Utilizzononconvenzionaledell’Imaging

inRadioterapia

Chieti,24febbraio2017

Dr.LucaBoldrini

Conventional

Role ofimaging inRadiationOncology

DiagnosisStaging :locoregional,systemic

Characterization :Mpimaging,IB,hybrid approaches

Prognostic evaluationConventional RadiationOncology purposes

segmentation

planning

delivery,motion managementandadaptive approach

acutetoxicity

Follow up:response,relapseandlatetoxicity

Theragnostics

Theuseofdiagnostics

totailor therapeutic approaches

thus facilitating

personalized medicine

Bentzen S.- LancetOncology 2005

Theragnosticimaging paradigm

DiagnosisStaging

Target volumes &Planning

In room imaging (IGRT)Off-line / On-line

Theragnostic Imaging(biologically adapted

prescription)

Response evaluation

Tumor recurrence

Late toxicity

BEFORE AFTERDURING

Modified fromBentzen S.- LancetOncology 2005

Hightechnologyopportunities

Dosesculpting

Courtesy ofVerellen D.- 2013

2DPlanning 3 DPlanning IMRTPlanning

Hightechnologyopportunities

Towards small(anddifferently visible)targetvolumes

UNconventional

Main objective: higher dosedeliverytotargetsandtoxicityreduction withorgans at risk sparing through image

optimization

Metabolic andfunctional imaging:newtargets,dosepainting,newtoxicity paradigms

Adaptive therapy:intra- interfraction,movement

management,autosegmentation

MRIintreatmentroomrequiresafullyintegratedsolution:

1.MRI– LinacdesignedinUMC– Utrecht

8MVaccelerator,FFF

Modified1.5TPhilipsIngenia MRI

Linac mountedinringaroundMRI

Raaymakersetal.PhysMedBiol-2009 CourtesyofUulkevanderHeide

MRIintreatmentroomrequiresafullyintegratedsolution:

2.MRI– 60CoMRIdian®(ViewRay)0.35TMRIsplitmagnet

Realtimeimaging4framespersecond

360Coheads(15.000Cieach)onaringgantry

Boresize:69.3cm

Primarycollimatorsdirectlyunderthesources

MLC:30leaves

GRE:Gradient Echo - Protondensity,T1,T2- 2DGREis 25secperimage

TRUFI:TRUe FastImaging withsteadystatefreeprecession – T1,T2– 25sec3D planning/pilot,0.25sec

treatmentscan

TFL:TurboFlash– T1,mixT1/T2– 3min

EPI:Echo PlanarImaging – T2,mixT1/T2– 0.25secperframe

SE:SpinEcho

CourtesyofVIewRay:00016technicalmanualrevG

Localizationimaging

Imageregistration

Adaptivere-planning(ifneeded)

Treatmentexecution• MRin-linemonitoring

Doseaccumulation

1.5 T 0.35 T

• MRIforin-roomimagingopensanewerainradiationtreatmentworkflow

• Thisnewtechnologybringsmanyexpectationsandmultiplecriticalissues

• Needtomulti-centric cooperation,common lexicon forMRI-RT

• Possibilitytohaveanewtoolforprognosticevaluationduring the treatment execution

• Needtocreatea robustQA fordose accumulation algorithms• Evaluationofimpactofaccumulated doses onoutcome

prediction

Deliverable: MeasurementofOrganMotionCollaboration: Specificmetrics 3D/4D

«Features»toMRIdian

Calipso- VARIAN MRIdian - VIEWRAY

GAMMA.ADAPTIVE:Adaptive

GAMMA.RADIOMICS

Fenotipico

Biologico Genetico

Radiomica

Much morethan vessels andcells...

Hanahan D.andWeinbergRA.- Cell- 2011

Tumorheterogeneity

Vogelstein B.etal- Science- 2013

Tumorheterogeneity

Gerlinger M.etal- NEJM- 2012

Gerlinger M.etal- NEJM- 2012

Tumorheterogeneity

Tumor&treatments heterogeneity

Biomarkers

Noninvasive

Low-cost

Reliable

Easy

RADIOMICS

Tumorheterogeneity management

RadiomicsRadiomics istheprocessofextractionofquantitativefeaturesfromstandardradiologicalimagingforclinicaldecisionmakingtool.

TextureAnalysis,HistogramAnalysisandMorphometricAnalysisrepresentthethreemain

approachesfor featuresextraction.Dedicatedsoftwareneeded.

Lambin P.etal– Eur JCancer - 2012

Lambin P.etal– Eur JCancer - 2012

Radiomics:features extraction

Radiomics evaluation

• Not invasive• Repeatable• Analyzes entire tumor

volume

• Uses diagnostic exams

already available

• Cheap

Histological evaluation

• Invasive• Difficult torepeat• Tumour samplenot

always arerepresentative

ofthewhole volume

(tumorheterogeneity)

• Expensive

Radiomics analysis

Imagecollection Segmentation

Features extraction Analysisandmodeling

Radiomics:features extraction

Gemelli206ptsdatabasetimeframe2008-2014

Pre treatmentT2MRI,HRsequences noise filter

TRGAvailability

173ptsfinaldatabase

24ptsexclusion

9ptsexclusion

1.ROI extraction

2.Pre-processing:- LoG filter application

3.Dataanalysis (Moddicom):- Modelconstruction- Modelvalidation

KBORadiomics:rectal cancer experience

MRI ROIextraction Filterapplication

Kurtosis,Skewness,Entropy

KBORadiomics:rectal cancer experience

s = 0.49

s = 0.69

KBORadiomics:rectal cancer experience

Thefollowing variables were evaluated withmultivariatelogistic analysis for173rectalcancer patientscTcNGTVVolumeGTVSurfaceEquivalent SphereVolume/GTVSurfaceEntropy s=0.49Skewness s=0.69

Final model:

Coefficients:

EstimateStd.Error zvalue Pr(>|z|)

(Intercept) -5.14663.9229-1.3120.18954

cT -1.04420.3584-2.9130.00358**cN 0.53500.34121.5680.11689

Entropy Sigma 0.493.23541.64201.9700.04880*Skewness Sigma 0.69-3.14801.1601-2.7140.00666**---

Signif.codes:0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1

KBORadiomics:rectal cancer experience

FromRadiomics tonomograms

AUC=0.73

Internalvalidation

5000bootstrapresampling

TRIPOD1b

Specificity

Sensitivity

0.0

0.2

0.4

0.6

0.8

1.0

1.0 0.8 0.6 0.4 0.2 0.0

Externalvalidation

25casesMAASTRO

TRIPOD3

AUC=0.77

KBORadiomics:rectal cancer experience

Internalvalidation Externalvalidation

KBO MAASTRO173Patients 25Patients

47/173pCR (28%) 7/25(26%)

T2-w T2-w

Slicethickness3mm Slicethickness3mm

RMGESigna Exite @1.5T RMAchieva @1.5T

KBORadiomics:features extraction

Objectthat presents thesame weave ondifferent scalesscaleinvariance

Kochcurve

Ponteconietal,2016;Crossetal,1998;Waliszewski P,2016

Fractals

FractaldimensionTheparameter that characterizes afractal is thefractal dimension

Measure ofobject’s complexity

Low FD Pronged system

CompactsystemHighFD

Kochcurve

Penrose distribution

S ! = !#$ 1 < '( < 2

MandelbrotB.Thefractalgeometryofnature. 1982

KBORadiomics:features extraction

Personalization byRadiomics

KBORadiomics:features extraction

FromRadiomics tonomograms

?

ClinicalData ImagingData

Datasharing

Genetics

Lambin P. et al - Eur J Cancer - 2012Valentini V. et al - J Clin Oncol - 2011

Datafromdifferentsourcesandcontextscouldhighlyimproveourknowledge

Whatwewouldneed toshare

Whatwearewilling toshare

Whatweareabletoshare

Datasharing

WillemetalBMCPublicHealth,2014

Whichbarriers?Datasharing

- transparencyandcooperation- reproducibilityofresearch- cost-efficiency- preventing redundancies- accelerationofdiscoveryandinnovation- making moreefficientandeffective public health programs

BenefitsDatasharing

ImagingandInterventionalRadiologyforRadiationOncology

Editors:ReginaG.H.Beets-Tan,Wim Oyen,VincenzoValentini

PartI:ImaginginOncology:fromdiagnosistooutcomesPartII:Fromsimulationtodeliveryguidedbyimaging:

technicalaspectsPartIII:ImagingfortumorstagingandvolumedefinitionPartIV:ResponseevaluationandFollowupbyImaging

Lookingto(anear)future

• Newsegmentationandplanningtechniques(e.g.imagingbiomarkers)

• NewparadigmsofIGRTandadaptiverealtimeRT

• Newhybridtechniquesandmachines

• Newprognosticstratificationsystemsandclinicaldecisiontools

• Newradiomics perspectivesandclinicalintegration

• Coordinators

V.Valentini,A.Damiani

• PhDs

A.R.Alitto,S.Chiesa,G.Chiloiro,D.Cusumano

N.Dinapoli,A.Farchione,R.Gatta,V.Lanni

J.Lenkowicz,G.C.Mattiucci,C.Masciocchi,E.Meldolesi

• CollaborationwithMAASTROClinic

A.Dekker,J.VanSoest,P.Lambin

KBOAcknowledgments