https://doi.org/10.1088/1361-6560/aae659.

  31. Molinelli S et al. November., The role of multiple anatomical scenarios in plan optimization for carbon ion radiotherapy of pancreatic cancer: Inter-fraction robustness in CIRT for pancreatic cancer, Radiother. Oncol, vol. 176, no. 2019, pp. 1–8, 2022, https://doi.org/10.1016/j.radonc.2022.09.005.

  32. Li H, Durante M, Jäkel O, Kong L, Lu L. Editorial: image-guided particle therapy. Front Oncol. 2023;13:10–2. https://doi.org/10.3389/fonc.2023.1175511.

    Article  Google Scholar 

  33. Mori S, Knopf A, Umegaki K. Motion management in particle therapy. Med Phys. Nov. 2018;45(11):e994–1010. https://doi.org/10.1002/mp.12679.

  34. Hagiwara Y, et al. Efficacy and feasibility of re-irradiation using carbon ions for pancreatic cancer that recurs after carbon-ion radiotherapy. Clin Transl Radiat Oncol. 2021;26:24–9. https://doi.org/10.1016/j.ctro.2020.10.007.

    Article  Google Scholar 

  35. Matsumoto S, et al. Unresectable chondrosarcomas treated with carbon ion radiotherapy: relationship between dose-averaged linear energy transfer and local recurrence. Anticancer Res. 2020;40(11):6429–35. https://doi.org/10.21873/anticanres.14664.

    Article  Google Scholar 

  36. Nenoff L, et al. Experimental validation of daily adaptive proton therapy. Phys Med Biol. 2021;66(20). https://doi.org/10.1088/1361-6560/ac2b84.

  37. Placidi L, et al. Effect of anatomic changes on Pencil Beam scanned Proton Dose distributions for cranial and extracranial tumors. Int J Radiat Oncol Biol Phys. 2017;97(3):616–23. https://doi.org/10.1016/j.ijrobp.2016.11.013.

    Article  Google Scholar 

  38. Giantsoudi D, Adams J, MacDonald SM, Paganetti H. Proton Treatment Techniques for Posterior Fossa Tumors: consequences for Linear Energy transfer and dose-volume parameters for the Brainstem and organs at Risk. Int J Radiat Oncol Biol Phys. 2017;97(2):401–10. https://doi.org/10.1016/j.ijrobp.2016.09.042.

    Article  Google Scholar 

  39. MacDonald SM, Laack NN, Terezakis S. Humbling advances in Technology: Protons, Brainstem Necrosis, and the self-driving Car. Int J Radiat Oncol Biol Phys. 2017;97(2):216–9. https://doi.org/10.1016/j.ijrobp.2016.08.001.

    Article  Google Scholar 

  40. Niemierko A, et al. Brain necrosis in adult patients after Proton Therapy: is there evidence for dependency on Linear Energy transfer? Int J Radiat Oncol Biol Phys. 2021;109(1):109–19. https://doi.org/10.1016/j.ijrobp.2020.08.058.

    Article  Google Scholar 

  41. Bauer J, Bahn E, Harrabi S, Herfarth K, Debus J, Alber M. How can scanned proton beam treatment planning for low-grade glioma cope with increased distal RBE and locally increased radiosensitivity for late MR-detected brain lesions? Med Phys. 2021;48(4):1497–507. https://doi.org/10.1002/mp.14739.

    Article  Google Scholar 

  42. Underwood TSA, McNamara AL, Appelt A, Haviland JS, Sørensen BS, Troost EGC. A systematic review of clinical studies on variable proton relative biological effectiveness (RBE). Radiother Oncol. 2022;175:79–92. https://doi.org/10.1016/j.radonc.2022.08.014.

    Article  Google Scholar 

  43. Traneus E, Ödén J. Introducing Proton Track-End objectives in Intensity Modulated Proton Therapy Optimization to Reduce Linear Energy Transfer and relative Biological effectiveness in critical structures. Int J Radiat Oncol Biol Phys. 2019;103(3):747–57. https://doi.org/10.1016/j.ijrobp.2018.10.031.

    Article  Google Scholar 

  44. Choi K, et al. Rectum dose constraints for carbon ion therapy: relative biological effectiveness model dependence in relation to clinical outcomes. Cancers (Basel). 2020;12(1). https://doi.org/10.3390/cancers12010046.

  45. Held T, et al. Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial. Radiat Oncol. 2022;17(1):1–12. https://doi.org/10.1186/s13014-022-02093-4.

    Article  MathSciNet  Google Scholar 

  46. Mein S et al. Assessment of RBE-Weighted Dose Models for Carbon Ion Therapy Toward Modernization of Clinical Practice at HIT: In Vitro, in Vivo, and in Patients, Int. J. Radiat. Oncol. Biol. Phys, vol. 108, no. 3, pp. 779–791, 2020, https://doi.org/10.1016/j.ijrobp.2020.05.041.

  47. Liermann J, Naumann P, Weykamp F, Hoegen P, Debus J, Herfarth K. Effectiveness of Carbon Ion Radiation in Locally Advanced Pancreatic Cancer, Front. Oncol, vol. 11, no. July, pp. 1–9, 2021, https://doi.org/10.3389/fonc.2021.708884.

  48. Bassler N, Jäkel O, Søndergaard CS, Petersen JB. Dose-and LET-painting with particle therapy. Acta Oncol (Madr). 2010;49(7):1170–6. https://doi.org/10.3109/0284186X.2010.510640.

    Article  Google Scholar 

  49. Inaniwa T, Kanematsu N, Noda K, Kamada T. Treatment planning of intensity modulated composite particle therapy with dose and linear energy transfer optimization. Phys Med Biol. 2017. https://doi.org/10.1088/1361-6560/aa68d7.

    Article  Google Scholar 

  50. Mein S, et al. Spot-scanning Hadron Arc (SHArc) Therapy: a Study with Light and Heavy ions. Adv Radiat Oncol. 2021;6(3):100661. https://doi.org/10.1016/j.adro.2021.100661.

    Article  Google Scholar 

  51. Fredriksson A, Glimelius L, Bokrantz R. The LET trilemma: conflicts between robust target coverage, uniform dose, and dose-averaged LET in carbon therapy. Med Phys. 2023;50(12):7338–48. https://doi.org/10.1002/mp.16771.

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partly founded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101008548 (HITRIplus).

Funding

The authors declare that no funds, grants, or any other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SM, GM and AV. The first draft of the manuscript was written by SM and all authors commented on previous versions of the manuscript. Further revisions have been managed by GM. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Giuseppe Magro.

Ethics declarations

Competing interest

The authors have no competing interests to disclose.

Ethical approval

No ethical approval was required for this article.

Consent to participate

No consent to participate was required for this article.

Consent to publish

The authors confirm that human participants provided informed consent for publication of the images in Figs. 1 and 2.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molinelli, S., Mirandola, A., Magro, G. et al. Treatment Planning: comparing techniques and standards. Health Technol. (2024). https://doi.org/10.1007/s12553-024-00845-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12553-024-00845-8

Keywords

Navigation