Tumor location cannot predict the mobility of lung tumors: a 3D analysis of data generated from multiple CT scans

doi:10.1016/S0360-3016(02)04467-X

International Journal of Radiation OncologyBiologyPhysics

Volume 56, Issue 2, 1 June 2003, Pages 348-354

https://doi.org/10.1016/S0360-3016(02)04467-X Get rights and content

Abstract

Purpose

There is limited information available on the three-dimensional (3D) motion of lung tumors. Data derived from multiple planning computed tomographic (CT) scans were used to characterize the 3D movement of small peripheral lung tumors.

Methods and materials

A total of 29 data sets from patients with Stage I non–small-cell lung cancer (NSCLC), each of which consisted of three “rapid” and three “slow” planning CT scans, were analyzed. All six scans were coregistered, and contoured gross tumor volumes (GTVs) were expanded by 5 mm to derive clinical target volumes (CTVs). Two-dimensional and 3D displacement vectors of the individual CTVs, relative to an “optimal” CTV derived from all six scans, were generated. Tumor mobility was correlated with location. Three-dimensional margins, which had to be added to individual CTVs to ensure coverage of “optimal” CTVs, were determined.

Results

No significant correlation was observed between the anatomic location of tumors and the extent of mobility in the x, y, and z axes. However, supradiaphragmatic lesions exhibited more mobility, particularly in the craniocaudal direction. The addition of a 3D margin of 5 mm to a single slow CTV ensured full coverage of the “optimal CTV”.

Conclusions

Lung tumors demonstrate significant mobility in all directions, and this did not closely correlate with anatomic location. Individualized assessment of tumor mobility remains necessary, and is possible when the CTV derived from a single slow scan is used for radiotherapy planning.

Introduction

Despite the use of three-dimensional (3D) conformal radiotherapy in Stage I non–small-cell lung cancer (NSCLC), the actuarial freedom from local progression at 1 and 3 years is only 85% and 43%, respectively (1). Geographical misses may contribute to local failure, as target volumes are usually derived from a single rapid planning computed tomographic (CT) scan, which results in tumors being imaged at a random position during the respiratory cycle 2, 3. This, in turn, contributes to a systematic error in treatment planning and delivery (4). Single rapid planning CT scans are still widely used for radiotherapy planning in Stage I NSCLC. After delineation of the gross target volume (GTV), margins for microscopic tumor extension, tumor mobility, and patient setup errors are incorporated to derive the planning target volume (PTV).

Information on tumor mobility for radiotherapy planning is commonly generated using fluoroscopy or by coregistering data from multiple rapid or slow CT scans 5, 6. Fluoroscopy is the most widely used method in clinical practice, but drawbacks of this technique include the fact that tumors are often poorly visualized, that the technique often provides crude data only on superior-inferior and mediolateral movements, and the absence of an accurate link of the mobility data to the geometry of planning CT scans. The generation of “mobile target volumes” from coregistered multiple CT scans is a labor-intensive method (5). Single markers that are implanted in tumors have also been used for this purpose (7), but this represents an invasive and technically difficult procedure for peripheral lung tumors. In addition, up to four implanted markers are required to optimally detect rotations and distortions in tumor shape (8).

There is a paucity of data on the 3D movement of peripheral lung tumors that are located in different lobes. Nevertheless, it is common practice to add standard margins to clinical target volumes (CTV), which are derived from single rapid CT scans. For example, tumors in the lower lobe are reportedly the most mobile 2, 7, 9, 10, and we previously added an isotropic GTV to PTV margin of 1 cm in all directions, with the exception of tumors located adjacent to the diaphragm, where 2-cm margins were applied in the craniocaudal axis (1).

The aims of the current study were to characterize the 3D movement of tumors in a cohort of patients with peripherally located Stage I NSCLC who had undergone multiple “rapid” and “slow” CT scans. These “population-based” data on mobility were correlated with the anatomic location to determine if standard margins for incorporating tumor mobility could be described for different tumor locations.

Section snippets

Methods and materials

Since 2000, we have generated data on tumor mobility for radiotherapy planning in patients with Stage I NSCLC by performing a total of six spiral CT scans, i.e., three “rapid” and three “slow” CT scans 3, 5. A total of 29 such data sets were used for this study. Data from 10 of these patients were partially reported in an earlier publication, in which the slow CT scan technique was described (5). In addition, data on 7 other patients formed the basis of a report describing the evaluation of

Target volumes generated using rapid and slow CT scans

The “optimal” CTV was the largest in all patients (Table 2). With only two exceptions, the mean CTVs captured by slow CT scans were larger than those captured by rapid CT scans, and the mean ratio between the slow CTVs and the “optimal” CTVs was 0.78 ± 0.06, vs. 0.68 ± 0.09 for rapid CTVs.

The vectors of mobility derived from rapid and slow CT scans

The mobility vectors in the x, y, and z axes, as well as the 3D displacement vectors, were determined for both rapid and slow CT scans (see Fig. 2). As more tumor mobility was incorporated into slow scans,

Discussion

High-precision radiotherapy entails the delivery of dose distributions with increasing conformity relative to the target volume and surrounding critical structures. However, such techniques are very sensitive to errors in patient setup and internal organ motion. Errors arising from external setup deviations at the treatment unit may be minimized using off-line setup correction protocols (12). Three-dimensional data on the internal mobility of target volumes in the lung and mediastinum are still

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Clinical investigation: lungTumor location cannot predict the mobility of lung tumors: a 3D analysis of data generated from multiple CT scans

Abstract

Purpose

Methods and materials

Results

Conclusions

Introduction

Section snippets

Methods and materials

Target volumes generated using rapid and slow CT scans

The vectors of mobility derived from rapid and slow CT scans

Discussion

Radiother Oncol

Int J Radiat Oncol Biol Phys

Radiother Oncol

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol Biol Phys

Radiother Oncol

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol Biol Phys

Clinical investigation: lung
Tumor location cannot predict the mobility of lung tumors: a 3D analysis of data generated from multiple CT scans