Skip to main content
Top
Published in: BMC Cancer 1/2018

Open Access 01-12-2018 | Research article

Moving targets in 4D-CTs versus MIP and AIP: comparison of patients data to phantom data

Authors: Kai Joachim Borm, Markus Oechsner, Moritz Wiegandt, Andreas Hofmeister, Stephanie E. Combs, Marciana Nona Duma

Published in: BMC Cancer | Issue 1/2018

Login to get access

Abstract

Purpose

Maximum (MIP) and average intensity projection (AIP) CTs allow rapid definition of internal target volumes in a 4D-CT. The purpose of this study was to assess the accuracy of these techniques in a large patient cohort in combination with simulations on a lung phantom.

Methods

4DCT data from a self-developed 3D lung phantom and from 50 patients with lung tumors were analyzed. ITVs were contoured in maximum (ITVMIP) and average intensity projection (ITVAIP) and subsequently compared to ITVs contoured in 10 phases of a 4D-CT (ITV10). In the phantom study additionally a theoretical target volume was calculated for each motion and compared to the contoured volumes.

Results

ITV10 overestimated the actual target volume by 9.5% whereas ITVMIP and ITVAIP lead to an underestimation of − 1.8% and − 11.4% in the phantom study. The ITVMIP (ITVAIP) was in average − 10.0% (− 18.7%) smaller compared to the ITV10. In the patient CTs deviations between ITV10 and MIP/AIP were significantly larger (MIP: – 20.2% AIP: -33.7%) compared to this. Tumors adjacent to the chestwall, the mediastinum or the diaphragm showed lower conformity between ITV10 and ITVMIP (ITVAIP) compared to tumors solely surrounded by lung tissue. Large tumor diameters (> 3.5 cm) and large motion amplitudes (> 1 cm) were associated with lower conformity between intensity projection CTs and ITV10−.

Conclusion

The application of MIP and AIP in the clinical practice should not be a standard procedure for every patient, since relevant underestimation of tumor volumes may occur. This is especially true if the tumor borders the mediastinum, the chest wall or the diaphragm and if tumors show a large motion amplitude.
Literature
1.
go back to reference Tyldesley S, et al. Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach. Int J Radiat Oncol Biol Phys. 2001;49(4):973–85.CrossRefPubMed Tyldesley S, et al. Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach. Int J Radiat Oncol Biol Phys. 2001;49(4):973–85.CrossRefPubMed
2.
go back to reference Wulf J, et al. Stereotactic radiotherapy of targets in the lung and liver. Strahlenther Onkol. 2001;177(12):645–55.CrossRefPubMed Wulf J, et al. Stereotactic radiotherapy of targets in the lung and liver. Strahlenther Onkol. 2001;177(12):645–55.CrossRefPubMed
3.
go back to reference Rusthoven KE, et al. Multi-institutional phase I/II trial of stereotactic body radiation therapy for lung metastases. J Clin Oncol. 2009;27(10):1579–84.CrossRefPubMed Rusthoven KE, et al. Multi-institutional phase I/II trial of stereotactic body radiation therapy for lung metastases. J Clin Oncol. 2009;27(10):1579–84.CrossRefPubMed
4.
go back to reference Ong CL, et al. Treatment of large stage I-II lung tumors using stereotactic body radiotherapy (SBRT): planning considerations and early toxicity. Radiother Oncol. 2010;97(3):431–6.CrossRefPubMed Ong CL, et al. Treatment of large stage I-II lung tumors using stereotactic body radiotherapy (SBRT): planning considerations and early toxicity. Radiother Oncol. 2010;97(3):431–6.CrossRefPubMed
5.
go back to reference Shimizu S, et al. Detection of lung tumor movement in real-time tumor-tracking radiotherapy. Int J Radiat Oncol Biol Phys. 2001;51(2):304–10.CrossRefPubMed Shimizu S, et al. Detection of lung tumor movement in real-time tumor-tracking radiotherapy. Int J Radiat Oncol Biol Phys. 2001;51(2):304–10.CrossRefPubMed
6.
go back to reference Shirato H, et al. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol. 2004;14(1):10–8.CrossRefPubMed Shirato H, et al. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol. 2004;14(1):10–8.CrossRefPubMed
7.
go back to reference Nakamura M, et al. Geometrical differences in target volumes between slow CT and 4D CT imaging in stereotactic body radiotherapy for lung tumors in the upper and middle lobe. Med Phys. 2008;35(9):4142–8.CrossRefPubMed Nakamura M, et al. Geometrical differences in target volumes between slow CT and 4D CT imaging in stereotactic body radiotherapy for lung tumors in the upper and middle lobe. Med Phys. 2008;35(9):4142–8.CrossRefPubMed
8.
go back to reference Vedam SS, et al. Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys Med Biol. 2003;48(1):45–62.CrossRefPubMed Vedam SS, et al. Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys Med Biol. 2003;48(1):45–62.CrossRefPubMed
9.
go back to reference Rietzel E, et al. Design of 4D treatment planning target volumes. Int J Radiat Oncol Biol Phys. 2006;66(1):287–95.CrossRefPubMed Rietzel E, et al. Design of 4D treatment planning target volumes. Int J Radiat Oncol Biol Phys. 2006;66(1):287–95.CrossRefPubMed
10.
go back to reference Guckenberger M, et al. Four-dimensional treatment planning for stereotactic body radiotherapy. Int J Radiat Oncol Biol Phys. 2007;69(1):276–85.CrossRefPubMed Guckenberger M, et al. Four-dimensional treatment planning for stereotactic body radiotherapy. Int J Radiat Oncol Biol Phys. 2007;69(1):276–85.CrossRefPubMed
11.
go back to reference Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol. 2004;14(1):81–90.CrossRefPubMed Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol. 2004;14(1):81–90.CrossRefPubMed
12.
go back to reference Bradley JD, 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):264–8.CrossRefPubMed Bradley JD, 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):264–8.CrossRefPubMed
13.
go back to reference Muirhead R, et al. Use of maximum intensity projections (MIPs) for target outlining in 4DCT radiotherapy planning. J Thorac Oncol. 2008;3(12):1433–8.CrossRefPubMed Muirhead R, et al. Use of maximum intensity projections (MIPs) for target outlining in 4DCT radiotherapy planning. J Thorac Oncol. 2008;3(12):1433–8.CrossRefPubMed
14.
go back to reference Park K, et al. Do maximum intensity projection images truly capture tumor motion? Int J Radiat Oncol Biol Phys. 2009;73(2):618–25.CrossRefPubMed Park K, et al. Do maximum intensity projection images truly capture tumor motion? Int J Radiat Oncol Biol Phys. 2009;73(2):618–25.CrossRefPubMed
15.
go back to reference Simon L, et al. Initial evaluation of a four-dimensional computed tomography system, using a programmable motor. Radiat Oncol Phys. 2006;7(4):50–65. Simon L, et al. Initial evaluation of a four-dimensional computed tomography system, using a programmable motor. Radiat Oncol Phys. 2006;7(4):50–65.
16.
go back to reference Underberg RW, et al. 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):253–60.CrossRefPubMed Underberg RW, et al. 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):253–60.CrossRefPubMed
17.
go back to reference Cai J, Read PW, Sheng K. The effect of respiratory motion variability and tumor size on the accuracy of average intensity projection from four-dimensional computed tomography: an investigation based on dynamic MRI. Med Phys. 2008;35(11):4974–81.CrossRefPubMed Cai J, Read PW, Sheng K. The effect of respiratory motion variability and tumor size on the accuracy of average intensity projection from four-dimensional computed tomography: an investigation based on dynamic MRI. Med Phys. 2008;35(11):4974–81.CrossRefPubMed
18.
go back to reference Borm KJ, et al. The importance of surrounding tissues and window settings for contouring of moving targets. Strahlenther Onkol. 2015;191(9):750–6.CrossRefPubMed Borm KJ, et al. The importance of surrounding tissues and window settings for contouring of moving targets. Strahlenther Onkol. 2015;191(9):750–6.CrossRefPubMed
19.
go back to reference Borm KJ, et al. The impact of CT window settings on the contouring of a moving target: a phantom study. Clin Radiol. 2014;69(8):e331–6.CrossRefPubMed Borm KJ, et al. The impact of CT window settings on the contouring of a moving target: a phantom study. Clin Radiol. 2014;69(8):e331–6.CrossRefPubMed
20.
go back to reference Oechsner M, et al. Interobserver variability of patient positioning using four different CT datasets for image registration in lung stereotactic body radiotherapy. Strahlenther Onkol. 2017;193(10):831–9.CrossRefPubMed Oechsner M, et al. Interobserver variability of patient positioning using four different CT datasets for image registration in lung stereotactic body radiotherapy. Strahlenther Onkol. 2017;193(10):831–9.CrossRefPubMed
21.
go back to reference van't Riet A, et al. A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: application to the prostate. Int J Radiat Oncol Biol Phys. 1997;37(3):731–6.CrossRefPubMed van't Riet A, et al. A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: application to the prostate. Int J Radiat Oncol Biol Phys. 1997;37(3):731–6.CrossRefPubMed
22.
go back to reference Katoh N, et al. Clinical outcomes of stage I and IIA non-small cell lung cancer patients treated with stereotactic body radiotherapy using a real-time tumor-tracking radiotherapy system. Radiat Oncol. 2017;12(1):3.CrossRefPubMedPubMedCentral Katoh N, et al. Clinical outcomes of stage I and IIA non-small cell lung cancer patients treated with stereotactic body radiotherapy using a real-time tumor-tracking radiotherapy system. Radiat Oncol. 2017;12(1):3.CrossRefPubMedPubMedCentral
23.
go back to reference Takao S, et al. Intrafractional baseline shift or drift of lung tumor motion during gated radiation therapy with a real-time tumor-tracking system. Int J Radiat Oncol Biol Phys. 2016;94(1):172–80.CrossRefPubMed Takao S, et al. Intrafractional baseline shift or drift of lung tumor motion during gated radiation therapy with a real-time tumor-tracking system. Int J Radiat Oncol Biol Phys. 2016;94(1):172–80.CrossRefPubMed
24.
go back to reference Ehrbar S, et al. ITV, mid-ventilation, gating or couch tracking - a comparison of respiratory motion-management techniques based on 4D dose calculations. Radiother Oncol. 2017;124(1):80–8.CrossRefPubMed Ehrbar S, et al. ITV, mid-ventilation, gating or couch tracking - a comparison of respiratory motion-management techniques based on 4D dose calculations. Radiother Oncol. 2017;124(1):80–8.CrossRefPubMed
25.
go back to reference Tian Y, et al. Dosimetric comparison of treatment plans based on free breathing, maximum, and average intensity projection CTs for lung cancer SBRT. Med Phys. 2012;39(5):2754–60.CrossRefPubMed Tian Y, et al. Dosimetric comparison of treatment plans based on free breathing, maximum, and average intensity projection CTs for lung cancer SBRT. Med Phys. 2012;39(5):2754–60.CrossRefPubMed
26.
go back to reference Louie AV, et al. Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era. Radiother Oncol. 2010;95(2):166–71.CrossRefPubMed Louie AV, et al. Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era. Radiother Oncol. 2010;95(2):166–71.CrossRefPubMed
Metadata
Title
Moving targets in 4D-CTs versus MIP and AIP: comparison of patients data to phantom data
Authors
Kai Joachim Borm
Markus Oechsner
Moritz Wiegandt
Andreas Hofmeister
Stephanie E. Combs
Marciana Nona Duma
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2018
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-018-4647-4

Other articles of this Issue 1/2018

BMC Cancer 1/2018 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine