Advertisement

Automation of pencil beam scanning proton treatment planning for intracranial tumours

Published:December 17, 2022DOI:https://doi.org/10.1016/j.ejmp.2022.11.007

      Highlights

      • Comprehensive automation of intra-cranial proton treatment planning system.
      • Automatic beams’ geometry selection based on intra-cranial target localization.
      • Beams’ geometry derived from previous planning experience and heterogeneity index.
      • Wish list approach is employed to a benchmark dose distribution for plan optimization.
      • Feasibility of the system in terms of time to generate clinical plans within OARs clinical constrains.

      Abstract

      Purpose

      To evaluate the feasibility of comprehensive automation of an intra-cranial proton treatment planning.

      Materials and methods

      Class solution (CS) beam configuration selection allows the user to identify predefined beam configuration based on target localization; automatic CS (aCS) will then explore all the possible CS beam geometries. Ten patients, already used for the evaluation of the automatic selection of the beam configuration, have been also employed to training an algorithm based on the computation of a benchmark dose exploit automatic general planning solution (GPS) optimization with a wish list approach for the planning optimization. An independent cohort of ten patients has been then used for the evaluation step between the clinical and the GPS plan in terms of dosimetric quality of plans and the time needed to generate a plan.

      Results

      The definition of a beam configuration requires on average 22 min (range 9–29 min).
      The average time for GPS plan generation is 18 min (range 7–26 min). Median dose differences (GPS-Manual) for each OAR constraints are: brainstem −1.60 Gy, left cochlea −1.22 Gy, right cochlea −1.42 Gy, left eye 0.55 Gy, right eye −2.33 Gy, optic chiasm −1.87 Gy, left optic nerve −4.45 Gy, right optic nerve −2.48 Gy and optic tract −0.31 Gy. Dosimetric CS and aCS plan evaluation shows a slightly worsening of the OARs values except for the optic tract and optic chiasm for both CS and aCS, where better results have been observed.

      Conclusion

      This study has shown the feasibility and implementation of the automatic planning system for intracranial tumors. The method developed in this work is ready to be implemented in a clinical workflow.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Paganetti H.
        • Beltran C.J.
        • Both S.
        • Dong L.
        • Flanz J.B.
        • Furutani K.M.
        • et al.
        Roadmap: proton therapy physics and biology.
        Phys Med Biol. 2020; https://doi.org/10.1088/1361-6560/abcd16
      1. PTCOG - Facilities under Construction n.d. https://www.ptcog.ch/index.php/facilities-under-construction (accessed January 28, 2022).

        • Nelms B.E.
        • Robinson G.
        • Markham J.
        • Velasco K.
        • Boyd S.
        • Narayan S.
        • et al.
        Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems.
        Pract Radiat Oncol. 2012; 2: 296-305https://doi.org/10.1016/j.prro.2011.11.012
        • Bolsi A.
        • Placidi L.
        • Pica A.
        • Ahlhelm F.J.
        • Walser M.
        • Lomax A.J.
        • et al.
        Pencil beam scanning proton therapy for the treatment of craniopharyngioma complicated with radiation-induced cerebral vasculopathies: A dosimetric and linear energy transfer (LET) evaluation.
        Radiother Oncol J Eur Soc Ther Radiol Oncol. 2020; 149: 197-204https://doi.org/10.1016/j.radonc.2020.04.052
        • Lowe M.
        • Gosling A.
        • Nicholas O.
        • Underwood T.
        • Miles E.
        • Chang Y.-C.
        • et al.
        Comparing Proton to Photon Radiotherapy Plans: UK Consensus Guidance for Reporting Under Uncertainty for Clinical Trials.
        Clin Oncol R Coll Radiol G B. 2020; 32: 459-466https://doi.org/10.1016/j.clon.2020.03.014
        • Hernandez V.
        • Hansen C.R.
        • Widesott L.
        • Bäck A.
        • Canters R.
        • Fusella M.
        • et al.
        What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans.
        Radiother Oncol. 2020; 153: 26-33https://doi.org/10.1016/j.radonc.2020.09.038
        • Pallotta S.
        • Marrazzo L.
        • Calusi S.
        • Castriconi R.
        • Fiorino C.
        • Loi G.
        • et al.
        Implementation of automatic plan optimization in Italy: Status and perspectives.
        Phys Medica PM Int J Devoted Appl Phys Med Biol Off J Ital Assoc Biomed Phys AIFB. 2021; 92: 86-94https://doi.org/10.1016/j.ejmp.2021.11.013
        • Moore K.L.
        Automated Radiotherapy Treatment Planning.
        Semin Radiat Oncol. 2019; 29: 209-218https://doi.org/10.1016/j.semradonc.2019.02.003
        • Cagni E.
        • Botti A.
        • Micera R.
        • Galeandro M.
        • Sghedoni R.
        • Orlandi M.
        • et al.
        Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer.
        Phys Medica PM Int J Devoted Appl Phys Med Biol Off J Ital Assoc Biomed Phys AIFB. 2017; 36: 38-45https://doi.org/10.1016/j.ejmp.2017.03.002
        • Rago M.
        • Placidi L.
        • Polsoni M.
        • Rambaldi G.
        • Cusumano D.
        • Greco F.
        • et al.
        Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions.
        PLoS One. 2021; 16: e0245305
        • Castriconi R.
        • Cattaneo G.M.
        • Mangili P.
        • Esposito P.
        • Broggi S.
        • Cozzarini C.
        • et al.
        Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy.
        Pract Radiat Oncol. 2021; 11: e236-e244https://doi.org/10.1016/j.prro.2020.09.012
        • Kaderka R.
        • Hild S.J.
        • Bry V.N.
        • Cornell M.
        • Ray X.J.
        • Murphy J.D.
        • et al.
        Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance.
        Int J Radiat Oncol Biol Phys. 2021; 111: 705-715https://doi.org/10.1016/j.ijrobp.2021.06.028
        • Speer S.
        • Klein A.
        • Kober L.
        • Weiss A.
        • Yohannes I.
        • Bert C.
        Automation of radiation treatment planning : Evaluation of head and neck cancer patient plans created by the Pinnacle3 scripting and Auto-Planning functions.
        Strahlenther Onkol Organ Dtsch Rontgengesellschaft Al. 2017; 193: 656-665https://doi.org/10.1007/s00066-017-1150-9
        • Hazell I.
        • Bzdusek K.
        • Kumar P.
        • Hansen C.R.
        • Bertelsen A.
        • Eriksen J.G.
        • et al.
        Automatic planning of head and neck treatment plans.
        J Appl Clin Med Phys. 2016; 17: 272-282https://doi.org/10.1120/jacmp.v17i1.5901
        • Breedveld S.
        • Storchi P.R.M.
        • Keijzer M.
        • Heemink A.W.
        • Heijmen B.J.M.
        A novel approach to multi-criteria inverse planning for IMRT.
        Phys Med Biol. 2007; 52: 6339-6353https://doi.org/10.1088/0031-9155/52/20/016
      2. Wang et al. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future 2019.

        • Delaney A.R.
        • Dong L.
        • Mascia A.
        • Zou W.
        • Zhang Y.
        • Yin L.
        • et al.
        Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study.
        Cancers. 2018; 10: E420https://doi.org/10.3390/cancers10110420
        • Delaney A.R.
        • Verbakel W.F.
        • Lindberg J.
        • Koponen T.K.
        • Slotman B.J.
        • Dahele M.
        Evaluation of an Automated Proton Planning Solution.
        Cureus. 2018; 10: e3696
        • Delaney A.R.
        • Dahele M.
        • Tol J.P.
        • Kuijper I.T.
        • Slotman B.J.
        • Verbakel W.F.A.R.
        Using a knowledge-based planning solution to select patients for proton therapy.
        Radiother Oncol J Eur Soc Ther Radiol Oncol. 2017; 124: 263-270https://doi.org/10.1016/j.radonc.2017.03.020
        • van de Water S.
        • Kooy H.M.
        • Heijmen B.J.M.
        • Hoogeman M.S.
        Shortening delivery times of intensity modulated proton therapy by reducing proton energy layers during treatment plan optimization.
        Int J Radiat Oncol Biol Phys. 2015; 92: 460-468https://doi.org/10.1016/j.ijrobp.2015.01.031
        • van de Water S.
        • Kraan A.C.
        • Breedveld S.
        • Schillemans W.
        • Teguh D.N.
        • Kooy H.M.
        • et al.
        Improved efficiency of multi-criteria IMPT treatment planning using iterative resampling of randomly placed pencil beams.
        Phys Med Biol. 2013; 58: 6969-6983https://doi.org/10.1088/0031-9155/58/19/6969
        • Arts T.
        • Breedveld S.
        • de Jong M.A.
        • Astreinidou E.
        • Tans L.
        • Keskin-Cambay F.
        • et al.
        The impact of treatment accuracy on proton therapy patient selection for oropharyngeal cancer patients.
        Radiother Oncol J Eur Soc Ther Radiol Oncol. 2017; 125: 520-525https://doi.org/10.1016/j.radonc.2017.09.028
        • Bijman R.G.
        • Breedveld S.
        • Arts T.
        • Astreinidou E.
        • de Jong M.A.
        • Granton P.V.
        • et al.
        Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy.
        Acta Oncol Stockh Swed. 2017; 56: 1444-1450https://doi.org/10.1080/0284186X.2017.1355113
        • Jagt T.
        • Breedveld S.
        • van de Water S.
        • Heijmen B.
        • Hoogeman M.
        Near real-time automated dose restoration in IMPT to compensate for daily tissue density variations in prostate cancer.
        Phys Med Biol. 2017; 62: 4254-4272https://doi.org/10.1088/1361-6560/aa5c12
        • Placidi L.
        • Togno M.
        • Weber D.C.
        • Lomax A.J.
        • Hrbacek J.
        Range resolution and reproducibility of a dedicated phantom for proton PBS daily quality assurance.
        Z Med Phys. 2018; 28: 310-317https://doi.org/10.1016/j.zemedi.2018.02.001
        • Fiandra C.
        • Rossi L.
        • Alparone A.
        • Zara S.
        • Vecchi C.
        • Sardo A.
        • et al.
        Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer.
        Radiother Oncol J Eur Soc Ther Radiol Oncol. 2020; 148: 126-132https://doi.org/10.1016/j.radonc.2020.04.020
        • Fiandra C.
        • Alparone A.
        • Gallio E.
        • Vecchi C.
        • Balestra G.
        • Bartoncini S.
        • et al.
        Automated Heuristic Optimization of Prostate VMAT Treatment Planning.
        Int J Med Phys Clin Eng Radiat Oncol. 2018; 7: 414-425https://doi.org/10.4236/ijmpcero.2018.73034
        • Bijman R.
        • Sharfo A.W.
        • Rossi L.
        • Breedveld S.
        • Heijmen B.
        Pre-clinical validation of a novel system for fully-automated treatment planning.
        Radiother Oncol J Eur Soc Ther Radiol Oncol. 2021; 158: 253-261https://doi.org/10.1016/j.radonc.2021.03.003
        • Breedveld S.
        • Storchi P.R.M.
        • Voet P.W.J.
        • Heijmen B.J.M.
        iCycle: Integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans.
        Med Phys. 2012; 39: 951-963https://doi.org/10.1118/1.3676689
        • Kataria T.
        • Sharma K.
        • Subramani V.
        • Karrthick K.P.
        • Bisht S.S.
        Homogeneity Index: An objective tool for assessment of conformal radiation treatments.
        J Med Phys Assoc Med Phys India. 2012; 37: 207-213https://doi.org/10.4103/0971-6203.103606
        • Lomax A.J.
        Myths and realities of range uncertainty.
        Br J Radiol. 2020; 93: 20190582https://doi.org/10.1259/bjr.20190582
        • Alpuche Aviles J.E.
        • Cordero Marcos M.I.
        • Sasaki D.
        • Sutherland K.
        • Kane B.
        • Kuusela E.
        Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.
        J Appl Clin Med Phys. 2018; 19: 215-226https://doi.org/10.1002/acm2.12322
        • Cozzi L.
        • Vanderstraeten R.
        • Fogliata A.
        • Chang F.-L.
        • Wang P.-M.
        The role of a knowledge based dose-volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients : Training and validation of a novel commercial system.
        Strahlenther Onkol Organ Dtsch Rontgengesellschaft Al. 2021; 197: 332-342https://doi.org/10.1007/s00066-020-01664-2
        • Xu Y.
        • Cyriac J.
        • De Ornelas M.
        • Bossart E.
        • Padgett K.
        • Butkus M.
        • et al.
        Knowledge-Based Planning for Robustly Optimized Intensity-Modulated Proton Therapy of Head and Neck Cancer Patients.
        Front Oncol. 2021; 11737901https://doi.org/10.3389/fonc.2021.737901
        • Xu Y.
        • Brovold N.
        • Cyriac J.
        • Bossart E.
        • Padgett K.
        • Butkus M.
        • et al.
        Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy.
        Int J Part Ther. 2021; 8: 62-72https://doi.org/10.14338/IJPT-20-00088.1
        • Celik E.
        • Baues C.
        • Claus K.
        • Fogliata A.
        • Scorsetti M.
        • Marnitz S.
        • et al.
        Knowledge-based intensity-modulated proton planning for gastroesophageal carcinoma.
        Acta Oncol Stockh Swed. 2021; 60: 285-292https://doi.org/10.1080/0284186X.2020.1845396
        • Taasti V.T.
        • Hong L.
        • Deasy J.O.
        • Zarepisheh M.
        Automated proton treatment planning with robust optimization using constrained hierarchical optimization.
        Med Phys. 2020; 47: 2779-2790https://doi.org/10.1002/mp.14148
        • Taasti V.T.
        • Hong L.
        • Shim J.S.A.
        • Deasy J.O.
        • Zarepisheh M.
        Automating proton treatment planning with beam angle selection using Bayesian optimization.
        Med Phys. 2020; 47: 3286-3296https://doi.org/10.1002/mp.14215
        • Fan J.
        • Wang J.
        • Chen Z.
        • Hu C.
        • Zhang Z.
        • Hu W.
        Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique.
        Med Phys. 2019; 46: 370-381https://doi.org/10.1002/mp.13271
        • Ma J.
        • Nguyen D.
        • Bai T.
        • Folkerts M.
        • Jia X.
        • Lu W.
        • et al.
        A feasibility study on deep learning-based individualized 3D dose distribution prediction.
        Med Phys. 2021; 48: 4438-4447https://doi.org/10.1002/mp.15025
        • Guerreiro F.
        • Seravalli E.
        • Janssens G.O.
        • Maduro J.H.
        • Knopf A.C.
        • Langendijk J.A.
        • et al.
        Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.
        Radiother Oncol. 2021; 156: 36-42https://doi.org/10.1016/j.radonc.2020.11.026
        • Wang M.
        • Zhang Q.
        • Lam S.
        • Cai J.
        • Yang R.
        A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning.
        Front Oncol. 2020; 10580919https://doi.org/10.3389/fonc.2020.580919
        • Shiraishi S.
        • Moore K.L.
        Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy.
        Med Phys. 2016; 43: 378https://doi.org/10.1118/1.4938583
        • Nguyen D.
        • Jia X.
        • Sher D.
        • Lin M.-H.
        • Iqbal Z.
        • Liu H.
        • et al.
        3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.
        Phys Med Biol. 2019; 64065020https://doi.org/10.1088/1361-6560/ab039b
        • Barragán-Montero A.M.
        • Nguyen D.
        • Lu W.
        • Lin M.-H.
        • Norouzi-Kandalan R.
        • Geets X.
        • et al.
        Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations.
        Med Phys. 2019; 46: 3679-3691https://doi.org/10.1002/mp.13597
        • Kearney V.
        • Chan J.W.
        • Haaf S.
        • Descovich M.
        • Solberg T.D.
        DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks.
        Phys Med Biol. 2018; 63235022https://doi.org/10.1088/1361-6560/aaef74
      3. [1807.06489] Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks n.d. https://arxiv.org/abs/1807.06489 (accessed January 28, 2022).