Contouring practices and artefact management within a synthetic CT-based radiotherapy workflow for the central nervous system.
Contouring process
MR-only
Metal artefacts
Radiation therapy
Synthetic-CT
Journal
Radiation oncology (London, England)
ISSN: 1748-717X
Titre abrégé: Radiat Oncol
Pays: England
ID NLM: 101265111
Informations de publication
Date de publication:
29 Feb 2024
29 Feb 2024
Historique:
received:
04
10
2023
accepted:
19
02
2024
medline:
1
3
2024
pubmed:
1
3
2024
entrez:
29
2
2024
Statut:
epublish
Résumé
The incorporation of magnetic resonance (MR) imaging in radiotherapy (RT) workflows improves contouring precision, yet it introduces geometrical uncertainties when registered with computed tomography (CT) scans. Synthetic CT (sCT) images could minimize these uncertainties and streamline the RT workflow. This study aims to compare the contouring capabilities of sCT images with conventional CT-based/MR-assisted RT workflows, with an emphasis on managing artefacts caused by surgical fixation devices (SFDs). The study comprised a commissioning cohort of 100 patients with cranial tumors treated using a conventional CT-based/MR-assisted RT workflow and a validation cohort of 30 patients with grade IV glioblastomas treated using an MR-only workflow. A CE-marked artificial-intelligence-based sCT product was utilized. The delineation accuracy comparison was performed using dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Artefacts within the commissioning cohort were visually inspected, classified and an estimation of thickness was derived using Hausdorff distance (HD). For the validation cohort, boolean operators were used to extract artefact volumes adjacent to the target and contrasted to the planning treatment volume. The combination of high DSC (0.94) and low AHD (0.04 mm) indicates equal target delineation capacity between sCT images and conventional CT scans. However, the results for organs at risk delineation were less consistent, likely because of voxel size differences between sCT images and CT scans and absence of standardized delineation routines. Artefacts observed in sCT images appeared as enhancements of cranial bone. When close to the target, they could affect its definition. Therefore, in the validation cohort the clinical target volume (CTV) was expanded towards the bone by 3.5 mm, as estimated by HD analysis. Subsequent analysis on cone-beam CT scans showed that the CTV adjustment was enough to provide acceptable target coverage. The tested sCT product performed on par with conventional CT in terms of contouring capability. Additionally, this study provides both the first comprehensive classification of metal artefacts on a sCT product and a novel method to assess the clinical impact of artefacts caused by SFDs on target delineation. This methodology encourages similar analysis for other sCT products.
Sections du résumé
BACKGROUND
BACKGROUND
The incorporation of magnetic resonance (MR) imaging in radiotherapy (RT) workflows improves contouring precision, yet it introduces geometrical uncertainties when registered with computed tomography (CT) scans. Synthetic CT (sCT) images could minimize these uncertainties and streamline the RT workflow. This study aims to compare the contouring capabilities of sCT images with conventional CT-based/MR-assisted RT workflows, with an emphasis on managing artefacts caused by surgical fixation devices (SFDs).
METHODS
METHODS
The study comprised a commissioning cohort of 100 patients with cranial tumors treated using a conventional CT-based/MR-assisted RT workflow and a validation cohort of 30 patients with grade IV glioblastomas treated using an MR-only workflow. A CE-marked artificial-intelligence-based sCT product was utilized. The delineation accuracy comparison was performed using dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Artefacts within the commissioning cohort were visually inspected, classified and an estimation of thickness was derived using Hausdorff distance (HD). For the validation cohort, boolean operators were used to extract artefact volumes adjacent to the target and contrasted to the planning treatment volume.
RESULTS
RESULTS
The combination of high DSC (0.94) and low AHD (0.04 mm) indicates equal target delineation capacity between sCT images and conventional CT scans. However, the results for organs at risk delineation were less consistent, likely because of voxel size differences between sCT images and CT scans and absence of standardized delineation routines. Artefacts observed in sCT images appeared as enhancements of cranial bone. When close to the target, they could affect its definition. Therefore, in the validation cohort the clinical target volume (CTV) was expanded towards the bone by 3.5 mm, as estimated by HD analysis. Subsequent analysis on cone-beam CT scans showed that the CTV adjustment was enough to provide acceptable target coverage.
CONCLUSION
CONCLUSIONS
The tested sCT product performed on par with conventional CT in terms of contouring capability. Additionally, this study provides both the first comprehensive classification of metal artefacts on a sCT product and a novel method to assess the clinical impact of artefacts caused by SFDs on target delineation. This methodology encourages similar analysis for other sCT products.
Identifiants
pubmed: 38424642
doi: 10.1186/s13014-024-02422-9
pii: 10.1186/s13014-024-02422-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
27Informations de copyright
© 2024. The Author(s).
Références
Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Resonan Imaging. 2018;48(6):1468–78. https://doi.org/10.1002/jmri.26271 .
doi: 10.1002/jmri.26271
Prabhakar R, parambath Haresh K, Ganesh T, Joshi RC, Julka PK, Rath GK. Comparison of computed tomography and magnetic resonance based target volume in brain tumors. J Cancer Res Ther. 2007;3(2):121–3. https://doi.org/10.4103/0973-1482.34694 .
doi: 10.4103/0973-1482.34694
pubmed: 17998738
Ulin K, Urie MM, Cherlow JM. Results of a multi-institutional benchmark test for cranial CT/MR image registration. Int J Radiat Oncol Biol Phys. 2010;77(5):1584–9. https://doi.org/10.1016/j.ijrobp.2009.10.017 .
doi: 10.1016/j.ijrobp.2009.10.017
pubmed: 20381270
pmcid: 2906611
Bird D, Nix MG, McCallum H, Teo M, Gilbert A, Casanova N, et al. The benefit of MR-only radiotherapy treatment planning for anal and rectal cancers: a planning study. J Appl Clin Med Phys. 2021;22(11):41–53. https://doi.org/10.1002/acm2.13423 .
doi: 10.1002/acm2.13423
pubmed: 34687138
pmcid: 8598134
Owrangi AM, Greer PB, Glide-Hurst CK. MRI-only treatment planning: benefits and challenges. Phys Med Biol. 2018;63(5): 05TR01. https://doi.org/10.1088/1361-6560/aaaca4 .
doi: 10.1088/1361-6560/aaaca4
pubmed: 29393071
pmcid: 5886006
Jonsson J, Nyholm T, Söderkvist K. The rationale for MR-only treatment planning for external radiotherapy. Clin Transl Radiat Oncol. 2019;18:60–5. https://doi.org/10.1016/j.ctro.2019.03.005 .
doi: 10.1016/j.ctro.2019.03.005
pubmed: 31341977
pmcid: 6630106
Rank CM, Hünemohr N, Nagel AM, Röthke MC, Jäkel O, Greilich S. MRI-based simulation of treatment plans for ion radiotherapy in the brain region. Radiother Oncol. 2013;109(3):414–8. https://doi.org/10.1016/j.radonc.2013.10.034 .
doi: 10.1016/j.radonc.2013.10.034
pubmed: 24268939
Maspero M, Bentvelzen LG, Savenije MHF, Guerreiro F, Seravalli E, Janssens GO, et al. Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol. 2020;153:197–204. https://doi.org/10.1016/j.radonc.2020.09.029 .
doi: 10.1016/j.radonc.2020.09.029
pubmed: 32976877
Ranta I, Wright P, Suilamo S, Kemppainen R, Schubert G, Kapanen M, et al. Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain. J Appl Clin Med Phys. 2023;24(9): e14044. https://doi.org/10.1002/acm2.14044 .
doi: 10.1002/acm2.14044
pubmed: 37345212
pmcid: 10476982
Olsrud J, Lätt J, Brockstedt S, Romner B, Björkman-Burtscher IM. Magnetic resonance imaging artifacts caused by aneurysm clips and shunt valves: dependence on field strength (1.5 and 3 T) and imaging parameters. J Magn Reson Imaging. 2005;22(3):433–7. https://doi.org/10.1002/jmri.20391 .
doi: 10.1002/jmri.20391
pubmed: 16104008
Hargreaves BA, Worters PW, Pauly KB, Pauly JM, Koch KM, Gold GE. Metal-induced artifacts in MRI. Am J Roentgenol. 2011;197(3):547–55. https://doi.org/10.2214/AJR.11.7364 .
doi: 10.2214/AJR.11.7364
Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics. 2004;24(6):1679–91. https://doi.org/10.1148/rg.246045065 .
doi: 10.1148/rg.246045065
pubmed: 15537976
Jungmann PM, Agten CA, Pfirrmann CW, Sutter R. Advances in MRI around metal: MRI around metal. J Magn Reson Imaging. 2017;46(4):972–91. https://doi.org/10.1002/jmri.25708 .
doi: 10.1002/jmri.25708
pubmed: 28342291
Andersson KM, Dahlgren CV, Reizenstein J, Cao Y, Ahnesjö A, Thunberg P. Evaluation of two commercial CT metal artifact reduction algorithms for use in proton radiotherapy treatment planning in the head and neck area. Med Phys. 2018;45(10):4329–44. https://doi.org/10.1002/mp.13115 .
doi: 10.1002/mp.13115
pubmed: 30076784
Varian Medical Systems I. Image registration and segmentation algorithms reference guide. Palo Alto: Varian Medical Systems, Inc.; 2019.
Varian Medical Systems I. Image registration and segmentation instructions for use. Palo Alto: Varian Medical Systems, Inc.; 2020.
Eekers DB, in ’t Ven L, Roelofs E, Postma A, Alapetite C, Burnet NG, et al. The EPTN consensus-based atlas for CT- and MR-based contouring in neuro-oncology. Radiother Oncol. 2018;128(1):37–43. https://doi.org/10.1016/j.radonc.2017.12.013 .
doi: 10.1016/j.radonc.2017.12.013
pubmed: 29548560
Eekers DBP, Di Perri D, Roelofs E, Postma A, Dijkstra J, Ajithkumar T, et al. Update of the EPTN atlas for CT- and MR-based contouring in Neuro-Oncology. Radiother Oncol. 2021;160:259–65. https://doi.org/10.1016/j.radonc.2021.05.013 .
doi: 10.1016/j.radonc.2021.05.013
pubmed: 34015385
Hu Y, Nguyen H, Smith C, Chen T, Byrne M, Archibald-Heeren B, et al. Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning. J Appl Clin Med Phys. 2023. https://doi.org/10.1002/acm2.13949 .
doi: 10.1002/acm2.13949
pubmed: 38098227
pmcid: 10795435
Schuss P, Ulrich CT, Harter PN, Tews DS, Seifert V, Franz K. Gliosarcoma with bone infiltration and extracranial growth: case report and review of literature. J Neurooncol. 2011;103(3):765–70. https://doi.org/10.1007/s11060-010-0437-9 .
doi: 10.1007/s11060-010-0437-9
pubmed: 20957407
Edmund JM, Nyholm T. A review of substitute CT generation for MRI-only radiation therapy. Radiat Oncol. 2017;12(1):28. https://doi.org/10.1186/s13014-016-0747-y .
doi: 10.1186/s13014-016-0747-y
pubmed: 28126030
pmcid: 5270229
Johnstone E, Wyatt JJ, Henry AM, Short SC, Sebag-Montefiore D, Murray L, et al. Systematic review of synthetic computed tomography generation methodologies for use in magnetic resonance imaging-only radiation therapy. Int J Radiat Oncol Biol Phys. 2018;100(1):199–217. https://doi.org/10.1016/j.ijrobp.2017.08.043 .
doi: 10.1016/j.ijrobp.2017.08.043
pubmed: 29254773
Demol B, Boydev C, Korhonen J, Reynaert N. Dosimetric characterization of MRI-only treatment planning for brain tumors in Atlas-based pseudo-CT images generated from standard T 1-weighted MR images: MRI-only treatment planning in atlas-based pseudo-CT images. Med Phys. 2016;43(12):6557–68. https://doi.org/10.1118/1.4967480 .
doi: 10.1118/1.4967480
pubmed: 27908187
Deeley MA, Chen A, Datteri R, Noble JH, Cmelak AJ, Donnelly EF, et al. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phys Med Biol. 2011;56(14):4557–77. https://doi.org/10.1088/0031-9155/56/14/021 .
doi: 10.1088/0031-9155/56/14/021
pubmed: 21725140
pmcid: 3153124
Wong J, Fong A, McVicar N, Smith S, Giambattista J, Wells D, et al. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol. 2020;144:152–8. https://doi.org/10.1016/j.radonc.2019.10.019 .
doi: 10.1016/j.radonc.2019.10.019
pubmed: 31812930
Boulanger M, Nunes JC, Chourak H, Largent A, Tahri S, Acosta O, et al. Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med. 2021;89:265–81. https://doi.org/10.1016/j.ejmp.2021.07.027 .
doi: 10.1016/j.ejmp.2021.07.027
pubmed: 34474325
Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys. 2021;48(11):6537–66. https://doi.org/10.1002/mp.15150 .
doi: 10.1002/mp.15150
pubmed: 34407209
Rousselle A, Amelot A, Thariat J, Jacob J, Mercy G, De Marzi L, et al. Metallic implants and CT artefacts in the CTV area: Where are we in 2020? Cancer/Radiothérapie. 2020;24(6–7):658–66. https://doi.org/10.1016/j.canrad.2020.06.022 .
doi: 10.1016/j.canrad.2020.06.022
pubmed: 32859465
Palmér E, Persson E, Ambolt P, Gustafsson C, Gunnlaugsson A, Olsson LE. Cone beam CT for QA of synthetic CT in MRI only for prostate patients. J Appl Clin Med Phys. 2018;19(6):44–52. https://doi.org/10.1002/acm2.12429 .
doi: 10.1002/acm2.12429
pubmed: 30182461
pmcid: 6236859
Schulze R, Heil U, Groβ D, Bruellmann D, Dranischnikow E, Schwanecke U, et al. Artefacts in CBCT: a review. Dentomaxillofac Radiol. 2011;40(5):265–273. https://doi.org/10.1259/dmfr/30642039 .