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AdvancedIMRTOp/miza/onStrategies
EmilieSoisson,Ph.D.ICTP2019
LearningObjec/ve
Tounderstandnewtoolstoimprovestandardiza/onandefficiencyinIMRTplanning
Acknowledgements
Iamnotauserofanyoftheproductsinthistalk.Iwouldliketothankthefollowingvendorsforsupplyingthisinforma/on.PhilipsRaySearchLaboratoriesVarianMedicalSystemBrainLab
WhyAutomatePlanning?
• Efficient• Standardiza/on• Consistentplanqualityandevalua/on
Automa/cSegmenta/on• ModelBasedSegmenta/on
(Pinnacle)– Triangularmeshadaptedto
pointsiden/fiedasboundariesbetweenoneorganandthenext
• Atlasbasedsegmenta/on(Brainlab,MIM,etc.)– Morestandarddeformable
registra/onusedtopropagatecontoursfromoneimagetothenext
• ManyTPShavetoolstoautosegmentpar/cularareas(ie.Bone,brain,lung,eyes,etc.)
www.usa.philips.com
Automa/cBeamDefini/on
• Useofscrip/ngtoautoma/callyposi/onandshapebeams
• TPSmayhaveanalgorithmtodeterminebeamangles
• Toolsgenerallyexisttoshapeblocks,jaws,etc.tothefield
www.usa.philips.com
Automa/cBeamPlacement
Automa/cBeamPlacement
FromPurdieetal.
Fromwww,raysearchlabs.com.
BreastPlanninginRaySta/on
BreastPlanninginRaysta/on
FromRaySerachLaboratorieswhitepaper.
BrainLabElements
• Mul/plebrainmetasteseplans• Automated:cri/calorgansegmenta/on(atlasbased),margins,prescrip/on,
isocenter,places2arcsperpredeterminedtableangle,arcstopandstopangles,MLCleafpaZern
• Eachleafpaircanonlyexposeonetargetata/me,collimatorminimizesinterleafleakage
• Weights,anglesandmarginadjusteda[erevalu/ngtargetconformityw/commonindices
Automa/cBrainMetastasesPlanningClinicalWhiltePaperavailablefromBrainLab
IMRTOp/miza/on
Wasnotsupposedtobesodifficult……
Automa/cIMRTOp/miza/on
• PinnacleAutoplanning• RaysearchMul/criteriaOp/miza/on• VarianKnowledgebasedplanning• Mostofthesesolu/onsaimtoavoidthetrialanderrorprocessofmanuallychangingobjec/vesandsearchingforthe“best”plan
PinnacleAutoplanning
• Mimicstheplanningprocessofanexperienceduser
• Createsresidualstructures(rings,etc.)• Adjustop/miza/ongoalsbasedonoverlap• Progressivetuning(basedonmatchtotrainingplans)
• Hotandcoldspotreduc/on
Progressiveop/miza/onalgorithmDrivestargetcoverageandsparingtothelimits
Deliveruniformdosetotarget
ImproveOARsparing
Dose(Gy)
Volume(cm
3 )
Ini$algoal
Auto-Planning
Ini$algoal
Auto-Planning
InputdatafromTreatmentTechniques
Outputclinicallyacceptableplan
Auto-Planningachievestheseresults……bymimickingtheexperiencedplanner
Op/mize
EvaluateFine
-tun
e
Mul8pleIMRT
op8miza8ons
AddTargetobjec/ves
AddOARobjec/ves
Addhot/coldspot
objec/ves
Finetuneeachobjec/ve
SlidecourtesyofFranciscoNunez,Philips
OAR2compromis
e
Auto-PlanningROIs
OAR1
PTV63
PTV70
Body
StandardROIs Auto-PlanningROIs
Nocompromise
SlidecourtesyofFranciscoNunez,Philips
Pinnacle³Auto-PlanningAccelera/ngIMRT&VMATplanning
• Reducesthetotal/merequiredtocreateanIMRTorSmartArcplanSimplified3-stepprocessreduces$me&efforttocreateaplan
• Replacesexhaus/vemanualdataentrytojustafewclicksTreatmentTechniquescreatedatsetupareusedrepeatedlyforeachplan
• EnhancesplanqualityandconsistencyTheAuto-PlanningEnginegenerateshighqualityplansatthe1stpass
• SimplifiesandstandardizestheplanapprovalprocessScorecardsreducetheneedformul$pleplanreviews
SlidecourtesyofFranciscoNunez,Philips
Mul/-CriteriaOp/miza/on
• Op/miza/ontechniquethattriestoallowplannerstomoreeffec/velyexploretrade-offsinIMRTplanning
• InIMRTyouneverknowyouhavethebestplanofallthepossiblesolu/onsanditwouldbeprohibi/vely/meconsumetoevaluateallpossibili/es
• Allowsforinterac/veexplora/onofthesolu/onspace
• CommercializedbyRaySta/on
Mul/-CriteriaOp/miza/on• MCOop/miza/onavoidexplicitweights• MCOiden/fies“paretoop/mal”planswithrespecttouser
specifiedobjec/ves• Paretoop/malplansarefeasiblewithrespecttoall
constraintsandnoobjec/vecanbeimprovedwithoutimpairingatleastoneother
• Aninfinitenumberofpossibleplansisrepresentedbyadiscretenumberofplansthatemphasizedifferentobjec/ves
• DoseineachstructureischaracterizedusingtheEUD(EquivalentUniformDose–Uniformdosethatleadstothesamebiologicaleffectasthenonuniformdoseintheorgan)
ParetoFron/er
FromWikipedia
ExampleofParetoFron/er.Theboxedpointsrepresentfeasiblechoicesandsmallervaluesarepreferredtolargervalues.PointCisnotonthefron/erbecauseitisdominatedbyAandB.AandBarenotdomiatedbyothersotheydolieonthefron/er.
FromRaySearchWhitePaper
AnchorPlans
FromRaySearchWhitePaper
MCOAlgorithm
• Op/miza/onsperformedusingbeamletintensi/es
• NplansgeneratedwhereN=numberofobjec/ves(anchorplans)
• N+1placesequalemphasisonallobjec/ve(balanceplan)
• BeyondN+1(auxiliaryplans)improvetheParetosurfacerepresenta/on– Generatedbygivingemphasistopairsofobjec/ves
Raysta/onInterface
Naviga/onAlgorithm
• UseslinearprogrammingtotranslateinputfromsliderbarsadjustbytheusertomovementalongtheParetosurface
• Algorithmlooksforthebestpointthatmeetstheuserspecifiedtradeoff
• Doseisupdatedinreal/mebyinterpola/onbetweentheParetoop/malplans
Dose“Mimicking”Algorithm
• Useofdirectmachineparameterop/miza/on(DMPO)reducestheerrorbetweenthenavigatedsolu/onandthedeliverableplan
• Thesolu/onspaceissearchedindosewhichminimizeserrorbetweentheop/mizedanddelivereddose
ComparisontoConven/onalOp/miza/on
IMAGE
Improved Planning Time and Plan Quality Through Multicriteria
Optimization for Intensity-Modulated Radiotherapy
International Journal of Radiation Oncology, Biology, Physics.
Craft, David L., Ph.D.; Hong, Theodore S., M.D.… Show all. Published December 31, 2011. Volume 82, Issue 1. Pagese83e90. © 2012.
Fig. 1
Block diagram for the two treatment planning workflows compared in this study. MCO = multicriteria optimization; DVH =dose–volume histogram; LAPC = locally advanced pancreatic cancer.
Copyright © 2017 Elsevier, Inc. All rights reserved.
PlanningTimeComparison
IMAGE
Improved Planning Time and Plan Quality Through Multicriteria
Optimization for Intensity-Modulated Radiotherapy
International Journal of Radiation Oncology, Biology, Physics.
Craft, David L., Ph.D.; Hong, Theodore S., M.D.… Show all. Published December 31, 2011. Volume 82, Issue 1. Pagese83e90. © 2012.
Fig. 2
Treatment planner time recorded for individual cases, for both standard planning procedure and the multicriteria optimization(MCO)based procedure. GBM = glioblastoma; LAPC = locally advanced pancreatic cancer.
Copyright © 2017 Elsevier, Inc. All rights reserved.
KnowledgeBasedPlanning• Aimisconsistencybetweenplans• Planningshouldbeefficientandproduceplansofhighquality
• Usessharedclinicalknowledgeandsuppliedtreatmentplanmodelsorcreatetheirown
• RPprovideses/matedDVHsasastar/ngpointforIMRT
• Doseandpa/entanatomyinforma/onfromexis/ngplansusedtoes/matedoseinnewpa/entbasedonpa/entanatomy
• MarketedfirstbyVarianMedicalSystems
DoseSpreadKnownFromPriorPlans
IMAGE
Performance of Knowledge-Based Radiation Therapy Planning forthe Glioblastoma Disease SiteInternational Journal of Radiation Oncology, Biology, Physics.
Chatterjee, Avishek, PhD; Serban, Monica, MSc… Show all. Published November 14, 2017. Volume 99, Issue 4. Pages10211028. © 2017.
Fig. 1
Regression (top left) and residual (top right) plots for brainstem. Points above the band in either plot tend to be negativedosimetric outliers, and points below the band are likely to be positive outliers. The concept of positive and negative outliersis also demonstrated (bottom right and bottom left, respectively). The blue bands correspond to the dosevolume histogram(DVH) estimate for a certain patient, and the solid blue lines correspond to the achieved DVH for the same patient. (A colorversion of this figure is available at www.redjournal.org (http://www.redjournal.org) .)
McGillStudySummary
• AknowledgebasedRTplanwascreated• 82GBMpa/entplanswereusedtotrainthemodel
• Modelwasvalidatedon45pa/ents• KBplanshadsuperiorPTVdosemetricsandbeZerop/capparatussparingthanmanualplans
• KBplanning/me7minsversus4hoursaverage/meformanualplanning
ComparisonKnowledge-based
• Dependentonaknowledgebase
• Notflexibletointer-physicianvariability
• Onlyasgoodastheknowledge
• Doesnotaddressnewknowledgeontoxicityendpoints
• Nodirectwaytomanagetradeoff
Mul/-criteriaop/miza/on
• Tradeoffseasilymanaged
• Mul/plesolu/onscanbecomparedveryquickly
• Requiresmostphysician/me
• DoesnotlendtoStandardiza/on
Autoplanning
• Mimicsac/onsoftrainedplanner
• S/llhardtodetermineplanquality
• Datarequiredformodelingfromins/tu/on
• Canbuildseparatemodelsforphysicianpreferences
RobustPlanning• Errorsoccurindelivery
duetopa/entposi/oningerrors,anatomicalchanges,etc.
• Robustplanningallowsforimproveddeliveryaccuracy(Robustness)forcertaindefinedweaknesses
• MorerelevantforprotontherapywherethePTVconceptbreaksdown
RobustOp/miza/onwhitepaperbyRaySearch
BiologicalOp/miza/on
• Feeddosevolumehistogramdataintobiologicalmodelsforplanevalua/onoftheimpactofthedosedistribu/ononbiology– NTC-NormalTissuecomplica/onprobability– TCP–Tumorcontrolprobability
Summary
• Thereareseveralautomatedplanningtoolsavailable.
• Thesetoolscanbeusedtoprovidestandardizedtreatmentplans
• Thesetoolswillmakeplanningmoreefficient.