Comparative analysis of delivered and planned doses in target volumes for lung stereotactic ablative radiotherapy.

Humans Lung Neoplasms / radiotherapy Radiosurgery / methods Radiotherapy Planning, Computer-Assisted / methods Retrospective Studies Carcinoma, Non-Small-Cell Lung / radiotherapy Radiotherapy Dosage Cone-Beam Computed Tomography Female Male Aged Middle Aged Aged, 80 and over Radiotherapy, Intensity-Modulated / methods
Adaptive CT Deformable image registration Dose evaluation Lung SABR

Journal

Radiation oncology (London, England)
ISSN: 1748-717X
Titre abrégé: Radiat Oncol
Pays: England
ID NLM: 101265111

Informations de publication

Date de publication:
16 Aug 2024
Historique:
received: 22 03 2024
accepted: 08 08 2024
medline: 17 8 2024
pubmed: 17 8 2024
entrez: 16 8 2024
Statut: epublish

Résumé

Adaptive therapy has been enormously improved based on the art of generating adaptive computed tomography (ACT) from planning CT (PCT) and the on-board image used for the patient setup. Exploiting the ACT, this study evaluated the dose delivered to patients with non-small-cell lung cancer (NSCLC) patients treated with stereotactic ablative radiotherapy (SABR) and derived relationship between the delivered dose and the parameters obtained through the evaluation procedure. SABR treatment records of 72 patients with NSCLC who were prescribed a dose of 60 Gy (D The prescribed dose was confirmed to be fully delivered to internal target volume (ITV) within maximum difference of 1%, and the difference between the planned and delivered doses to the PTV was agreed within 6% for more than 95% of the ACT cases. Volume changes of the ITV during the treatment course were observed to be minor in comparison of their standard deviations. Multiple linear regression analysis between the obtained parameters and the dose delivered to 95% volume of the PTV (D Evaluation of the dose delivered to patients with NSCLC treated with SABR using ACTs confirmed that the prescribed dose was accurately delivered to the ITV. However, for the PTV, certain ACT cases characterised by high HI deviations from the original plan demonstrated variations in the delivered dose. These variations may potentially arise from factors such as patient setup during treatment, as suggested by the statistical analyses of the parameters obtained from the dose evaluation process.

Sections du résumé

BACKGROUND BACKGROUND
Adaptive therapy has been enormously improved based on the art of generating adaptive computed tomography (ACT) from planning CT (PCT) and the on-board image used for the patient setup. Exploiting the ACT, this study evaluated the dose delivered to patients with non-small-cell lung cancer (NSCLC) patients treated with stereotactic ablative radiotherapy (SABR) and derived relationship between the delivered dose and the parameters obtained through the evaluation procedure.
METHODS METHODS
SABR treatment records of 72 patients with NSCLC who were prescribed a dose of 60 Gy (D
RESULTS RESULTS
The prescribed dose was confirmed to be fully delivered to internal target volume (ITV) within maximum difference of 1%, and the difference between the planned and delivered doses to the PTV was agreed within 6% for more than 95% of the ACT cases. Volume changes of the ITV during the treatment course were observed to be minor in comparison of their standard deviations. Multiple linear regression analysis between the obtained parameters and the dose delivered to 95% volume of the PTV (D
CONCLUSIONS CONCLUSIONS
Evaluation of the dose delivered to patients with NSCLC treated with SABR using ACTs confirmed that the prescribed dose was accurately delivered to the ITV. However, for the PTV, certain ACT cases characterised by high HI deviations from the original plan demonstrated variations in the delivered dose. These variations may potentially arise from factors such as patient setup during treatment, as suggested by the statistical analyses of the parameters obtained from the dose evaluation process.

Identifiants

DOI: 10.1186/s13014-024-02505-7 PMID: 39152502
pubmed: 39152502
doi: 10.1186/s13014-024-02505-7
pii: 10.1186/s13014-024-02505-7
doi:

Types de publication

Journal Article Comparative Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

110

Subventions

Organisme : SNUH Research Fund
ID : 0320232010
Organisme : National Research Foundation of Korea
ID : RS-2023-00211810

Informations de copyright

© 2024. The Author(s).

Références

Benedict SH, Yenice KM, Followill D, et al. Stereotactic body radiation therapy: the report of AAPM Task Group 101. Med Phys. 2010;37:4078.
pubmed: 20879569 doi: 10.1118/1.3438081
Kestin L, Grills I, Guckenberger M, et al. Dose–response relationship with clinical outcome for lung stereotactic body radiotherapy (SBRT) delivered via online image guidance. Radiother Oncol. 2014;110:499.
pubmed: 24630539 doi: 10.1016/j.radonc.2014.02.002
Onishi H, Shirato H, Nagata Y, et al. Hypofractionated stereotactic Radiotherapy (HypoFXSRT) for stage I non-small cell Lung Cancer: updated results of 257 patients in a Japanese multi-institutional study. J Thorac Oncol. 2007;2:S94.
pubmed: 17603311 doi: 10.1097/JTO.0b013e318074de34
Dunlap NE, Larner JM, Read PW, et al. Size matters: a comparison of T1 and T2 peripheral non–small-cell lung cancers treated with stereotactic body radiation therapy (SBRT). J Thorac Cardiovasc Surg. 2010;140:583.
pubmed: 20478576 doi: 10.1016/j.jtcvs.2010.01.046
Zhang R, Kang J, Ren S, et al. Comparison of stereotactic body radiotherapy and radiofrequency ablation for early-stage non-small cell lung cancer: a systematic review and meta-analysis. Ann Transl Med. 2022;10:104.
pubmed: 35282118 pmcid: 8848429 doi: 10.21037/atm-21-6256
Chang JY, Mehran RJ, Feng L, et al. Stereotactic ablative radiotherapy for operable stage I non-small-cell lung cancer (revised STARS): long-term results of a single-arm, prospective trial with prespecified comparison to surgery. Lancet Oncol. 2021;22:1448.
pubmed: 34529930 pmcid: 8521627 doi: 10.1016/S1470-2045(21)00401-0
Chang JY, Senan S, Paul MA, et al. Stereotactic ablative radiotherapy versus lobectomy for operable stage I non-small-cell lung cancer: a pooled analysis of two randomised trials. Lancet Oncol. 2015;16:630.
pubmed: 25981812 pmcid: 4489408 doi: 10.1016/S1470-2045(15)70168-3
Murray P, Franks K, Hanna GG. A systematic review of outcomes following stereotactic ablative radiotherapy in the treatment of early-stage primary lung cancer. Br J Radiol. 2017;90:20160732.
pubmed: 27885858 pmcid: 5601509 doi: 10.1259/bjr.20160732
Siewerdsen J, Jaffray D. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys. 2001;28:220.
pubmed: 11243347 doi: 10.1118/1.1339879
Jarry J, Graham SA, Moseley DJ, et al. Characterization of scattered radiation in kV CBCT images using Monte Carlo simulations. Med Phys. 2006;33:4320.
pubmed: 17153411 doi: 10.1118/1.2358324
Poludniowski G, Evans PM, Kavanagh A, et al. Removal and effects of scatter-glare in cone-beam CT with an amorphous-silicon flat-panel detector. Phys Med Biol. 2011;56:1837.
pubmed: 21358018 doi: 10.1088/0031-9155/56/6/019
Siewerdsen JH, Jaffray DA. A ghost story: spatio-temporal response characteristics of an indirect-detection flat-panel imager. Med Phys. 1999;26:1624.
pubmed: 10501063 doi: 10.1118/1.598657
Herman GT. Correction for beam hardening in computed tomography. Phys Med Biol. 1979;24:81.
pubmed: 432276 doi: 10.1088/0031-9155/24/1/008
Thing RS, Bernchou U, Mainegra-Hing E, et al. Hounsfield unit recovery in clinical cone beam CT images of the thorax acquired for image guided radiation therapy. Phys Med Biol. 2016;61:5781.
pubmed: 27405692 doi: 10.1088/0031-9155/61/15/5781
Kaplan LP, Elstrom UV, Molle DS, et al. Cone beam CT based dose calculation in the thorax region. Phys Imaging Radiat Oncol. 2018;7:45.
pubmed: 33458404 pmcid: 7807659 doi: 10.1016/j.phro.2018.09.001
Thing RS, Nilsson R, Andersson S, et al. Evaluation of CBCT based dose calculation in the thorax and pelvis using two generic algorithms. Phys Med. 2022;103:157.
pubmed: 36327677 doi: 10.1016/j.ejmp.2022.10.012
Richter A, Hu Q, Steglich D, et al. Investigation of the usability of conebeam CT data sets for dose calculation. Radiat Oncol. 2008;3:42.
pubmed: 19087250 pmcid: 2648965 doi: 10.1186/1748-717X-3-42
Elstrøm UV, Olsen SRK, Muren LP, et al. The impact of CBCT reconstruction and calibration for radiotherapy planning in the head and neck region - a phantom study. Acta Oncol. 2014;53:1114.
pubmed: 24975372 doi: 10.3109/0284186X.2014.927073
Holm AIS, Nyeng TB, Møller DS, et al. Density calibrated cone beam CT as a tool for adaptive radiotherapy. Acta Oncol. 2021;60:1275.
pubmed: 34224288 doi: 10.1080/0284186X.2021.1945678
Fotina I, Hopfgartner J, Stock M, et al. Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques. Radiother Oncol. 2012;104:249.
pubmed: 22809588 doi: 10.1016/j.radonc.2012.06.007
Thing RS, Bernchou U, Hansen O, et al. Accuracy of dose calculation based on artefact corrected Cone Beam CT images of lung cancer patients. Phys Imaging Radiat Oncol. 2017;1:6.
doi: 10.1016/j.phro.2016.11.001
Kidar HS, Azizi H. Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: evaluation of a hybrid correction approach. Phys Med. 2020;69:269.
pubmed: 31927264 doi: 10.1016/j.ejmp.2020.01.002
Hansen DC, Landry G, Kamp F, et al. ScatterNet: a convolutional neural network for cone-beam CT intensity correction. Med Phys. 2018;45:4916.
pubmed: 30199101 doi: 10.1002/mp.13175
Rossi M, Cerveri P. Comparison of supervised and unsupervised approaches for the Generation of Synthetic CT from Cone-Beam CT. Diagnostics. 2021;11:1435.
pubmed: 34441369 pmcid: 8395013 doi: 10.3390/diagnostics11081435
Spadea MF, Maspero M, Zaffino P, et al. Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys. 2021;48:6537.
pubmed: 34407209 doi: 10.1002/mp.15150
Onozato Y, Kadoya N, Fujita Y, et al. Evaluation of On-Board kV Cone Beam computed tomography–based dose calculation with deformable image Registration using Hounsfield Unit modifications. Int J Radiat Oncol Biol Phys. 2014;89:416.
pubmed: 24685445 doi: 10.1016/j.ijrobp.2014.02.007
Veiga C, Janssens G, Teng C-L, et al. First clinical investigation of cone beam computed tomography and deformable registration for adaptive proton therapy for lung cancer. Int J Radiat Oncol Biol Phys. 2016;95:549.
pubmed: 27084664 doi: 10.1016/j.ijrobp.2016.01.055
Marchant TE, Joshi KD, Moore CJ. Accuracy of radiotherapy dose calculations based on cone-beam CT: comparison of deformable registration and image correction based methods. Phys Med Biol. 2018;63:065003.
pubmed: 29461255 doi: 10.1088/1361-6560/aab0f0
Oh S, Kim S. Deformable image registration in radiation therapy. Radiat Oncol J. 2017;35:101.
pubmed: 28712282 pmcid: 5518453 doi: 10.3857/roj.2017.00325
Hoffmann C, Krause S, Stoiber EM, et al. Accuracy quantification of a deformable image registration tool applied in a clinical setting. J Appl Clin Med Phys. 2014;15:237.
pmcid: 5711221 doi: 10.1120/jacmp.v15i1.4564
He Y, Cazoulat G, Wu C, et al. Geometric and dosimetric accuracy of deformable image registration between average-intensity images for 4DCT-based adaptive radiotherapy for non-small cell lung cancer. J Appl Clin Med Phys. 2021;22:156.
pubmed: 34310827 pmcid: 8364273 doi: 10.1002/acm2.13341
Sarrut D, Baudier T, Ayadi M, et al. Deformable image registration applied to lung SBRT: usefullness and limitations. Phys Med. 2017;44:108.
pubmed: 28947188 doi: 10.1016/j.ejmp.2017.09.121
Yan D, Jaffray DA, Wong JW. A model to accumulate fractionated dose in a deforming organ. Int J Radiat Oncol Biol Phys. 1999;44:665.
pubmed: 10348298 doi: 10.1016/S0360-3016(99)00007-3
Wang H, Garden AS, Zhang L, et al. Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-Dimensional computed tomography images using deformable image Registration Method. Int J Radiat Oncol Biol Phys. 2008;72:210.
pubmed: 18722272 pmcid: 2593788 doi: 10.1016/j.ijrobp.2008.05.008
Czajkowski P, Piotrowski T. Evaluation of the accuracy of dose delivery in stereotactic radiotherapy using the velocity commercial software. Phys Med. 2022;95:133.
pubmed: 35176719 doi: 10.1016/j.ejmp.2022.02.005
Wang B, Wang DQ, Lin MS, et al. Accumulation of the delivered dose based on cone-beam CT and deformable image registration for non-small cell lung cancer treated with hypofractionated radiotherapy. BMC Cancer. 2020;20:1112.
pubmed: 33198676 pmcid: 7670776 doi: 10.1186/s12885-020-07617-3
Lawson JD, Schreibmann E, Jani AB, et al. Quantitative evaluation of a cone-beam computed tomography-planning computed tomography deformable image registration method for adaptive radiation therapy. J Appl Clin Med Phys. 2007;8:2432.
doi: 10.1120/jacmp.v8i4.2432
Keall PJ, Nguyen DT, O’Brien R, et al. The first clinical implementation of real-time image-guided adaptive radiotherapy using a standard linear accelerator. Radiother Oncol. 2018;127:6–11.
pubmed: 29428258 doi: 10.1016/j.radonc.2018.01.001
Brock KK, Mutic S, McNutt TR, et al. Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM Radiation Therapy Committee Task Group 132. Med Phys. 2017;44:e43.
pubmed: 28376237 doi: 10.1002/mp.12256
Park JM, Wu HG, Kim HJ, et al. Comparison of treatment plans between IMRT with MR-linac and VMAT for lung SABR. Radiat Oncol. 2019;14:105.
pubmed: 31196120 pmcid: 6567463 doi: 10.1186/s13014-019-1314-0
Park JW, Kang MK, Yea JW. Feasibility and efficacy of adaptive intensity modulated Radiotherapy Planning according to Tumor volume change in early stage non-small cell lung Cancer with stereotactic body Radiotherapy. Prog Med Phys. 2015;26:79.
doi: 10.14316/pmp.2015.26.2.79
Bhatt AD, El-Ghamry MN, Dunlap NE, et al. Tumor volume change with stereotactic body radiotherapy (SBRT) for early-stage lung cancer: evaluating the potential for adaptive SBRT. Am J Clin Oncol. 2015;38:41.
pubmed: 24513663 doi: 10.1097/COC.0b013e318287bd7f
Gunter T, Ali I, Matthiesen C, et al. Gross tumour volume variations in primary non-small-cell lung cancer during the course of treatment with stereotactic body radiation therapy. J Med Imaging Radiat Oncol. 2014;58:384.
pubmed: 24592817 doi: 10.1111/1754-9485.12168
Saito AI, Olivier KR, Li JG, et al. Lung tumor motion change during stereotactic body radiotherapy (SBRT): an evaluation using MRI. J Appl Clin Med Phys. 2014;15:4434.
pubmed: 24892328 doi: 10.1120/jacmp.v15i3.4434
Tatekawa K, Iwata H, Kawaguchi T, et al. Changes in volume of stage I non-small-cell lung cancer during stereotactic body radiotherapy. Radiat Oncol. 2014;9:8.
pubmed: 24393430 pmcid: 3904205 doi: 10.1186/1748-717X-9-8
Kim JI, Kim JH, Park JM. Uncertainties of cumulative dose assessment for prostate IMRT. Transl Cancer Res. 2017;6(Sppl2):S357.
doi: 10.21037/tcr.2017.03.30

Auteurs

Geum Bong Yu (GB)

Department of Radiation Oncology, Seoul National University Hospital, 101, Daehak-ro, Jongno- gu, Seoul, 03080, Republic of Korea.
Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, South Korea.

Jung In Kim (JI)

Department of Radiation Oncology, Seoul National University Hospital, 101, Daehak-ro, Jongno- gu, Seoul, 03080, Republic of Korea.
Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, South Korea.
Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea.

Hak Jae Kim (HJ)

Department of Radiation Oncology, Seoul National University Hospital, 101, Daehak-ro, Jongno- gu, Seoul, 03080, Republic of Korea.
Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, 03080, Korea.
Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Korea.

Seungwan Lee (S)

Department of Radiological Science, Konyang University, Nonsan, 35365, South Korea.

Chang Heon Choi (CH)

Department of Radiation Oncology, Seoul National University Hospital, 101, Daehak-ro, Jongno- gu, Seoul, 03080, Republic of Korea. dm140@snu.ac.kr.
Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, South Korea. dm140@snu.ac.kr.
Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea. dm140@snu.ac.kr.

Seonghee Kang (S)

Department of Radiation Oncology, Seoul National University Hospital, 101, Daehak-ro, Jongno- gu, Seoul, 03080, Republic of Korea. kangsh012@gmail.com.
Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, South Korea. kangsh012@gmail.com.
Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea. kangsh012@gmail.com.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Comparative analysis of delivered and planned...
questionsmedicales.fr › Maladies de l'appareil respiratoire › Tumeurs de l'appareil respiratoire › Tumeurs du poumon › Comparative analysis of delivered and planned...
questionsmedicales.fr › Techniques d'investigation › Techniques stéréotaxiques › Radiochirurgie › Comparative analysis of delivered and planned...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Prise de décision assistée par ordinateur › Thérapie assistée par ordinateur › Radiothérapie assistée par ordinateur › Planification de radiothérapie assistée par ordinateur › Comparative analysis of delivered and planned...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Comparative analysis of delivered and planned...
questionsmedicales.fr › Maladies de l'appareil respiratoire › Tumeurs de l'appareil respiratoire › Tumeurs du poumon › Tumeurs des bronches › Carcinome bronchogénique › Carcinome pulmonaire non à petites cellules › Comparative analysis of delivered and planned...
questionsmedicales.fr › Thérapeutique › Radiothérapie › Dosimétrie en radiothérapie › Comparative analysis of delivered and planned...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Tomographie à rayons X › Tomodensitométrie › Tomodensitométrie à faisceau conique › Comparative analysis of delivered and planned...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Comparative analysis of delivered and planned...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Adulte d'âge moyen › Comparative analysis of delivered and planned...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Sujet âgé de 80 ans ou plus › Comparative analysis of delivered and planned...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Prise de décision assistée par ordinateur › Thérapie assistée par ordinateur › Radiothérapie assistée par ordinateur › Radiothérapie conformationnelle › Radiothérapie conformationnelle avec modulation d'intensité › Comparative analysis of delivered and planned...