Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-26T21:09:30.542Z Has data issue: false hasContentIssue false
  • English
  • Français

Correlating lung tumour location and motion with respiration using 4D CT scans

Published online by Cambridge University Press:  13 January 2020

T R. Siow Open the ORCID record for T R. Siow [Opens in a new window]  and
S K. Lim
T R. Siow*
Affiliation:
National Cancer Centre Singapore, 11 Hospital Drive, Singapore169610, Republic of Singapore
S K. Lim
Affiliation:
National Cancer Centre Singapore, 11 Hospital Drive, Singapore169610, Republic of Singapore
*
Author for correspondence: Siow T. Rui, National Cancer Centre Singapore, 11 Hospital Drive, Singapore169610, Republic of Singapore. Tel: +6563214204. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

Lung tumours, especially those in the lower lobes, can move a lot during respiration; this motion needs to be accounted for during radiotherapy. In cases where 4D CT simulation scans are not performed, the current protocol at our centre is to apply a generic (internal motion + setup) margin of 0·70 cm in the axial plane and 1·20 cm in the longitudinal plane to all lung tumours, regardless of location. We analyse the tumour motions of a cohort of our local patients and categorise them into different locations in the lung. We seek to assess the adequacy of the current margins and to derive a more accurate set of standard margins which are specific for lung tumour locations.

Methods:

All cases of lung tumours treated with stereotactic ablative radiotherapy between 2012 and 2016 were identified retrospectively and 4D CT scan data analysed. These tumours were grouped into the following locations: upper zone (UZ), middle zone (MZ) and lower zone (LZ). The treatment planning system was used to generate the displacements of the centre of mass of the tumours in the right–left, anterior–posterior and superior–inferior axes; these were compared with the current generic margins. Median displacements were calculated for each axis in each location. New planning target volume (PTV) margins were derived by summing the median displacement, median absolute deviation (MAD) and 0·5 cm (for setup error).

Results:

Sixty-three cases were eligible for analyses. Motion in the superior–inferior direction was the greatest for all tumour locations, ranging from a median of 0·17 cm (MAD 0·12 cm) in UZ to 0·77 cm (MAD 0·27 cm) in LZ. Median tumour displacements in the anterior–posterior and right–left axes were similar for all locations, <0·30 and 0·20 cm, respectively. The current generic margins were adequate for only one-third of the cases in this study. A new PTV margin of 2·10 cm in the superior–inferior axis may be required for LZ tumours, while an additional 1–2 mm should be added to the current radial margins.

Conclusion:

The current generic margins are inadequate for the majority of cases. Tumour motion is the greatest in LZ in the superior–inferior axis. Motion mitigation strategies are essential for large LZ tumours.

Keywords

ITV marginslungrespiratory motiontumour location
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 20 , Issue 1 , March 2021 , pp. 17 - 21
Check for updates
Copyright
© The Author(s) 2020. Published by Cambridge University Press.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cheng, TY, Cramb, SM, Baade, PD, Youlden, DR, Nwogu, C, Reid, ME. The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics. J Thorac Oncol 2016; 11 (10): 1653–1671.CrossRefGoogle ScholarPubMed
National Registry of Diseases Office. 2017. Singapore Cancer Registry Annual Registry Report 2015. Ministry of Health Singapore. Retrieved 17 Nov 2019 from https://www.nrdo.gov.sg/docs/librariesprovider3/Publications-Cancer/cancer-registry-annual-report-2015_web.pdf?sfvrsn=10 Google Scholar
Keall, PJ, Mageras, GS, Balter, JM et al. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 2006; 33 (10): 3874–3900.CrossRefGoogle ScholarPubMed
Liu, HH, Balter, P, Tutt, T et al. Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer. Int J Radiat Oncol Biol Phys 2007; 68 (2): 531–540.CrossRefGoogle ScholarPubMed
Barrett, A, Dobbs, J, Morris, S, Roques, T. Practical Radiotherapy Planning, 4th edition. London, England: Hodder Arnold, 2009.CrossRefGoogle Scholar
Onimaru, R, Shirato, H, Fujino, M et al. The effect of tumor location and respiratory function on tumor movement estimated by real-time tracking radiotherapy (RTRT) system. Int J Radiat Oncol Biol Phys 2005; 63 (1):164–169.CrossRefGoogle ScholarPubMed
Stevens, CW, Munden, RF, Forster, KM et al. Respiratory-driven lung tumor motion is independent of tumor size, tumor location, and pulmonary function. Int J Radiat Oncol Biol Phys 2001; 51 (1): 62–68.CrossRefGoogle ScholarPubMed
Ross, CS, Hussey, DH, Pennington, EC, Stanford, W, Doornbos, JF. Analysis of movement of intrathoracic neoplasms using ultrafast computerized tomography. Int J Radiat Oncol Biol Phys 1990; 18 (3): 671–677.CrossRefGoogle ScholarPubMed
Plathow, C, Ley, S, Fink, C et al. Analysis of intrathoracic tumor mobility during whole breathing cycle by dynamic MRI. Int J Radiat Oncol Biol Phys 2004; 59 (4): 952–959.CrossRefGoogle ScholarPubMed
Seppenwoolde, Y, Shirato, H, Kitamura, K et al. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 2002; 53 (4): 822–834.CrossRefGoogle ScholarPubMed
Chen, QS, Weinhous, MS, Deibel, FC, Ciezki, JP, Macklis, RM. Fluoroscopic study of tumor motion due to breathing: facilitating precise radiation therapy for lung cancer patients. Med Phys 2001; 28 (9): 1850–1856.CrossRefGoogle ScholarPubMed
Sarudis, S, Karlsson Hauer, A, Nyman, J, Bäck, A. Systematic evaluation of lung tumor motion using four-dimensional computed tomography. Acta Oncol 2017; 56 (4): 525–530.CrossRefGoogle ScholarPubMed