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Dose volume histogram metrics and tumour control probability modelling in locally advanced non-small-cell lung cancer: average intensity dataset versus individual four-dimensional CT phases

Published online by Cambridge University Press:  15 September 2020

Cathy Fleming Open the ORCID record for Cathy Fleming [Opens in a new window] ,
Ronan McDermott ,
Serena O’Keeffe ,
Mary Dunne ,
John G. Armstrong ,
Brendan McClean  and
Luis León Vintró
Cathy Fleming*
Affiliation:
Department of Physics, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland UCD School of Physics, University College Dublin, Dublin, Ireland
Ronan McDermott
Affiliation:
Department of Radiation Oncology, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland
Serena O’Keeffe
Affiliation:
Department of Physics, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland UCD School of Physics, University College Dublin, Dublin, Ireland
Mary Dunne
Affiliation:
Clinical Trials Resource Unit, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland
John G. Armstrong
Affiliation:
Department of Radiation Oncology, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland
Brendan McClean
Affiliation:
Department of Physics, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland
Luis León Vintró
Affiliation:
UCD School of Physics, University College Dublin, Dublin, Ireland
*
Author for correspondence: Cathy Fleming, Department of Physics, St. Luke’s Radiation Oncology Network, St. Luke’s Hospital, Dublin, Ireland. Tel: 014065264. E-mail: [email protected]
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Abstract

Aim:

This work compares dose-volume constraints (DVCs) and tumour control predictions based on the average intensity projection (AVIP) to those on each phase of the four-dimensional computed tomography.

Materials and methods:

In this prospective study plans generated on an AVIP for nine patients with locally advanced non-small-cell lung cancer were recalculated on each phase. Dose-volume histogram (DVH) metrics extracted and tumour control probabilities (TCP) were calculated. These were evaluated by Bland–Altman analysis and Pearson Correlation.

Results:

The largest difference between clinical target volume (CTV) on the individual phases and the internal CTV (iCTV) on the AVIP was seen for the smallest volume. For the planning target volume, the mean of each metric across all phases is well represented by the AVIP value. For most patients, TCPs from individual phases are representative of that on the AVIP. Organ at risk metrics from the AVIP are similar to those seen across all phases.

Findings:

Utilising traditional DVH metrics on an AVIP is generally valid, however, additional investigation may be required for small target volumes in combination with large motion as the differences between the values on the AVIP and any given phase may be significant.

Keywords

average intensity projection (AVIP)four-dimensional computed tomography (4DCT)Volumetric modulated arc therapy (VMAT)Acuros (AXB)tumour control probability (TCP)dose volume histogram (DVH)
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 20 , Issue 4 , December 2021 , pp. 373 - 379
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

Rietzel, E, Liu, A K, Doppke, K P, et al. Design of 4D treatment planning target volumes. Int J Radiat Oncol Biol Phys 2006; 66(1): 287–295.CrossRefGoogle ScholarPubMed
Bradley, J D, Nofal, A N, El Naqa, I M, et al. Comparison of helical, maximum intensity projection (MIP), and averaged intensity (AI) 4D CT imaging for stereotactic body radiation therapy (SBRT) planning in lung cancer. Radiother Oncol 2006; 81(3): 264–268.CrossRefGoogle ScholarPubMed
Admiraal, M A, Schuring, D, Hurkmans, C W. Dose calculations accounting for breathing motion in stereotactic lung radiotherapy based on 4D-CT and the internal target volume. Radiother Oncol 2008; 86(1): 55–60.CrossRefGoogle ScholarPubMed
Ehler, E D, Tomé, W A. Lung 4D-IMRT treatment planning: An evaluation of three methods applied to four-dimensional data sets. Radiother Oncol 2008; 88(3): 319–325.CrossRefGoogle ScholarPubMed
Han, K, Basran, P S, Cheung, P. Comparison of Helical and Average Computed Tomography for Stereotactic Body Radiation Treatment Planning and Normal Tissue Contouring in Lung Cancer. Clin Oncol 2010; 22(10): 862–867.CrossRefGoogle ScholarPubMed
Oechsner, M, Odersky, L, Berndt, J, Combs, S E, Wilkens, J J, Duma, M N. Dosimetric impact of different CT datasets for stereotactic treatment planning using 3D conformal radiotherapy or volumetric modulated arc therapy. Radiat Oncol 2015; 10(1): 1–9.CrossRefGoogle ScholarPubMed
Giraud, P, Antoine, M, Larrouy, A, et al. Evaluation of microscopic tumor extension in non-small-cell lung cancer for three-dimensional conformal radiotherapy planning. Int J Radiat Oncol Biol Phys 2000; 48(4): 1015–1024.CrossRefGoogle ScholarPubMed
Fogliata, A, Nicolini, G, Clivio, A, Vanetti, E, Cozzi, L. Dosimetric evaluation of Acuros XB Advanced Dose Calculation algorithm in heterogeneous media. Radiat Oncol 2011; 6: 82.CrossRefGoogle ScholarPubMed
Han, T, Mikell, J K, Salehpour, M, Mourtada, F. Dosimetric comparison of Acuros XB deterministic radiation transport method with Monte Carlo and model-based convolution methods in heterogeneous media. Med Phys 2011; 38(5): 2651–2664.CrossRefGoogle ScholarPubMed
Nahum, A E, Sanchez-Nieto, B. Tumour control probability modelling: basic principles and applications in treatment planning. Phys Med 2001; XVII: 13–23.Google Scholar
Nahum, A E, Uzan, J, Malik, Z I, Baker, C. Quantitative tumour control predictions for the radiotherapy of non-small-cell lung tumours. Med Phys 2011; 38(6): 3364–3881.CrossRefGoogle Scholar
Webb, S. Optimum parameters in a model for tumour control probability including interpatient heterogeneity. Phys Med Biol 1994; 39(11): 1895–1914.CrossRefGoogle Scholar
Bland, J M, Altman, D G. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud 2010; 47: 931–936.CrossRefGoogle Scholar
D’Souza, W, Nazareth, D, Zhang, B, et al. The use of gated and 4D CT imaging in planning for stereotactic body radiation therapy. Med Dosim 2007; 32(2): 92–101.CrossRefGoogle ScholarPubMed
Muirhead, R, McNee, S G, Featherstone, C, Moore, K, Muscat, S. Use of maximum intensity projections (MIPs) for target outlining in 4DCT radiotherapy planning. J Thorac Oncol 2008; 3(12): 1433–1438.CrossRefGoogle ScholarPubMed
Underberg, R W M, Lagerwaard, F J, Slotman, B J, Cuijpers, J P, Senan, S. Use of maximum intensity projections (MIP) for target volume generation in 4DCT scans for lung cancer. Int J Radiat Oncol Biol Phys 2005; 63(1): 253–260.CrossRefGoogle ScholarPubMed
Park, K, Huang, L, Gagne, H, Papiez, L. Do Maximum Intensity Projection Images Truly Capture Tumor Motion? Int J Radiat Oncol Biol Phys 2009; 73(2): 618–625.CrossRefGoogle ScholarPubMed
Geld van der, . Reproducibility of target volumes generated using uncoached 4-dimensional CT scans for peripheral lung cancer. Radiat Oncol 2006; 43.CrossRefGoogle Scholar
Kong, F-M S, Machtay, M, Bradley, J D, et al. RTOG 1106/ACRIN 6697 Randomized Phase II Trial of Individualized Adaptive Radiotherapy Using during Treatment FDG-PET/CT and Modern Technology in Locally Advanced Non-Small Cell Lung Cancer (NSCLC). 2013.Google Scholar
Kang, Y, Zhang, X, Chang, J Y, et al. 4D Proton treatment planning strategy for mobile lung tumors. Int J Radiat Oncol Biol Phys 2007; 67(3): 906–914.CrossRefGoogle ScholarPubMed
Ehrbar, S, Lang, S, Stieb, S, et al. Three-dimensional versus four-dimensional dose calculation for volumetric modulated arc therapy of hypofractionated treatments. Z Med Phys 2016; 26(1): 45–53.CrossRefGoogle ScholarPubMed
Starkschall, G, Britton, K, McAleer, M, et al. Potential dosimetric benefits of four-dimensional radiation treatment planning. Int J Radiat Oncol Biol Phys 2009; 73(5): 1560–1565.CrossRefGoogle ScholarPubMed
Schmidt, M L, Hoffmann, L, Kandi, M, Moller, D S, Poulsen, P R. Dosimetric impact of respiratory motion, interfraction baseline shifts, and anatomical changes in radiotherapy of non-small cell lung cancer. Acta Oncol (Madr) 2013; 52(7): 1490–1496.CrossRefGoogle ScholarPubMed
Fox, J L, Ford, E, Redmond, K, Zhou, J, Wong, J W, Song, D Y. Quantifcation of tumor volume changes during radiotherapy for non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2009; 74(2): 341–348.CrossRefGoogle Scholar