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British Journal of Radiology (2004) 77, 588-596
© 2004 British Institute of Radiology
doi: 10.1259/bjr/64800801
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Full Paper

A feasibility study on the prediction of tumour location in the lung from skin motion

S Ahn, MD, PhD1,2, B Yi, PhD1, Y Suh, BS1, J Kim, MD, PhD1, S Lee, MD1, S Shin, MD1, S Shin, PhD2 and E Choi, MD, PhD1

1 Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, 388-1 Pungnap-dong Songpa-gu Seoul and 2 Department of Physics, Ewha Womans University, Seoul, Korea


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussions and conclusions
 References
 
The system for predicting tumour location from skin motion induced by respiration was designed to reduce the effects of target movement. Fluoroscopic studies on 34 sites in the lungs and 14 sites in the diaphragm were performed so that the motions of skin markers and organs could be observed simultaneously. While patients were lying down in the simulator with radio-opaque markers on their skin, fluoroscopic images both in the anterior–posterior (AP) view and in the lateral view were sent to an analysing computer and recorded. The results that showed a strong correlation (0.77±0.12) between the patients' skin and tumour movement, especially for the sites located in the lower lung fields or in the diaphragm. With the prediction from skin motion, the uncertainties of the position of tumours due to respiratory movement could be reduced by up to 1.47 cm in the lower lung fields in the superior–inferior (SI) direction. This study revealed that it is possible to trace the exact location of tumours in the lungs by observing skin motion in most cases (up to 88%).


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussions and conclusions
 References
 
Minimizing the planning target volume (PTV) margin and reproducing its position during treatment as well as between treatments are important tasks in external beam radiotherapy. Tumour movement occurs not only as a result of set-up errors but also from respiration and interferes with optimized treatment planning. The range of respiratory motion has been reported to be up to 3.8 cm [1–4]. Owing to respiratory movement during irradiation, considerable margins have to be added around the treatment sites in thoracic and abdominal regions. Moreover, for moving organs, especially in intensity-modulated radiotherapy (IMRT), large discrepancies between the intended and delivered planning target volume can be generated, even if there are adequate margins [5, 6].

Since significant margins are required to cover the tumour displacements due to respiration, normal tissues near the target are irradiated unnecessarily. Therefore, efforts to reduce the effect of tumour motion are needed to concentrate the prescribed dose on the target and decrease complication rates in normal tissues [7–11]. A number of techniques have been proposed to take account of tumour movement due to respiration: (1) to minimize tumour motion by controlling patients' breathing actively or passively [1, 2, 12–20]; (2) to synchronize beam exposure with the part of the respiratory cycle when tumour motion is minimal [16, 21–30]; and (3) to adapt the alignment of the radiation field continuously to follow the moving tumour [31–33]. For greater accuracy, the respiratory gating and the adaptive radiotherapy methods require a means of tracking the tumour while the breath-hold technique still needs daily on-line imaging and repositioning [17]. The 4D radiotherapy technique [31] proposes tracking a tumour with the radiation beam as the tumour moves during the respiratory cycle, and requires respiration signals, which are presumed to be correlated with internal organ movement, such as infrared reflective markers, spirometry, strain gauges, video tracking, or fluoroscopic tracking.

Thus, the demand for tracking tumours by observing their movement or predicting their positions continually during treatment has increased in the field of radiation oncology [23, 26, 28, 30, 34–36]. If an appropriate technique for detecting tumour location is available, it is possible to reduce the PTV by the adaptive beam technique or gated radiation therapy. Among the methods to track internal tumour location, this study looked at a predicted system for indirect tracking which could be performed with conventional tools, charge-coupled device (CCD) cameras with skin markers. External respiration signals were tracked and assumed to correlate with internal tumour movement [35–39]. Though a few studies examined the correlations between external respiration signals and internal tumour motion, none have produced a complete evaluation, still leaving the question open [25, 31, 40]. Therefore, the aim of this study was to verify the relationship between the movement of the skin and target organ for use in the tumour tracking or the gating techniques.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussions and conclusions
 References
 
The system for investigating the correlation between skin markers and tumour movement was designed, as shown in Figure 1Go. First of all, magnitude, direction, period, and phase of motions of skin markers and the lungs due to breathing were characterized. Then the system and the algorithm that predicts the location of tumour from the patient's skin motion were derived and validated.



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  		Figure 1. Scheme for deducing the correlation between skin markers and tumour movements: observing skin markers and internal tumour synchronously, and then inferring the correlation between them. AP, anterior–posterior.

 
This system is based on a fluoroscopic examination, which made it possible to observe skin and tumour movement simultaneously (Figure 1Go). Patients with lung cancer and good performance status who were being treated in the Asan Medical Center, and who agreed to the aim of this study, gave written informed consent. 34 sites in the lungs, 15 in the upper lung fields, 19 in the lower lung fields and 14 sites in the diaphragm, were selected. Fluoroscopic images from a conventional simulator (Ximatron, Varian, UK) were used for motion analysis. Prior to treatment simulation, radio-opaque skin markers to indicate the locations of points of interest (POIs), including tumours, in the lungs or in the diaphragm, were taped on the patient's skin. During simulation, patients lay in a supine position on the simulator couch, breathing shallowly. Fluoroscopic movies of both skin markers and corresponding POIs in the anterior–posterior (AP) view and in the lateral view were directly sent to an analysing computer and recorded for about 2 min. Movements in the AP view were observed separately from those in the lateral view (Figure 1Go and Figure 2Go), because of the limitations of machine. The acquired fluoroscopic movies were digitized at a rate of 29.97 frames per second via an image capture board (Fusion MPEG, Dvico, Korea) with 720 x 480 pixels, and individual breathing patterns were identified from these digitized movies. Video sequences were sampled every 10 frames (approximately 0.33 s) for analysis.



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  		Figure 2. Fluoroscopic images of skin markers and organs (a) in the anterior–posterior view and (b) in the lateral view: the locations of skin and organ were determined from several points on the individual skin markers and organs during normal breathing.

 
Coordinates were defined so that the x-axis was in the right-to-left (R–L) direction, the y-axis is superior–inferior (SI) direction and z-axis in the AP direction. Movement in the x-axis and in the y-axis were obtained from the images in the AP view and those in the y-axis and in the z-axis were from the images in the lateral view. In order to examine whether tumour movements differ according to site, respiratory movements were divided into the upper lung fields, the lower lung fields, and the diaphragm. On the extracted images, the locations of the skin and tumour were determined from several points on the individual skin markers and POIs during normal breathing, as shown in Figure 2Go. A dot was placed on an arbitrary point specified above and motion patterns of skin and tumour were measured by keeping track of that point. The averages and the standard deviations of ranges of movement were determined with the peak-to-peak values (amplitude). The average displacements of skin and tumour were adjusted to zero for convenience of analysis. Subsequently, we deduced tumour motion from skin motion and with the resulting information about motion patterns of skin markers, motion patterns of the internal tumour were predicted using the least square method. In most cases, an amplitude fitting was employed, which magnified or reduced the amplitude of skin motion so that it matched the organ motion, while a phase-corrected fitting, which did the same procedure using the respiratory phase, was employed in some cases.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussions and conclusions
 References
 
Figure 3Go shows the images of skin markers and organs during inspiration and expiration, and their difference, in the lateral view. Skin markers moved predominantly in the y-axis and in the z-axis, and organs moved in the y-axis, but both moved almost negligibly in the x-axis. Motion ranges in the x-axis throughout most parts of the lungs or the diaphragm were within 0.10 cm (±0.05 cm). Allowing for measurement errors, mostly owing to difficulties in detecting POIs in the lungs or in the diaphragm and noise restrictions, these ranges were insignificant for respiratory compensation. Hence the cases in which motion range of either skin or tumour was less than 0.10 cm were discarded from analysis. Figure 4Go displays several examples of breathing patterns that were plotted by the movement of skin markers and tumours during normal breathing.



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  		Figure 3. Images of skin markers and internal organs during (a) inspiration and (b) expiration, and (c) their difference, in the lateral view.

 


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  		Figure 4. Respiratory motion patterns of skin and tumour: motion patterns of skin markers and tumour (a) in the x-axis (right upper lung fields in the anterior–posterior (AP) view), (b) in the y-axis (right upper lung fields in the AP view) and (c) in the z-axis (lower lung fields in the lateral view). (d) Motion patterns in the y-axis (right upper lung fields in the AP view): when skin makers began to move, a short delay in motion was caused by respiratory tumour motion. (e) Motion patterns in the y-axis (left upper lung fields in the AP view): when distortions were caused by sudden changes in period, amplitude, or phase of respiration, skin and tumour experienced the same distortions, and thus, remained correlated. x-axis, right–left direction of the body; y-axis, superior–inferior direction; z-axis, anterior–posterior direction.

 
Only motion ranges (amplitude) in the x-axis in the right upper lung fields were included, and these were relatively small; 0.23 cm for skin markers and 0.49 cm for tumour. Motion ranges in the y-axis of the right upper lung fields, the left upper lung fields, the right lower lung fields, and the left lower lung fields, and the diaphragm were 0.30 cm, 0.32 cm, 0.34 cm, 0.32 cm, and 0.24 cm for skin markers, respectively, and 0.97 cm, 1.57 cm, 1.50 cm, 0.98 cm, and 1.82 cm for tumour, respectively. Motion ranges in the z-axis of the upper lung fields, the lower lung fields, and the diaphragm were 0.43 cm, 0.31 cm, and 0.96 cm for skin markers and 0.34 cm, 0.27 cm, and 0.47 cm for tumour, respectively. Table 1Go represents the average motion ranges of the individual cases depending on views, directions, and sites. The time for the results varied from 9 s to 122 s (46 s on average), which was caused by limitations on image acquisition during analysis.


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  		Table 1. Motion ranges (amplitude) of skin markers and tumour of the individual cases depending on views, directions and sites, on the average (cm)

 
As shown in the motion patterns in the y-axis in Figure 4bGo, skin markers and tumour were found to move in the opposite direction in nearly half of the cases. For example, when the diaphragm moved in the cranial direction, skin markers moved in the caudal direction due to the effect of the diaphragm's motion. That is, depending on the sites of POIs and the axes observed, the correlation could be negative. For ease of analysis, these correlations were made positive. When skin markers began to move, a short delay in motion was caused by respiratory tumour motion. In other words, there was a short interval between movement of skin markers and tumour in some cases, as illustrated in Figure 4dGo. Figure 4eGo shows the breathing patterns of a certain patient that were distorted and random. This reflects the correlation of skin and tumour motions when distortions were caused by sudden changes in period, amplitude, or phase of respiration; skin and tumour were distorted in the same way, and thus, remained correlated.

Despite these observations, the movement of skin markers and tumour correlated strongly (Figure 5Go). In other words, plots of skin and tumour movement may seem to be quite different from each other (Figure 4Go), but the correlation coefficients between them were 0.77±0.12, indicating that correlation was strong. Table 2Go summarizes the correlation coefficients between the two. The strong correlation between skin and tumour movement, especially in the sites located in the lower lung fields or in the diaphragm that moved the most, made it possible to infer tumour location from the motion of skin markers. From the analysis of respiratory motion patterns shown in Figure 4Go, tumour movements can be deduced from the motion of skin markers by an amplitude fitting (as in Figures 6a, bGo) or a phase-corrected fitting (as in Figure 6cGo), both using the simple least square method. Predictions from skin motion allowed the uncertainties of tumour location to be reduced by up to 1.14 cm in the upper lung fields, 1.47 cm in the lower lung fields, and 1.08 cm in the diaphragm, in the y-axis.



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  		Figure 5. Correlation between skin and tumour movements: correlation coefficient of (a) the best case was 0.94 (upper lung fields in the lateral view), while that of (b) the worst case was 0.41 (left upper lung fields in the anterior–posterior view).

 

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  		Table 2. Correlation coefficients between skin and tumour movement depending on views, direction, and sites; correlation coefficients were 0.77 on the average and ranged from 0.41 to 0.97. Correlation coefficient between skin motion in the z-axis in the lateral view and tumour motion in the y-axis in the AP view were also presented (remote skin markers)

 


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  		Figure 6. Prediction of internal tumour location from external skin markers, and the uncertainties of tumour position due to movement during breathing before prediction (actual tumour motion) and after prediction (corrected tumour motion, which were the difference between actual tumour motion and predicted motion): motions (a) in the y-axis (right lower lung fields in the anterior–posterior (AP) view) and (b) in the y-axis (lower lung fields in the lateral view) were predicted by an amplitude fitting, and motions (c) in the y-axis (right lower lung fields in the AP view) by a phase-corrected fitting. (d) Prediction with remote skin markers: from skin motion in the z-axis in the lateral view, tumour motion in the y-axis in the AP view was predicted (lower lung fields in the lateral view). The uncertainties of tumour position were reduced (a) from 2.26 cm to 0.92 cm, (b) from 2.26 cm to 1.05 cm, (c) from 1.78 cm to 0.77 cm, and (d) 1.74 cm to 0.72 cm. x-axis, right–left direction of the body; y-axis, superior–inferior direction; z-axis, anterior–posterior direction.

 
The largest displacement of skin was observed in the z-axis followed by the lateral view, while that of tumour was in the y-axis followed by the AP view. To validate the necessity for the skin markers' proximity to the target and the possibility of using the largest displacement of skin and tumour movements, we also predicted tumour location in the y-axis from the AP view by the motion of skin markers in the z-axis from the lateral view, which were distant from tumour (remote skin marker). The correlation of tumour movement with that of skin markers near tumour and with remote skin markers were similar, as shown in Figure 6dGo. Thus, it is reasonable to use remote skin markers as an external respiratory signal, in which displacement is larger than for skin markers near tumour. The uncertainties of tumour position due to movement during breathing were reduced by a maximum of 1.47 cm (from 2.01 cm to 0.54 cm) after prediction from the motion of skin markers (Table 3Go). Therefore, it reduced the effect of respiratory motion.


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  		Table 3. Reduced effect of displacement of tumour by respiratory motion with the prediction of the best cases in each situation: uncertainties of tumour position due to movement during breathing before prediction (actual tumour motion), after prediction (corrected for tumour motion, the difference between actual tumour motion and predicted motion), and reduction subtracting "after" from "before". We also predicted tumour motion by the motion of skin markers in the z-axis from the lateral view, which were distant from tumour (remote skin marker)

 

   Discussions and conclusions
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussions and conclusions
References
 
There have been reports on detecting internal tumour movement and correlating tumour motion with a respiratory motion signal using special markers or devices [41, 42]. In contrast, this technique uses existing marks on a patient's skin to acquire external respiratory signals to monitor breathing, without a special device. In the Asan Medical Center, we use Indian red ink in order to mark a patient's skin for set-up. Therefore, the red lines on patient's skin could be used as skin markers in the clinic. Only the red components, which were extracted from images obtained by CCD cameras, need to be detected and tracked. This method was validated in our previous animal study [43].

Displacement of the tumour due to respiratory movement could be reduced by up to 1.47 cm by predicting tumour location from skin marker information. Despite the name "amplitude fitting", it was not an amplitude but a phase of breathing that was employed for prediction. In some cases, better results can be obtained using the respiratory phase shift to represent a delay in the movement of the skin and tumour. This study showed the feasibility of predicting the location of the tumour in the lungs or the diaphragm from external skin movements. Considering that correlation coefficients over 0.80 mean "very strong correlation", nearly half were shown to be very strong. The rest were strong correlations (0.60~0.79) with only 4 moderate cases (0.40~0.59) (Table 4Go). Since the patients skin and tumour movements showed a strong correlation, especially for the sites in the lungs or the diaphragm that moved most, this technique can be used to compensate for respiratory-induced motion. While the frequencies of patients' breathing tended to change, the motion of skin markers made the same patterns as that of tumour (Figure 6Go). Although not shown here, changes in breathing amplitudes showed the same patterns for skin markers and the tumour, so that strong correlations were maintained. That is, even with the different directions and the phase differences caused by movement delay, the location of the tumour could be predicted by information from markers on a patient's skin. Predictions using remote skin markers showed no difference compared with those using skin markers near the tumour. Though remote skin markers were placed at a distance from the target, movement was easier to detect and had fewer errors, suggesting that they were more effective.


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  		Table 4. Distribution of correlation coefficients: number of cases and their cumulative percentages. Nearly 88% of the correlations were strong (over 0.6)

 
Since displacement of skin markers is small, errors owing to set-up and measurement, including difficulties in detecting POIs and diversity of pixel size, were not insignificant. Judging from the worst cases where skin motion was too small to predict tumour motion correctly, slight movements of the skin below 0.20 cm are useless and can be discarded. Clinically, tumour movements less than 0.20 cm need not be tracked and compensated for because these are less than set-up errors. Set-up errors for the whole body stereotactic radiosurgery (SRS) are within 0.50 cm in all directions [44], and since the minimum beamlet size for IMRT is 0.50 x 0.50 cm2, displacements less than 0.20 cm are within half the smallest beamlet size. Data that were not statistically significant, such as an excessive variation or phase shift, were also discarded.

During a course of the respiratory adapting radiotherapy, marks on a patients' skin would be monitored via CCD cameras, and the beam would be stopped according to the criteria arranged previously from the observed motion of the tumour, until the patient's breathing had restored the original position. Consequently margins in the PTV could be reduced markedly, ensuring the clinical usefulness of this technique.

By using skin markers as an external respiratory signal, the position of the moving tumour was deduced from skin motion, and its feasibility was evaluated. This technique, however, cannot be applied to all patients. By selection of those that had correlation coefficients larger than 0.6, which means strong correlation, more than 88% of the cases were eligible for this technique (Table 4Go). Thus, a fluoroscopic study with radio-opaque skin markers should be performed before treatment, to check whether the respiratory movements of the skin and tumour of a patient are correlated. Further studies, for instance, using real-time image acquisition and processing, may make this technique easier to apply in the clinic.


   Footnotes
 
This work was supported by a Nuclear R&D Program from the Ministry of Science and Technology in Korea. Back

Received for publication August 11, 2003. Revision received January 19, 2004. Accepted for publication February 20, 2004.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussions and conclusions
 References
 

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