a simulation study of irregular respiratory motion and its dosimetric impact on lung tumors

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A simulation study of irregular respiratory motion and its dosimetric impact on lung tumors

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2011 Phys. Med. Biol. 56 845

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IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 56 (2011) 845859 doi:10.1088/0031-9155/56/3/019

A simulation study of irregular respiratory motionand its dosimetric impact on lung tumors

Y D Mutaf1, C J Scicutella, D Michalski, K Fallon, E D Brandner,

G Bednarz and M S Huq

Department of Radiation Oncology, University of Pittsburgh Medical Center, Pittsburgh,PA 15232, USA

E-mail: [email protected]

Received 5 August 2010, in final form 29 November 2010Published 17 January 2011

Online at stacks.iop.org/PMB/56/845

Abstract

This study is aimed at providing a dosimetric evaluation of the irregular motion

of lung tumors due to variations in patients respiration. Twenty-three lung

cancer patients are retrospectively enrolled in this study. The motion of the

patient clinical target volume is simulated and two types of irregularities

are defined: characteristic and uncharacteristic motions. Characteristic

irregularities are representative of random fluctuations in the observed target

motion. Uncharacteristic irregular motion is classified as systematic errors in

determination of the target motion during the planning session. Respiratory

traces from measurement of patient abdominal motion are also used for the

target motion simulations. Characteristic irregular motion was observed to

cause minimal changes in target dosimetry with the largest effect of 2.5%

0.9% (1) reduction in the minimum target dose (Dmin) observed for targets

that move 2 cm on average and exhibiting 50% amplitude variations within a

session. However, uncharacteristic irregular motion introduced more drastic

changes in the clinical target volume (CTV) dose; 4.1% 1.7% reduction for

1 cm motion and 9.6% 1.7% drop for 2 cm. In simulations with patients

abdominal motion, corresponding changes in target dosimetry were observed

to be negligible (


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846 Y D Mutaf et al

technologies such as 4DCT (Rietzel et al 2005, Mutaf and Brinkmann 2008a), the changes in

3D patient anatomy such as organ motion are measured over time. This additional knowledge

is incorporated into the treatment planning process for the patient and used in conjunction

with appropriate treatment delivery techniques. In modern radiotherapy, the 3D model of the

patient anatomy is valid as long as it is sufficiently reproduced between the planning and theactual delivery stages of the radiotherapy. Similarly, the 4D model of the patient used for

treatmentplanning purposes is only deemedvalidif the reproducibility of themotion is verified.

Analogous to patient setup errors affecting the 3D localization of the target volume, a large

component of error in 4D radiotherapy could be the target motion irregularities (Ozhasoglu

and Murphy 2002, Chang et al 2007, Juhler Nottrup et al 2007) due to variations in patient

respiration.

Irregular breathing could be as large as the underlying respiratory motion itself.

Respiratory variations could also have intra and inter-fractional components affecting the

radiotherapy of mobile tumors in different ways. Using optical measurement methods, Juhler

Nottrup etal (2007) evaluated the variations in the chest wall motion of 11 lung cancer patients

and found out that inter-fractional variations can be up to ten times larger than the intra-

fractional variations. Due to observed irregularities in patients respiration, Juhler Nottrupetal (2007) also concluded that margins to account for respiratory motion cannot be determined

in one imaging session. Several techniques have been developed to mitigate the occurrence of

irregularities in patient respiratory motion. Audio and visual feedback techniques have been

demonstrated to be an effective way of achieving a consistent pattern of respiration. George

et al (2006) subjected 24 lung cancer patients to audio-visual respiratory feedback techniques

and observed significant improvement in the reproducibility of respiration. Gating technology

(Ramsey et al 1999, Wagman et al 2003) is also used to prevent irradiation in the presence of

drastic changes in breathing such as sneezing or coughing.

Despite the existing studies demonstrating the amount of irregularities in respiratory

motion, there is limited information on the dosimetric outcome of irregular breathing and

whether or not they have a clinically significant impact on therapeutic goals of the irradiation.

Due to radiation dispersion in tissue, penumbral effects and features of plan topology,

geometric observations performed with radiographic imaging or optical methods are notdirectly translatable to dosimetric conclusions. In the context of patient setup errors, the

amount of geometric miss and the associated target margin to ensure sufficient coverage are

shown to be proportional but not the same (van Herk 2004). This is a result of differences in

the shapes of target definition function perceived visually as a step function at the border of

the target and the function of the dosimetric profile across the target with a gradual fall off.

Similarly, van Herk et al (2003) analytically demonstrated that the additional target margin

required to compensate the motion of the target is substantially smaller than its full motion

amplitude. This was also confirmed recently by Mutaf and Brinkmann (2008b) in a more

extensive treatment planning study incorporating actual patient data. The aim of the current

study is to extend the dosimetric investigation of mobile targets under respiration and evaluate

changes in target coverage due to irregularities in its motion.

2. Methods and materials

The dosimetric effects of respiratory motion need to be evaluated in relation to the specific

treatment delivery techniques used for compensation of the target motion. In this study, target

motion was incorporated into treatment planning via an expansion of the target volume by

an amount equal to the target excursion during the full respiratory cycle (i.e. no respiratory


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Irregular respiratory motion 847

Table 1. Patient target volume statistics and their anatomical location are listed. The number offields and conformity of the plan dose to the target are also shown in simple and complex geometryplans.

Simple plans Complex plans

Patient no Anatomic location CTV (ml) Nfields Conformity Nfields Conformity

1 Right lung (LL) 68.7 2 4.62 6 1.79

2 Left lung (LL) 77.4 3 2.06 5 1.82

3 Right lung (LL) 5.6 2 2.31 7 2.40

4 Right lung (LL) 58.2 3 3.45 5 1.85

5 Right lung (UL) 1.4 3 2.83 6 2.63

6 Right lung (LL) 91.6 3 4.64 8 1.85

7 Left lung (LL) 36.9 3 2.30 7 1.93

8 Right lung (ML) 207.6 3 2.79 6 1.74

9 Right lung (UL) 32.9 3 2.53 5 1.98

10 Right lung (ML) 3.3 2 2.32 5 2.37

11 Left lung (UL) 12.0 3 2.90 6 1.87

12 Right lung (ML) 23.7 2 9.12 6 2.02

13 Right lung (LL) 5.1 2 9.72 5 2.23

14 Right lung (ML) 4.9 3 8.72 5 2.71

15 Left lung (UL) 29.0 2 8.87 6 2.00

16 Right lung (UL) 6.0 3 4.45 5 2.08

17 Right lung (ML) 61.8 2 4.71 5 1.82

18 Left lung (LL) 35.6 3 2.96 6 1.93

19 Right lung (UL) 1.6 2 18.00 5 2.30

20 Left lung (UL) 39.2 2 5.75 5 1.89

21 Right lung (LL) 83.8 3 2.76 7 1.65

22 Left lung (UL) 20.9 3 3.03 6 1.98

23 Right lung (ML) 1.8 3 2.94 7 2.58

Average 39.5 2.6 4.95 5.8 2.06Minimum 1.4 2 2.06 5 1.65

Maximum 207.6 3 18.00 8 2.71

LL: lower lobe; UL: upper lobe; ML: middle lobe.

gating). Irregularities in respiratory motion are investigated using simulations of target motion

and analysis of respective clinical treatment plans.

2.1. Patient cohort and dosimetry

Twenty-three lung cancer patients previously treated in our clinic are retrospectively analyzed

through their informed consent according to an approved University of Pittsburgh institutionalreview board protocol. Clinical target volumes (CTV) ranged from 1.4 to 207.6 ml. Although,

prescription doses varied between 50.0 and 66.0 Gy for actual patient treatments, the

prescription doses were all normalized to 60.0 Gy to facilitate easier comparisons for the

effects of respiratory motion. A summary of patient information related to treatment plans

of the patients is provided in table 1. Due to the radio-sensitivity of lungs to low doses of

radiation (Oetzel et al 1995, Graham et al 1999), the clinical treatment plans were designed to

minimize irradiation volume and therefore consisted of two or three fields (usually in parallel


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848 Y D Mutaf et al

(a) (b)

Figure 1. Comparison of dose distributions in simple (a) and complex (b) geometry plans in axialand coronal views for patient 12.

opposed and oblique orientations). In this paper, such 3D conformal plans are referred to as

simple geometry plans where dose homogeneity within the target ranged between 95% and

115% of the prescription dose. Adopting the RTOG formulation of conformity (Shaw et al

2000) (defined as the ratio of 95% isodose volume to the target volume), the average target

conformity in simple plans was 4.95. In addition to simple geometry plans, an alternative

treatment plan was also generated incorporating more fields, ranging from five to eight, in

order to produce more conformal target dosimetry. These plans, referred as complex geometry

plans, achieved an average conformity of 2.06 in addition to improving dose homogeneity

within the target (95% to 105% of prescription dose). The typical dose distributions in simple

and complex geometry plans are demonstrated in figure 1 for one of the patients (patient 12).

The corresponding dosevolume histograms for target and bi-lateral lungs are also presented

in figure 2. In both type of plans, the selection of beams is restricted to the angles orthogonalto the longitudinal axis of the patients (transverse co-planar arrangement). All plans were

manually optimized to conform to the shape of the target with static 0.5 cm MLCs and a block

margin of 0.8 cm. Hard wedges were utilized for improvement of dose homogeneity. No

intensity modulation techniques and optimization engines (i.e. IMRT) are utilized.

Plans were further imposed with other constraints such as a maximum tolerable spinal

cord dose of 45.0 Gy and a mean heart dose of 40.0 Gy, although the latter constraint was

considered on a case-by-case basis depending on the location of the tumor.


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Figure 2. Comparison of target volume and bi-lateral lungs dosevolume histograms for simple(solid lines) and complex (dashed lines) geometry plans shown in figure 1.

2.2. Target selection and contouring

Target definitions and contouring adhered to ICRU guidelines (International Commission on

Radiation Units and Measurements 1993, 1999). Simulation images were acquired from

4DCT scans using a retrospective phase sorting technique producing ten phase images at

equal temporal intervals (i.e. 10% of respiration period). Following institutional protocols,

treatment simulation and planning was performed on the end-of-exhalation (EOE) phase

dataset extracted from the 4DCT scan (image in which the tumor volume is located most

superiorly, typically 50% phase image). The EOE image was preferred for planning since in a

typical respiratory cycle, longest amount of time is spent at this phase and therefore produces

3D images with least amount of motion artifacts. CTV was contoured on the EOE images

by the patients primary radiation oncologist with consideration for the subclinical extent of

the disease. The clinical ITV was finally generated using an asymmetric expansion, internal

margin or IM, equal to the amplitude of the target motion. In order to investigate the effects of

different amplitudes of motions, the CTV is enlarged by a specific IM amount which is chosen

to be equal to the amount of simulated motion and its direction (equation ( 1)):

ITVIM = CTV IM(x,y,z). (1)

In this formalism, ITV volumes are created by adding 1 and 2 cm margins to CTV in theinferior direction as demonstrated in figure 3. These final structures are referred as ITV1 cmand ITV2 cm within the text, where ITV1 c m = CTV1 cm and ITV2 c m = CTV2 cm..

2.3. Respiratory motion simulations

The mechanics of respiration induces an expansioncontraction motion observed mostly in

the cranio-caudal (CC) axis of the patient. Several radiological studies (Seppenwoolde et al


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Figure 3. An illustration of the target volumes (CTV and ITV) described in the text. Internaltarget volumes (ITV) are generated from clinical target volumes (CTV) via application of 1 and2 cm asymmetric internal margins in the inferior direction.

2002, Murphy et al 2002) have confirmed that largest motion occurs along the CC axis of the

patients. Therefore, motion simulations in this study are restricted to the CC axis of the patient

where CTV center-of-mass is changed with respect to the amount of simulated motion.

Respiratory motion simulations are performed via dose accumulation for voxels within

the CTV boundaries through the length of time the respiratory motion assessed. At each

dose sampling time, the target center-of-mass was moved with respect to the instantaneousamplitude of the respiratory motion but no deformations were allowed. Therefore, the total

number of target voxels and their coordinates within the target reference frame remain the

same. Due to the scan resolution of the CT images, target voxels have the dimensions of

1.00 1.00 mm2 in the axial plane and are separated by 1.25 mm slice thickness. Similarly,

the simulated motions were also digitized with respect to the scan resolution, i.e. 1.25 mm

since the simulated motion is in the longitudinal axis of the patient. In order to improve

processing time, the temporal resolution of the simulations are reduced such that the target

dose sampling is only performed at 10 equally spaced times within a respiratory cycle (i.e.

10% phase intervals).

Two main forms of respiration motion models are used for the simulations: regularand

irregularrespiration. In a regular respiration study, a single respiratory cycle with a functional

form represented in equation (2) was used for target simulations (Lujan et al 2003). A plot

of respiratory motion amplitude in this form is shown in figure 4. For simulation of a 1.0

cm peak-to-peak motion (A0), a 1 cm internal margin was used for CTV expansion to ITV

(ITV1 cm), and similarly, a 2 cm internal margin was used as the target volume for a 2.0 cm

peak-to-peak motion:

z() = A0 cos4( ). (2)

Irregular motion profiles used in the study were created similarly by generating 100

respiratory cycles which follow the functional form in equation (2). However, each respiratory


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Figure 4. Plot of the functional form of respiration used in the simulations. The peak-to-peakamplitude of the motion, A0, is used as the free parameter and adjusted accordingly as describedin the text.

cycle within a session was varied by changing its peak-to-peak amplitude (A0) sampled from a

Gaussian distribution with a preselected mean (A) and standard deviation (A). The examples

of simulated irregular respiration cycles and the corresponding distribution of peak-to-peak

amplitudes are shown in figure 5.

Irregular respiratory motion simulations are further divided into two categories.

Characteristic or random irregular motion where the irregularities in the respiratory

motion during treatment are distributed randomly about the motion amplitude attributed

to the CTV during the 4DCT imaging.

Uncharacteristic or systematically irregular motion where there is a systematic shift

between CTV motion amplitude determined at the time of 4DCT imaging and the actual

respiratory behavior of the patient during treatment.

In characteristic motion, the meanof the Gaussiandistribution for peak-to-peak amplitudes

is chosen to be equal to the internal margin. For example, a set of respiratory cycles distributed

about a mean of 1.0 cm (e.g. figure 5, upper panel) is characteristic for the simulations of CTV

motion using the plan generated with a 1.0 cm internal margin (ITV1 cm). In uncharacteristic

motion simulations, however, the internal margin between the CTV and ITV is smaller thanthe mean of the respiratory cycle amplitudes by 1 standard deviation. An example to such

respiratory motion would be a set of respiratory cycles distributed according to a mean of 1.5

cm and a standard deviation of 0.5 cm (e.g. figure 5, lower panel) used for the simulations

with only a 1.0 cm internal margin (ITV1 cm).

A characteristic irregular motion would, therefore, be the best-case scenario where the

irregularities are essentially random in nature and CTV motion as observed in the simulation

(e.g. with 4DCT) corresponds to the average target motion. In contrast, the uncharacteristic


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Figure 5. A time series plot of the simulated irregular respiration used in the study for the meanpeak-to-peak amplitude values (A) of 1.0cm (upperpanel) and1.5 cm (lowerpanel). A histogramof the cycle peak-to-peak amplitudes is plotted next to the corresponding plot of the respirationdata. Althoughthe continuous form of the full respiration datais shown (solid line), the simulationsare performed for respiration amplitudes sampled at phase intervals of 10% (filled circles). Thesampled motion is also digitized by 1.25 mm equal to the slice thickness of the CT images.

irregular motion reflects a more extreme case where in addition to random variations in

respiration, the determined CTV motion and internal margin are compromised by a systematic

difference. This systematic error occurs when the amount of motion determined at the planning

stage is consistently less than the average target motion observed during patients treatment.Characteristic and uncharacteristic irregular motion simulation parameters are listed

below.

Simulations with ITV1 cmA = 1.00 cm, A = 0.25 cm or 25% (characteristic)

A = 1.00 cm, A = 0.50 cm or 50% (characteristic)

A = 1.25 cm, A = 0.25 cm (uncharacteristic)

A = 1.50 cm, A = 0.50 cm (uncharacteristic).

Simulations with ITV2 cmA = 2.00 cm, A = 0.50 cm or 25% (characteristic)

A = 2.00 cm, A = 1.00 cm or 50% (characteristic)

A = 2.50 cm, A = 0.50 cm (uncharacteristic)

A = 3.00 cm, A = 1.00 cm (uncharacteristic).These a priori choices of the mean and the standard value of the respiration amplitude

distribution are intended to provide several threshold levels for their influence on target

dosimetry rather than exact representations of what is observed with actual patients. In this

respect, 25% variations as used in this study stand as a close approximation to what we

observed within our patient population as well as what is reported by other investigators

(Britton et al 2007). However, amplitude variations as large as 50% are not typically reported

in the literature and therefore constitute a limiting case.


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Figure 6. Respiratory motion trace of a patient (abdomen motion). The average peak inhale andexhale amplitudes are emphasized with blue and red horizontal lines, respectively.

In addition to the functional forms of irregular respiratory motion as described above,

patients actual respiratory waveforms were also used for target motion simulations; however,

these data were available only for a subset of patients, 13 out of 23 patients studied here.The respiratory signal represented the vertical motion of the abdominal surface recorded with

the aid of reflective markers placed on patients abdomen and an infrared camera as available

in a commercial respiratory management system (RPM, Varian Inc.). In clinical 4DCT

procedures, these patients were subjected to audio-coaching feedback methods for improving

the reproducibility of their respiratory pattern (Kini et al 2003, George et al 2006, Haasbeek

et al 2008).

The abdominal motion is used as surrogate for the actual tumor motion (Rietzel et al

2005, Mutaf and Brinkmann 2008a) and has been shown to have a high correlation with the

actual tumor motion in lung cancer patients (Ozhasoglu and Murphy 2002, Starkschall et al

2004, Gierga et al 2005). However, this correlation is only relevant for the relative amounts

of surrogate and target motions and therefore the surrogate motion is scaled in the simulations

such that the average respiratory cycle from the entire surrogate waveform was normalized

to selected peak-to-peak amplitudes. As described earlier, this normalization was chosen as

1.0 cm for the average peak-to-peak motion of the surrogate data in simulations for plans

with ITV1 cm and 2.0 cm for simulations with ITV2 cm. An example of patient respiration

waveform is demonstrated in figure 6. The average respiration cycle data are used for

regular motion simulation; however, the full respiration waveform is used for irregular motion

simulations.

In contrast to the synthesized respiratory motion data used for characteristic and

uncharacteristic irregular motion simulations, the actual respiratory trace of the patients

provides a more diverse spectrum of irregularities in breathing. While synthesized respiratory

simulations only represent variations in inhale amplitude, actual motion data also include such

irregular motion as variations in end-of-exhale (a.k.a. baseline-drift) as well as the presence

of outlier respiratory cycles. However, due to the use of single session data, the simulations of

irregular motion with patient motion traces only represent intra-fractional variations excludingpossible inter-fractional changes in breathing.

2.4. Plan evaluation metrics

Several metrics are utilized to assess the effect of irregular respiratory motion on dosimetric

coverage for CTV. Respiratory motion mostly affects the cells at the margins of the target

causing them to move outside the boundaries of the irradiation field and receive less than


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planned dose. Therefore, the target minimum dose (Dmin) is expected to be the most sensitive

metric for dosimetric evaluation of structures under motion. In addition, global dosimetric

evaluators such as the equivalent uniform dose (EUD) (Niemierko 1997, Choi and Deasy

2002) and tumor control probability (TCP) (Webb and Nahum 1993, Okunieffet al 1995) are

used for evaluations.The EUD is defined such that if a target is irradiated uniformly with this dose, it will

result in the same clonogenic cell killing as the actual inhomogeneous dose distribution across

the target volume. Assuming a homogeneous density of tumor clonogens within the target

volume and ignoring cell repair and repopulation effects, Niemierkos (1997) EUD formalism

was adopted for this study as shown in equation (3):

EUD = Dref ln

1N

Ni=1 (SF2)Di/Dref

ln(SF2). (3)

In equation (3), the reference dose, Dref, is defined as the reference dose per fraction and

usually taken as 2 Gy, whereas SF2, the surviving fraction of clonogens at 2 Gy, is set to 0.5.

Using these parameters, the average EUD values were 61.1 Gy (range 59.7 to 63.1 Gy) and

60.4 Gy (range 59.5 to 61.2 Gy) for simple and complex plans, respectively (note that the all

prescription doses have been normalized to 60 Gy in all patient plans for the purpose of thisstudy).

TCP is also calculated to assist the dosimetric evaluations for the target and assess any

dosimetric changedue to irregular respiratory motionin terms of itsrelative role in undermining

local control, if any. TCP is defined as the Poisson probability for achieving complete tumor

cell lethality (zero survival probability). Analysis of dose-response data from population-

based studies results in several empirical formulations for TCP and we implemented the logit

form as described in Okunieffet al (1995) and shown in equation (4):

TCP =e

450DD50D50

1 + e

450DD50D50

. (4)

Tumor control probability has a sigmoid shape which is modeled by the two free parameters

D50 and 50, dose to achieve 50% control probability and the slope of the sigmoid curve atD50, respectively. Following the multi-institutional analysis of the dose response data for

macroscopic lung tumors of Okunieff et al we adopted a D50 value of 51.2 Gy and 50 of

0.83. For this set of parameters, the average TCP values were 0.66 (range 0.640.69) and 0.65

(range 0.630.66) for simple and complex plans, respectively.

It is not the intent of the current study to evaluate the validity of these treatment plan

metrics or their selected parameters. These metrics are solely used to facilitate comparisons

between target dosimetry under irregular respiratory motion as compared to its regular motion.

Therefore, these metrics are only interpreted with respect to their relative values within

corresponding comparisons. Furthermore, we define a figure of relativity for each metric

discussed above that is equal to the ratio of the same metric for the target under irregular motion

to that under regular motion. This is formulated in equation (5). Following this approach, we

define rel-Dmin, rel-EUD and rel-TCP metrics for the targets and motions studied here:

rel-M= Mirreg/Mreg. (5)

3. Results

The result of characteristic irregular motion simulations with 1.0 and 2.0 cm amplitudes are

displayed in figure 7 for all 23 patients. The changes in the target minimum dose, represented

by rel-Dmin, are also shown for simple and complex plans.


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Figure 7. Relative target minimum dose (rel-Dmin) results of characteristic irregular motionsimulations for all patients; mean motion amplitudes of 1 cm (upper plot) and 2 cm (lower panel)are shown.

In characteristic motion simulations with a 1 cm average amplitude and 25% standard

deviation, the maximum change in the minimum dose to CTV was 0.1% for both simple

and complex plans. However no change in EUD or TCP was observed. A change in target

dose was slightly larger when the variations in respiratory motion increased to 50% standard

deviation with a maximum reduction of 0.6% in the minimum target dose for simple and 0.8%

for complex plans. For this motion, maximum changes in both EUD and TCP were 0.2% in

both simple and complex plans among all patients.

When the motion amount was increased to 2 cm in amplitude, changes in all dosimetry

metrics for CTV were larger for both 25% and 50% variations. The maximum change in the

minimum dose to a target was 3.7% in the case of 50% variations. For 50% variations,

the maximum EUD reduction was 2.1% and the TCP reduction was 3.0%. No substantial

difference between simple and complex plan dosimetry was observed.Target dosimetry for uncharacteristic motion is also presented in comparison with the

characteristic motion in figure 8 shown only for complex geometry plans. The effect

of uncharacteristic motion on simple plans again was not substantially different than the

complex plans. For plans with 1.0 and 2.0 cm internal margins in all patients, CTV dose

was compromised more drastically in uncharacteristic irregular motion simulations compared

to characteristic motion. The largest change in target dosimetry was observed for plan

with a 2.0 cm margin and associated uncharacteristic irregular motion ( = 3.0 cm,


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856 Y D Mutaf et al

Figure 8. Comparison of relative target minimum dose (rel-Dmin) in irregular motion simulationsfor characteristic and uncharacteristic motion traces; simulations with 1 cm (upper panel) and2 cm (lower panel) internal margins are shown.

= 1.0 cm) such that the minimum target dose was reduced by 13.6% as compared to

the regular motion.

Simulations of respiratory motion using the actual abdominal (surrogate) motion of

patients were also repeated for both 1.0 and 2.0 cm average motion amplitudes. The maximum

change in the target minimum dose was 0.1% in both cases using complex plans. The

maximum reduction in EUD was 0.3% and in TCP was 0.5% observed only with a 2 cm

motion. Simple plans yielded similar results.

The ranges of target dosimetry metrics among all the patients for all simulated motions

are summarized in table 2.

4. Discussion

In this study, an evaluation of the dosimetric effects of irregular motion was presented

for 23 lung cancer patients. Several types of irregular motion simulations are performed

to assess target dosimetry under various irregular motion conditions. In the process, the

interplay between the plan topology as demonstrated by simple and complex three-dimensional

dosimetry and the presence of irregular respiratory motion was also investigated.

Characteristic irregular motion type representative of random variations in the determined

(i.e.planned) target motion caused non-substantive dosimetric effects. In mostsimulated cases,


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Table 2. Range of target dosimetry evaluation metrics for the patient population investigated inthis study. Relative metrics as tabulated here are defined in equation (5).

Characteristic motion (IM = ) rel-Dmin rel-EUD rel-TCP

= 1.0 cm, = 0.25 cm (IM = 1.0 cm) 99.9100.0% 100.0% 100.0% = 1.0 cm, = 0.50 cm (IM = 1.0 cm) 99.2100.0% 99.8100.0% 99.8100.0%

= 2.0 cm, = 0.5 cm (IM = 2.0 cm) 99.5100.0% 99.8100.0% 99.7100.0%

= 2.0 cm, = 1.0 cm (IM = 2.0 cm) 96.299.6% 97.999.8% 97.099.7%

Un-characteristic motion (IM = )

= 1.25 cm, = 0.25 cm (IM = 1.0 cm) 99.0100.0% 99.8100.0% 99.8100.0%

= 1.50 cm, = 0.50 cm (IM = 1.0 cm) 91.699.3% 98.299.9% 98.199.8%

= 2.5 cm, = 0.5 cm (IM = 2.0 cm) 95.8100.0% 98.799.9% 98.499.9%

= 3.0 cm, = 1.0 cm (IM = 2.0 cm) 86.493.6% 92.799.0% 90.899.0%

Patient respiratory motion

Normalized to 1 cm (IM = 1.0 cm) 99.9100.0% 99.9100.0% 99.9100.0%

Normalized to 2 cm (IM = 2.0 cm) 99.0100.0% 99.7100.0% 99.5 100.0%

the change in the target minimum dose was clinically insignificant (less than 2%) to require

intervention or other compensation techniques. In the extreme scenario where the simulated

irregular motion had a mean of 2 cm and showed 1 cm amplitude variations (at 1 standard

deviation), the minimum target dose was dropped by 2.5% 0.9%. Although minimal, these

observations confirm the expectation that the dosimetric effect of characteristic irregularities

increases as the average amount of motion and its relative variations increases.

However, there is no assurance that the target motion as determined during patient

simulation (e.g. 4DCT) would be reflective of the average respiratory motion for the entire

course of treatment. For normally distributed motion data, there is a 50% probability that the

internal margin amount determined at simulation will be smaller than the average amplitude of

the motion. Dosimetric consequences for such scenarios are investigated in this study via the

simulations of uncharacteristic irregular motion where the chosen internal margin for the target

is smaller than the mean of the entire peak amplitude distribution by 1 standard deviation.

Uncharacteristic irregular motion resulted in clinically significant dosimetric

consequences with minimum CTV doses reduced about 8.4% for a 1.0 cm internal margin

(motion = 1.5 cm, = 0.5 cm) and up to 13.6% for a 2.0 cm internal margin (motion =

3.0 cm, = 1.0 cm). This observation signifies that such systematic discrepancies between

the determined target motion and the entire respiratory motion have more impact than the

random variations in respiration. In fractionated radiotherapy, an example of this discrepancy

could appear as inter-fractional variations of the respiratory motion between the simulation

session where target motion is determined and subsequent treatment sessions.

Results from the simulation of actual patient respiratory waveforms indicated that therespiration motion irregularities demonstrated by these patients did not cause detrimental

dosimetric consequences. This parallels our observations with characteristic irregular motion.

The similarity between the actual patient respiration motion and generated characteristic

irregular motion simulations could be attributed to the fact that the patient respiratory data

were collected with the help of audio-coaching techniques. Since coaching compels the patient

to reproduce a respiratory pattern, the breathing irregularities are consequentially restricted

to random fluctuations. Therefore, it can be argued that the coaching techniques help avoid


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858 Y D Mutaf et al

the occurrence of uncharacteristic respiratory motion which was observed to result in largest

degradation for target dosimetry.

5. Conclusion

Determination of respiratory motion is a necessary first step for the management of respiratory

motion in radiotherapy. The process also requires that determined respiratory motion is valid

throughout the full course of patients treatment. However, this validity is compromised

due to the varying degrees of breathing irregularities. In this study, we assessed the clinical

implications and dosimetric consequences of these variations and categorized different types

of breathing irregularities. Characteristic motion irregularities are not observed to have a

considerable clinical effect on target dosimetry. Similarly, simulations of target motion using

actual patient respiration data have not produced a clinically substantial dosimetric effect.

However, uncharacteristic irregularities are shown to have critical dosimetric consequences

for the target coverage. Coaching techniques are suggested as potential ways to avoid such

inconsistencies and maintain a common baseline for patients respiration throughout the course

of radiotherapy. Additionally, there was no substantial difference between the simple andcomplex treatment plan geometries incorporating different levels of target dose homogeneity

and conformity.

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a simulation study of irregular respiratory motion and its dosimetric impact on lung tumors

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