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.