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omputed Tomography Guidedanagement of Interfractional Patient Variation

i Yan, DSc, David Lockman, DSc, Alvaro Martinez, MD, John Wong, PhD,onald Brabbins, MD, Frank Vicini, MD, Jian Liang, PhD, and Larry Kestin, MD

Interfractional patient variation occurs regularly and considerably during the radiotherapycourse. Consequently, a generic but large planning target margin has to be applied whenpatient treatment plan design based on a single pre-treatment computed tomography scanis used to guide multifraction radiation treatment, which creates a major limiting factor forradiotherapy improvement. Planning target margins can be significantly reduced usingmultiple (or 4-dimensional) image feedback management in the routine treatment process.The most effective method in multiple-image feedback management of radiotherapy is theadaptive control methodology. The adaptive radiotherapy technique aims to customizeeach patient’s treatment plan to patient-specific variation by evaluating and characterizingthe systematic and random variations through image feedback and including them inadaptive planning. Adaptive radiotherapy will become a new treatment standard, in whicha predesigned adaptive treatment strategy, including the schedules of imaging and replan-ning, will eventually replace the predesigned treatment plan in the routine clinical practice.Semin Radiat Oncol 15:168-179 © 2005 Elsevier Inc. All rights reserved.

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nterfractional patient variation implies the differences inpatient anatomic position and shape appearing at treat-ent delivery with respect to those at treatment simulation.hese differences may arise from various sources, such asatient positioning and motion, organ filling and wriggling,espiratory motion, and cardiac motion. In addition, radia-ion dose response-induced organ motion can also be con-iderable. It has been commonly observed that interfractionalatient variation occurs regularly and considerably duringhe radiotherapy course. Consequently, a generic but largelanning target margin has to be applied when a single-plan-ing computed tomography (CT) scan is used to guide mul-ifraction radiation treatment, which creates a major limitingactor for radiotherapy improvement.

The planning target margin can be significantly reduced byystematically managing patient-specific variation. However,he management of interfractional patient-specific anatomicariation requires multiple measurements of patient anatomyccomplished by different image feedbacks. Among them,T image feedback, including both onboard cone-beam CTnd off-board conventional CT scans, has been most com-

epartment of Radiation Oncology, William Beaumont Hospital, Royal Oak,MI.

ddress reprint requests to Di Yan, DSc, Department of Radiation Oncology,William Beaumont Hospital, Royal Oak, MI 48073. E-mail: dyan@

sbeaumont.edu

68 1053-4296/05/$-see front matter © 2005 Elsevier Inc. All rights reserved.doi:10.1016/j.semradonc.2005.01.007

only applied in clinics. The purpose of this article is tontroduce techniques of CT-guided management of interfrac-ional patient variation and discuss their potential for themprovement of radiation treatment. In the first section,haracterization and modeling of interfractional patient vari-tion will be described. In the second section, the negativeffects of patient variation as well as the improvements ob-ained from using multiple CT image-guided radiotherapyill be outlined. CT-guided techniques, some of which haveeen applied in routine clinical practice and some of themaving been proposed and currently being implemented, wille discussed in the third section. Finally, in the last section,e will outline our experience with and clinical results ofultiple CT image-guided prostate cancer and lung cancer

reatment.

haracterizationnd Modeling of Interfractionalatient Variation

nterfractional patient variation indicates the discrepancies inatient anatomic position and shape between the time peri-ds of pretreatment simulation and treatment deliveries. In-rafractional patient variation, on the other hand, implies theiscrepancies appearing within each time period of treatment

imulation or delivery. Major causes of interfractional patient

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Management of Interfractional Patient Variation 169

ariation are patient positioning, organ filling, and tumorhrinkage. In addition, intrafractional patient variations,uch as patient respiration-induced organ motion, cardiacotion, bladder filling, and so on, are also sources of inter-

ractional patient variation. In principle, intrafractional pa-ient variation is a subprocess of interfractional patient vari-tion, which can be managed by using the same methodologys that for managing interfractional patient variation, unlessnline gating1 or tracking2,3 techniques are applied. An inter-ractional patient variation may have a constant mean valuend a constant standard deviation during the entire course ofreatment; therefore, it is a stationary process. Examples ofonstationary processes are usually dose response relatednd include (1) reexpansion of the lung after atelectasis, (2)rgan filling that may be affected by radiation dose, or (3)hape and position variations of normal organs adjacent to ahrinking target.

Patient/organ geometric variation has been traditionallyuantified using rigid body displacement of bony anatomy orsingle point of a soft organ, such as the center of mass or aoundary point. Using deformable organ registration to trackrgan shape changes4 has grown increasingly popular re-

Figure 1 Organ subvolume displacement distribution mposterior direction for bladder and rectum (the first col

ently in radiotherapy because of the feasibility of multiple o

T imaging or 4-dimensional (4D) CT imaging and the desireo apply adaptive treatment planning. Compared with rigidody motion that assumes no shape change, deformable or-an registration provides all details of local shape change of aeforming organ. Therefore, it provides more informationnd also brings extra challenges to radiotherapy practice.igure 1 shows the displacement (standard deviation) distri-utions of organ subvolumes determined by using deform-ble organ registration on multiple CT images obtained atultiple treatment deliveries of a prostate cancer patient.ne can expect that the detailed variation information will, in

he near future, be included in routine clinical planning todaptively optimize planning dose distribution.

Early models of interfractional patient variation have beenatient population-based. Directional displacements of pa-ient and internal organ position for a given treatment siteave typically been statistically characterized by using 2 pa-ameters; the mean, Mp, of all displacements and the standardeviation, �p, of all displacements of a given group of pa-ients. (Figure 2A shows a typical plot of interfractional pa-ient setup variation during the prostate cancer treatment.)he patient population-based statistics provide information

d by using the standard deviation (cm) in the antero-nd prostate and seminal vesicle (the second column).

easure

f a global bias between treatment simulation and delivery

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ndicated by the mean, Mp, and a generic distribution ofariations represented by the standard deviation, �p. Nor-ally, nonzero Mp indicates a discrepancy of patient setup

rocedures and/or machine miscalibration between simula-ion and treatment. However, some procedures used in CTimulation to intentionally control patient anatomy also in-roduce a global bias Mp. A typical example is the use of annema to empty the patient’s rectum for prostate cancer treat-ent planning. The generic information characterized by theparameters {Mp, �p } as discussed in the next section, can beirectly used to design a generic margin for treatment plan-ing target but has the limitation that it lacks individual

nformation.Rabinowitz et al5 pointed out in the early 80s that systematic

ariation and random variation of tumor and organ location ofhe individual patient should be considered separately becausef their different effects on treatment. The systematic variation,

i, of the individual patient i has been defined, in that study, ashe average of treatment-to-treatment variations, whereas theandom variation, on the other hand, was the treatment-to-reatment variation characterized by using the correspondingtandard deviation, �i. When the patient-specific mean andtandard deviation are used to characterize individual variation,he heterogeneities of individuals’ variation become obviousFig. 2B and C). Four statistical parameters have been used6,7

o characterize the distribution of patient-specific mean andtandard deviation, which are (1) the mean M��i�

and the stan-ard deviation ���i�

of the individual means and (2) the root-ean-square RMS��i�

and the standard deviation ���i�of the

ndividual standard deviations. Of these 4 parametersM��i�

, ���i�, RMS��i�

, ���i��, M��i�

contains the same informations the population-based mean Mp. The standard deviation ofndividual means ���i�

and the average of individual standardeviations RMS��i�

, which ideally have similar magnitude,8 haveeen commonly used to quantify the magnitude of the genericariation. It can be shown7 that the population-based statisticalarameters and the individual-based parameters are related by

p � M��i�and �p � ����i�

2 �RMS��i�2 (note that the equality

olds when the number of measurements used to calculate thendividual mean and the individual standard deviation is largenough; otherwise, it is only approximately equal).

Interfractional patient variation can be characterized bysing either the 2-parameter {Mp, �p} model to characterizehe distribution of the generic variation or more completelyy the 4-parameter �M��i�

, ���i�, RMS��i�

, ���i�� model to

haracterize the distribution of the patient-specific mean andtandard deviation. In principle, the 2 models share a similarunction in the design of generic margins for the planningarget volume, but the 4-parameter model provides morentrinsic information to guide the management of interfrac-ional patient variation with multi-image feedback. Basically,he deviations of individual means and individual standardeviations (ie, ���i�

and ���i�), indicate the potential for

lanning target volume reduction, when patient-specificanagement is performed. A large ���i�

implies that a largeeduction in treatment volume is achievable when using off-

igure 2 Setup errors (RL direction) obtained from 535 prostateancer patients; A was quantified using the 2-parameter model, and

ine correction of individual systematic variation, whereas a

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Management of Interfractional Patient Variation 171

arge ���i�implies that extra reduction is possible when ap-

lying offline planning modification to compensate for indi-idual random variation (here, offline refers to processes thatake place between treatment sessions).

ffects ofnterfractional Patientariation and CT-Guidedanagement on Treatment

etrimental effects of interfractional patient variation onreatment have been extensively studied in the last 20 yearsnd can be divided into 3 categories: (1) potential reductionf prescribed target dose and therapeutic gain, (2) uncertain-ies in organ dose-volume determination, and (3) large ge-eric margin for planning target volume.

ffect on Target Underdoseomputer simulation studies9-12 have shown that dose re-uction in part of the clinical target could be significant if thelanning target margin is not adequately designed. By usinglinical patient data of multiple CT and portal image mea-urements, we have performed a retrospective study13 re-ently to test the adequacy of 1-cm uniform planning targetargin for conventional prostate cancer intensity-modulated

adiation therapy (IMRT) therapy (ie, an IMRT plan based onsingle preplan CT image and standard fractionation

cheme). An average of 17 CT scans per patient and dailyortal images obtained from treatments of 22 prostate canceratients have been used in the study to reconstruct the cu-ulative dose for each patient treatment. The target dose-

olume relationship evaluated using the generalized equiva-

Figure 3 Reduction of target equivalent uniform doseprostate cancer treatment: the conventional IMRT techideal online image-guided IMRT (IGART).

ent uniform dose was compared with the planned target o

ose. The results shown in Figure 3 show that 9% of patients2/22) can suffer severe underdose in the target, and an ad-itional 25% patients might experience moderate underdose.onsequently, the quality of a conventional IMRT treatmentsing a 1-cm uniform planning margin or less should beuestioned. However, as has been pointed out by Ni-mierko14 recently, the consequence of target underdose be-ause of interfractional variation in a relatively small group ofatients (10%�20%) cannot likely be evaluated by using thectual clinical outcome data unless underdosing is severe forost patients. Therefore, the appropriateness of a genericlanning target margin, although a very important clinicaluality assurance issue, may not be testable with existinglinical data or using a clinical trial.

ffect on Uncertainties inrgan Dose-Volume Determination

ncertainty in organ dose-volume determination based oningle-planning CT image is the other negative effect of inter-ractional patient variation. Variation in the position andhape of an organ adjacent to the treatment target clearlyauses variation of the dose distribution in the organ volume,onsequently increasing uncertainties in the organ dose-re-ponse evaluation. Dose distribution in critical normal struc-ures is often the basis for treatment decision and optimiza-ion in 3-dimensional (3D) conformal and IMRT treatmentlanning. Therefore, knowledge of the actual organ dose dis-ribution from treatment delivery is an important componento the improvement of radiation therapy. However, thisnowledge cannot be obtained without an effective methodo quantify the cumulative dose.

The calculated dose distribution in organs of interest based

because of interfractional patient/organ variation forith 1-cm planning target margin (conv IMRT) versus

(EUD)nique w

n a single pretreatment CT image is often implicitly as-

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172 D. Yan et al

umed to be the actual dose distribution accumulated in thergans during the entire course of radiotherapy. However,he veracity of this assumption is dubious. As has beenointed out by numerous investigators, patient anatomyanifested on the planning CT image represents only a single

napshot of the organ configurations and positions. There-ore, significant bias could be introduced in the planningecision and outcome analysis if patient/organ geometry var-

es during the treatment process, particularly if the planningnapshot represents an atypical configuration. Figure 4A andshow the daily dose-volume histograms (DVHs) of the rec-

al and bladder walls of 1 prostate cancer patient constructedy applying a treatment plan to the multiple CT images ac-uired during the treatment course. In each of the organs, theVH calculated at initial treatment planning (dash-doturve) deviates substantially from the daily DVHs. Conse-uently, inappropriate treatment decisions could be made.or example, if the dose in 30% of the rectal wall volumehorizontal line in Fig 4A) was used as the constraint for therescription dose design, a 40% difference in the choice ofrescription dose could result depending on the time of the

igure 4 Daily rectal wall (A) and bladder wall (B) DVHs (dash-doturve, planning DVH) for a patient in prostate cancer treatment. Theercent dose is normalized to the prescription dose at the target

socenter.

lanning CT imaging acquisition. Comparing this uncer- e

ainty to the dose escalation level (5%�10%) tested currentlyor prostate cancer treatment, we should question the confi-ence of using rectal and bladder DVHs calculated at treat-ent planning based on 1 single-planning CT image. Theose discrepancy in the previous example is quite typical inrostate cancer treatment. Figure 5A and B show the doseiscrepancy at 30% rectal wall volume and 50% bladder wallolume for 22 prostate cancer patients who had an average of7 CT images during the course of 3D conformal treatment inur clinic. In addition to the uncertainty in organ cumulativeose, fraction dose deviation caused by interfractional patientariation introduces extra uncertainties in the evaluation ofadiobiological effect of normal organs. As has been pointedut in a previous study,8 fraction dose-volume deviation,hus biological effective dose deviation, in a critical organdjacent to treatment target could be significant. This devia-ion can be magnified by a radiotherapy treatment with amall number of fractions (hypofractionation) or a high-doseradient around the target edge. Therefore, it is essential toevelop a reliable method for organ dose-volume evaluation

n hypofractionated or IMRT treatment.

igure 5 Difference of daily dose (%) at 30% rectal wall volume (A)nd 50% bladder wall volume (B). � denotes mean absolute differ-

nce; - denotes maximum absolute difference.

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Management of Interfractional Patient Variation 173

ffect on Genericargin for Planning Target

he major detrimental effect of patient position and anatomichape variation is probably the requisite necessity of a largeeneric margin for the planning target volume. A genericlanning target margin has been traditionally selected to en-ure adequate prescription dose in the target for majority ofatients. As has been pointed out by Goitein15 in the early0s, the planning target margin can be simply designed as aultiple (1.5�3) of the standard deviation, �p, in the 2-pa-

ameter {Mp, �p } model based on a preselected confidenceevel, if the global bias Mp is small. As shown in Figure 2, the

argin to compensate for patient setup errors should be.5 � 3.1 mm � 4.7 mm for 85% confidence level or 2.27 �.1 mm � 7 mm for 98% confidence level. However, thisimple recipe for planning target margin selection does notrovide clear information of the risk of potential target un-erdose. For example, with respect to the 85% confidence

evel, mistargeting in 15% of treatment fractions for everyatient or mistargeting for 15% of patients in all treatmentractions have very different consequences for therapeuticutcome. The first may only cause slight underdosing in amall volume around the tumor edge. But the latter will causeignificant under-dosage of the target for the group of 15%atients. Unfortunately, in clinical practice, the latter is most

ikely. It has been proved in theory8,16 and also shown usinglinical patient data7,8 that the random variation and system-tic variation in patients are not mutually independent, and aroup of patients who show large random variations in theirreatment position also have higher probability to have largeystematic variation. Therefore, a large confidence level�98%) or margin �2.27 � �p may be needed when onepplies the simple margin recipe to select planning targetargin.The other recipe11,12 for generic planning target margin

election has been derived from the 4-parameterM��i�

, ���i�, RMS��i�

, ���i�� model. Assuming a small global

ias �M��i�� 0� and uniform random variation across all

atients ����i�� 0�, van Herk et al12 have proposed a generic

argin recipe as 2.5����i�� 0.7�RMS��i�

to ensure that theinimum dose to CTV is 95% for 90% of patients with an

deal conformal treatment plan. This margin recipe indicateshat within the generic margin, the portion to compensate foratient systematic variation is approximately 3.5 times largerhan that to compensate for the patient random variation. Inrinciple, the planning target margin determined by usinghis recipe is similar to the one determined by using theimple margin recipe derived from the 2-parameter model,lthough different criteria and statistical confidence haveeen used in their designs. In fact, using the relationship

p � ����i�2 �RMS��i�

2 and considering ���i�� RMS��i�

, itollows that 2.5����i�

� 0.7�RMS��i�� 2.263��p. This

mplies that the generic margin recipe derived using the 4-pa-ameter model is equivalent to the simple recipe derived bysing the 2-parameter model with about 97% confidence

evel.

The generic margin recipe proposed by Stroom et al11 and o

an Herk et al12 can be generalized as �·M��i�� c1·���i�

�·�RMS��i�

� c2·���i�� by considering all 4 parameters in the

-parameter model. The first term in this generalized recipeepresents the margin to compensate for the global bias. Con-ribution of the global bias to the total planning target marginn any given patient direction (eg, anterior-to-posterior, left-o-right, or inferior-to-superior) can be either positive or neg-tive depending on the sign of M��i�

and the factor �, whichquals �1 or �1 based on the patient direction (coordinateign). As has been discussed, the sources of the global biasould be quite different. For a miscalibration between treat-ent simulation and delivery, one should identify the source

f the bias and eliminate it. However, for bias caused by thentended procedures in CT simulation, one has to determine

proper margin to compensate for it. Considering patientnterior (positive)-to-posterior (negative) direction in a pros-ate cancer treatment planning, the intended empty rectumor patient in planning CT causes a positive global bias or

��i� 0. Therefore, target posterior margin will be reduced

ecause � � � 1 and the target anterior margin will bencreased at same amount because � � � 1 which is ofourse favorable for the appearance of the planning DVH ofectum. However, it is important to understand that a non-ero global bias because of the intended simulation proce-ure should not reduce the size of planning target volume,ut rather introduces a nonuniform planning target margin,esulting in a systematic bias between the planning dose vol-me and the treatment dose volume of a normal organ. Theecond term is the margin to compensate for the systematicariation. Because it is desirable to achieve a high confidenceevel for systematic variation, c1 � 2 or 2.5 has been usedrefer to Fig. 2B). The third term is used to compensate for theandom variation. The parameter c2 (� 1�2) can be selectedo ensure that the number in parenthesis characterizes anpper bound of the individual standard deviations (refer toig. 2C). The parameter normally depends on the magni-ude of random variation and dose distribution shape aroundarget edge. It can have a value from 0 to 1.6 for typical rangesf random variations and treatment techniques.16 In additiono accurately prescribing a generic planning target margin,he margin recipe can also be used to understand the signif-cance of image feedback management of patient-specificariation. Considering the typical example (Fig. 2) of patientetup error in prostate cancer radiotherapy with ���i�

�.7 mm, RMS��i�

� 1.5 mm,���i�� 0.9 mm, and assuming

��i�� 0, the generalized margin recipe with c1 � 2.5, c2 �

, and � 0.2 (the value of 0.2 was selected16 with respect tohe conventional conformal treatment and the upper boundandom error of 2.4 mm) produces a 7.2-mm generic setupargin for planning target (no internal organ motion was

onsidered). Now, if we can apply a multiple image feedbackechnique to reduce the systematic variation ���i�

to 1.5 mm,he target margin will be reduced to 4.2 mm. In addition, ifhe individual or patient-specific systematic variation andandom variation {�i, �i} can also be estimated and consid-red, with the estimation residuals, in the margin design (theetails have been described Yan et al16, Appendix B), we will

btain the patient-specific margin distribution plotted in Fig-

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re 6, indicating planning target margin of 2 mm or less for0% of patients and 4 mm or less for at least 90% of patients.argin reduction from 7.2 mm of the generic margin to an

verage 2-mm patient-specific margin (the treated volumeecreases by more than one half for an approximate 5-cmiameter tumor) is clearly significant for radiotherapy. How-ver, to achieve it, we have to develop a method to evaluateatient-specific systematic variation and random variationefore or early in treatment. The most common methods toanage patient-specific variation so far are using multiple-

mages feedback to perform patient-specific target positionorrection and treatment planning modification. In the nextection, some of these techniques will be outlined.

T-Guided Evaluation,ffline Correction, anddaptive Planning Modification

he effect of patient variation on treatment dose can be fullyetermined with the knowledge of patient-specific system-tic and random variations.9 Therefore, an interfractional pa-ient variation can be effectively managed by including itsystematic and random components in the treatment plan-ing or dose-distribution design. However, to evaluate pa-ient-specific systematic and random variations, multiple-im-ge measurements inquired during the treatment course aressential. Multiple CT image-guided treatment is a practicalrocess to adapt the treatment plan and dose-distributionesign to patient-specific variations. Objectives in the designf this process are commonly (1) to improve treatment accu-acy and reduce the treated volume by correcting the system-tic variation, (2) to adapt the dose distribution to patient-pecific random variation and residuals, (3) to reduce thereated volume by correcting both systematic and randomariations, and (4) to improve treatment efficacy by modu-

igure 6 Distribution of individual planning target margin deter-ined by considering the residuals of the systematic error estima-

ion and compensation for the individual random error.

ating daily dose per fraction and number of fractions. t

learly, the objectives have to be selected based on expectedreatment goals and available technologies. The first 2 can bemplemented by using an offline image feedback, correction,nd planning modification technique. On the other hand,ither online (at the time of treatment) image-guided adjust-ent and planning modification, or real-time image-guided

ating and tracking techniques for patient respiration-nduced target motion, must be implemented to achieve ob-ectives 3 and 4. Most offline correction techniques havemplemented the adaptive planning and patient position cor-ection once during the treatment course, except for the casen which a large residual appears. In addition, most onlineorrection techniques have aimed to adjust patient treatmentosition only by moving the couch and/or beam aperture.owever, it may be best to implement a hybrid technique, inhich offline adaptive planning is performed to modify thengoing treatment plan at certain time intervals (eg, weekly)uring the online daily correction process.An offline adaptive process, which corrects patient-spe-

ific systematic variation and includes the residuals and pa-ient-specific random variation in the treatment replanning,as been clinically shown to be an efficient method to reducelanning target margin and improve treatment outcome forrostate cancer radiotherapy.17-19 This closed-loop image

eedback adaptive treatment process includes procedures ofmage-guided evaluation of patient/organ position and shapeariation, offline correction for patient-specific systematicariation, offline planning modification (4D adaptive plan-ing), and image-guided treatment verification of patient/rgan position and shape. This process has been used in ourlinic for prostate cancer treatment for over 5 years. Moreecently, the closed-loop image feedback treatment processo manage patient respiration-induced interfractional organariation has also been implemented in our clinic.20,21 Nu-erous imaging techniques can be applied in an offline pro-

ess to observe patient-specific patient/organ variations.mong them, CT imaging, including both onboard/offboardone-beam CT and conventional CT, has been one of therimary imaging modalities in the clinical implementation.

T-Guided Evaluation/Verificationrequent CT imaging of patient/organ position and shapeuring the treatment course is a feasible means to verify pa-ient/organ variation in radiotherapy and evaluate the pa-ient-specific systematic and random variations. Ideally, im-ging should be performed with the patient in the treatmentosition and with an imaging schedule compatible with therequency of the patient/organ variation considered. Threeypical sources of patient/organ variation can be classifiedased on their variation frequency. Among them, dose re-ponse-induced organ variation is the slowest one. Next isatient setup and organ filling-induced organ variation. Theost rapid one (except for heart motion) is, of course, theatient respiration-induced organ variation. Because theseariations occur with different frequencies, the CT imagingchedule and techniques of evaluating patient-specific sys-

ematic variation and random variation are also different. In

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Management of Interfractional Patient Variation 175

his section, we only provide a simple outline of each tech-ique without including the detailed evaluation model andlgorithms. However, the interested reader can find them inhe studies by Yan,22 Hoogeman et al,23 and Lotz et al.24

A tumor, specifically an advanced tumor, shows significanthrinkage in its volume during the late course of radiother-py, thus causing position and shape variations to those ad-acent normal organs. Barker and his colleagues25 have re-orted the changes in volume and position of the parotidland during the radiotherapy of advanced head and neckumors. In addition, normal organ function affected by radi-tion dose, such as reexpansion of the lung after atelectasisnd rectal filling state changes because of radiation dose-nduced patient diarrhea, also causes variations. Because ofhe relatively slow changes, weekly CT could be sufficient tovaluate these variations. However, the nonstationary featuref the variation (time-dependent patient-specific mean andtandard deviation) complicates the evaluation.

Patient setup and organ filling-induced patient/organ vari-tion have been clinically evaluated by using multiple dailyT imaging. Because of the relatively stationary feature, esti-ation of individual systematic variation and random varia-

ion of organ position and shape can be performed by usingultiple daily CT images obtained early in the treatment

ourse, in batches or continuously. In general, the imagingchedule is selected in a treatment protocol based on a pre-esigned CT-guided treatment strategy. In the case of singleffline correction for patient-specific systematic variation andlanning modification for residuals and patient-specific ran-om variation during the treatment course of prostate cancer,n imaging schedule of planning CT image plus 4 CT mea-urements obtained daily in the early treatment course haseen used.16 This study shows that planning target volumeonstructed by using the 5 CT images is adequate with re-pect to target dose and is reduced significantly comparedith the generic one. In addition, a recent study26 on 4D

daptive inverse planning for prostate cancer radiotherapyas shown that there was only a marginal improvement whenffline-planning modification was continuously performedompared with a single modification after 5 measurements.n general, optimal selection of appropriate CT imagingchedule is a nontrivial problem, which is closely dependentn tumor site, variation magnitude and treatment technique.Patient respiration-induced organ variation can be most

ffectively managed by using an offline CT-guided technique.he individual mean position and random variation of pa-

ient respiration-induced organ motion can be evaluated be-ore treatment planning by using respiratory correlated CT orone beam CT images.27,28 A respiratory correlated CT images often referred to as a 4D CT image, although in principleny multiple CT images to observe patient variation comprise4D CT image. One important application of 4D CT images

s, of course, to include patient-specific variation in the treat-ent planning (4D adaptive planning). It has been shown

hat the planning target margin to compensate for patientespiration-induced target motion can be minimized signifi-antly by including the motion in the planning dose distri-

ution and the prescription dose design.20 In this case, the p

reatment plan is adapted to the mean respiratory target po-ition and treatment dose distribution will be adapted to therequency distribution of the motion. However, studies29,30

hat have attempted to determine the reproducibility of pa-ient breathing induced organ motion have revealed that theean position of patient respiration-induced organ motion

ould vary considerably during the course of lung cancerreatment. Figure 7 shows the changes of the mean and thetandard deviation of patient diaphragm displacement mea-ured with respect to the same bony structure reference dur-ng the course of lung cancer radiotherapy. The changes inhe mean respiratory target position could be both organ doseesponse and patient setup position dependent; therefore,ultiple portal fluoroscopy-based21 or 4D cone-beam image-

ased verification and correction becomes necessary. In ad-ition, repeat 4D CT, possibly once a week, may be necessaryo manage adaptive treatment of lung cancer.

ffline Correction of Patient-pecific Systematic Variationfter CT-guided evaluation of patient-specific systematic varia-

ion and random variation, offline treatment adjustment can beerformed to correct the systematic variation. Correcting pa-ient-specific systematic variation has commonly been imple-ented by adjusting the relative position of the patient with

espect to the beam or adjusting the beam aperture. Of the 2,djusting the beam aperture clearly enjoys the advantage of be-ng able to correct the systematic changes of organ shape, be-ause primarily of organ deformation and rotation, projected onhe beam-eye view. However, because beam aperture adjust-ent is considered as treatment replanning whereas adjusting

ouch position and collimator rotation does not, the former isurrently limited in clinical implementation. It is straightfor-ard to implement a correction for patient-specific systematicariation; however, the potential residuals in the correction pro-ess could be problematic and should be considered in the off-ine planning modification. There are different sources of resid-als in the CT-guided correction process, such as uncertainties

n the calibration between the imaging device and dose deliveryevice, image registration, estimation of the systematic varia-ion, machine calibration, and limitations in the method of ap-lying the correction. Generally, these residuals should be eval-ated individually and compensated for in the planning targetargin.

D Adaptive Planning Modificationhe aim of adaptive treatment planning is to design and modify

he treatment dose distribution in response to patient-specificandom variation and the residuals resulting from the evaluationnd correction of the systematic variation. It is important tonderstand that the planning target margin to compensate foratient-specific random variation is closely dependent on theose distribution around the target periphery. For a knownistribution of patient-specific random variation (ie, patient res-iration-induced organ motion), a dose distribution can be de-igned such that no geometrical margin is necessary between the

rescription dose and clinical target edge. This therefore indi-

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ates the potential of adaptive treatment planning in which pa-ient-specific variation must be included in the planning opti-ization. Compared with conventional treatment planning,

daptive treatment planning commonly includes the temporalatient/organ variation observed from multiple 3D images (orD image) in the planning dose evaluation and therefore is alsoesignated 4D planning. Dose-modifying parameters, such aslanning target margin, beam aperture, beam weight, and/or

Figure 7 Temporal variations (weekly) of the mean andiaphragm motion.

eamlet intensities, are typically used to adjust dose distribution p

n the region of interest to improve the quality of ongoing treat-ent. In addition, other parameters, such as prescription dose,ose per fraction, and number of fractions, can also be consid-red for a 4D adaptive planning optimization.31

Two basic methods have been proposed and applied clini-ally to perform 4D adaptive treatment planning. The first orndirect method does not directly include patient-specific vari-tions in the planning dose calculation. Instead, it constructs a

dard deviation of patient-specific respiration-induced

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Management of Interfractional Patient Variation 177

rgan at risk, based on the characteristics of patient-specificariation as well as a generic dose distribution, and then per-orms conventional conformal or inverse planning accordingly.he second or direct method performs treatment planning byirectly including patient-specific variations in the planningose calculation. The adaptive treatment plan designed with thexpected dose distribution can then best compensate for theiven patient-specific variations. Consequently, predesignedarget margin is either unnecessary or used only to compensateor the residuals in the evaluation. The indirect method has beenmplemented in the clinic for many years,16 whereas the direct

ethod, on the other hand, has been, so far, limited to modelingnd simulation studies.26,32,33

D Adaptive Planning With Indirect Methodhe planning technique with the indirect method is primarily

he same as the conventional one, except for the constructionf the planning target margin and the margins for organs atisk. The patient-specific planning target margin consists of 2arts: the margin to compensate for the residuals resultingrom the offline evaluation and correction and the one toompensate for the patient-specific random variation. Be-ause the residuals are most likely systematic, the marginhould be selected to fully compensate for them. On the otherand, the margin to compensate for the patient-specific ran-om variation is designed by considering the distribution ofhe variation and the planning dose distribution, which is, ineneral, calculated by using a convolution method with re-pect to a predefined target dose tolerance.16 Because patient-pecific PTV construction is also dependent on the planningose distribution, primarily the dose gradient and curvature

n the neighborhood of the CTV periphery, control parame-ers to directly adjust the dose gradient and curvature aremportant for the adaptive treatment planning. A typical in-irect method for 4D adaptive planning of prostate cancerreatment has been systematically studied and applied in theoutine clinical practice.16 Using the first week of portal im-ging and daily CT imaging, 4D adaptive planning is per-ormed to both correct systematic variations by recalibratinghe beam apertures and to compensate for residuals and ran-om variations by design of a patient-specific PTV. The newTV and apertures are applied for the remaining treatments.

D Adaptive Planning With Direct Methodn the direct method of 4D adaptive planning, patient-spe-ific variations evaluated during the previous treatment areirectly included in the planning dose calculation and opti-ization. Consequently, the dose distribution around the

dge of the target can be designed by selecting the beamperture or modulating the beam intensity fluence to effec-ively compensate for the residuals and the patient-specificandom variations. Most 4D adaptive planning has been per-ormed by using an inverse planning engine that searches forhe optimal beam intensity fluence based on an objectiveunction calculated with the expected dose evaluated by in-luding the patient-specific variations of organ position and

hape. The objective functions and search algorithm com- i

only remain the same as those in conventional inverse plan-ing, but dose computation is much more time consuming,pecifically when feedback volumetric CT images need to bencluded in the dose computation. Most investigations of 4Ddaptive inverse planning have been limited to modeling andypothetical simulation with preassumed distribution of in-erfractional patient variation. One recent study26 has directlyncluded clinical patient data in its 4D adaptive inverse plan-ing model and showed that the optimal dose distributionrom 4D inverse planning converges after 5 CT measure-ents in an offline adaptive process for prostate cancer ra-iotherapy. Figure 8 shows a typical dose distribution fromhe 4D inverse planning for a prostate cancer treatment,here the dose gradient and curvature in the adjacent regionetween target and normal structure were designed to bestompensate for the variations induced from treatment setupnd internal organ motion.

linical Applicationsgeneral clinical protocol of multiple CT-guided radiation

reatment provides a workflow of imaging, feedback, treat-ent evaluation, and adaptive planning modification. In thisrocess, schedules of imaging, correction, and planningodification should be initiated in a clinical protocol based

n treatment goals, as well as the nature of patient variationstationary or nonstationary). However, these schedules cane updated per image feedback-based treatment evaluation.o date, optimal scheduling has not been extensively studiedecause of the lack of clinical patient data. The only evidenceo far, which has been confirmed to be clinically beneficial, ishe image feedback schedule of 4 sequential CT scans inmage-guided adaptive radiotherapy of prostate cancer.16-19

here could be many possible choices of CT-guided manage-ent of interfractional patient variation for a given treatment

ite. However, an adaptive treatment process must includedaptive planning modification based on image feedback.hat is, to adapt is, by definition, to change course in the facef evolving information, and therefore a treatment plan in andaptive treatment process must respond to the treatmentmage feedback that is unavailable before the commencementf treatment.

ffline Adaptive Process forrostate Cancer Radiotherapyn offline adaptive treatment process has been initiated and

mplemented for external radiotherapy of prostate cancer inhe Radiation Oncology Department of William Beaumontospital since late 1997. In this process, patient treatmentas initiated with a conventional 4-field-box plan. Onlineortal imaging and off-board CT imaging were obtained forach of the first 4 treatment days. Portal images were ana-yzed to evaluate the patient-specific systematic and randomisplacements between treatment beam and patient bonytructure, meanwhile the targets manifested on the 4 dailyTs were delineated after patient bony registration to the

nitial planning CT to evaluate internal target motion/defor-

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178 D. Yan et al

ation. Correction of the systematic variations was per-ormed by modifying the planning beam isocenter and bydjusting the planning target position/shape. Therefore, theethod of systematic variation correction in this application

s beam aperture based. A new patient-specific planning tar-et was constructed, at the mean position with respect to theatient/organ position variation, by using the union of the 5TVs plus extra margin to compensate for patient-specific

andom setup error and the residuals. A new conformal planbefore 2003) or IMRT plan (after 2003) was then createdased on the new planning target and applied to the remain-

ng treatment. Technical details of this process, detailing im-ge evaluation, target construction, and planning modifica-ion, as well as clinical validation and evaluation, can beound in the published studies.16,17,34 To date, 657 patientsave been treated by using this process. Clinical outcomes onhronic toxicity have also been reported.18,19 In short, signif-cant dose escalation has been safely achieved with the high-st dose level 83.2 Gy to the treatment isocenter (79.2 Gy tohe minimum dose of the planning target volume). With theean follow-up of 2 years (the first 270 patients), there is no

ignificant difference of genitourinary or gastrointestinal tox-

Figure 8 Dose distribution (color) and profile along thebased on multiple image feedback (gray image). Target aand green curves, respectively.

cities between high-dose and low-dose groups. t

ffline Adaptive Processor Lung Cancer Radiotherapyn offline adaptive process for non–small-cell lung canceras been implemented in the Radiation Oncology Depart-ent of William Beaumont Hospital since early 2002. The

asic concept of this technique is to design the initial patientreatment plan at the mean target respiratory position anddapt the planning dose distribution to the respiratory targetotion measured during the pretreatment simulation. Ini-

ially, the active breathing control device35 was applied tobtain a CT image at the mean target position, and moreecently, a respiratory-correlated 4D CT image or 4D cone-eam CT image have been used. The planning target margin

s calculated by applying the indirect method based on theattern of target respiratory motion measured for each pa-ient at the treatment position, dose distribution, and pre-cription dose. In addition, the residuals in the evaluationnd correction of the mean target respiratory position andtandard deviation are also included in the margin design.atient daily treatment is verified using portal fluoroscopybtained from the onboard kV imager. The mean target posi-ion and the standard deviation of the respiratory motion were

line (black) designed by an adaptive inverse planningal wall occupancy distributions are indicated by the red

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Management of Interfractional Patient Variation 179

etermine if patient position correction and planning modifica-ion of beam aperture would be needed. The detailed adaptiverocess and potential reduction of the planning target marginave been discussed in our previous studies.20,21

ummarylarge generic planning target volume has been the typicaleans of addressing interpatient and interfraction variation

n radiotherapy, which has been one of the major limitingactors for treatment improvement. However, this detrimen-al effect can be significantly reduced by using image feed-ack management in the routine treatment process. The mostffective method in image feedback management of radio-herapy is the adaptive control methodology. Adaptive radio-herapy aims to customize each patient’s treatment plan toatient-specific variation by evaluating and characterizinghe systematic and random variations through image feed-ack and including them in the adaptive planning. As a tech-ological innovation, adaptive radiotherapy will become theew treatment standard, in which a predesigned adaptivereatment strategy will eventually replace the predesignedreatment plan in the routine clinical practice.

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