0078936

Brain cancer treatment

picturing tomorrow’s treatment using yesterday’s images

SURFsara Super D eventDecember 15, 2015

Dr Philip De Witt Hamer

probability images of cancer treatment to unlock and release expertise

Frederik BarkhofJan de MunckMarnix Witte

Marcel van HerkUli MezgerLuc Dewit

Anne-Marie BruynzeelFrank Lagerwaard

Philip de Witt Hamer

institute QuantiVision

I n n o v a t i v e M e d i c a l D e v i c e s I n i t i a t i v e

the project

Outlinetoday’s treatment – the problem

our approach – probability maps

datamanagement & calculations – SURFsara

0126509

-1 +3 +35

-1 +99 +1471

2428901

+361

5.2 yr

high-gradeglioma

52yoM

low-gradeglioma

34yoM

1.1 yr

+1735

surgery radiation

The problem

improve tumor control by more aggressive therapy

preserve brain functionby less aggressive therapy

perfect cut / beam?

balance

Our solution: probability maps of brain cancer treatment

-unlock knowledge from medical professionals

-comprehensive accumulation of expertise

-transparent communication of care quality

terrain

streets

places

location is keytraffic & transport

Google maps

n=115De Witt Hamer et al, PLoSONE 2013

tumour probability map

LR

L Rn=131 n=103

resectionprobability

map

Research goals

Standard for best practice in Neurosurgery

tumor removal probability maps

postsurgery damage probability maps

Standard for best practice in Radiation Oncology

tumor progression probability maps

radiation toxicity probability maps

improve future image-guided surgery

improve future dose-painting radiation

3 workpackages

- Technical work plan to optimize and validate methods PhD physics

- Clinical work plan for neurosurgery applications PhD medicine

- Clinical work plan for radiation oncology applications PhD technical medicine

Martin Visser Roelant EijgelaarDomenique M+ller

deliverablesMRI library of glioma (n~2k)

probability mapping software

research communications

stakeholders2013

2013

20142011

patients with a gliomamedical professionalsmedical industryhealth care insurershealth care policy makersneuroscientists

TPM

RPM

residuecavity

tumour

standardbrainspace

collaborators

20 hospitalsLGG: ~ 500 ptsHGG: ~ 2000 pts

Methodological challenges

1. collect

2. segment

3. register

4. analyze

1-collect

1 patient outcome data – age, condition, survival 1 kb

1 patient imaging data – routine MRI, radiation plan 1 GB

1 patient postprocessing data – segmentations, registrations 500 MB

20 hospitals: ~2500 patients (cohort: 2012-2013) ~ 4 TB

Clinical Trial Processor

PSEUDONYMIZECOLLECT

sftp

@

working data storage

eCRF

dicom .dcm%.nii

.csvclinical&data

permanent data

storage

imaging&data

application contracts agreements minutes time plan

SV1 SV2 SV3 PhD1 PhD2 PhD3

home home home home home home

smartbrush elements

elastic fusion

.R%

.bashscripts

exchange datalab

protocols .txt metadata%data%doi%tar.gz

<run>

version change

website

REGISTER

ANTsR

ANALYZESEGMENT

local

WRITE

administration

grid

.nii%

.mpg%

.png

RESULTS

webapp

DataManagement Plan v1-1

the PICTURE project

2-segment

Old segmentation: 45 min per patient

New segmentation: 30 sec per patient with BrainLab server on surfsara

3-register

Advanced Normalisation Tools ANTs

symmetric diffeomorphic image registration with cross-correlation (25M DoF)

Brain registration: 1 hr - notebook: 4 core, 16 GB RAM5 min - surfsara: 16 core, 32 GB RAM

4-analyze

Example analysis

150 patients from two hospitals

1.2*106 voxels in MRI

2000 randomizations

Calculation: 52 hrs - notebook: 4 core, 16 GB RAM1 hr - surfsara: 16 core, 32 GB RAM

Initial resultstowards tomorrow’s treatment

E092

23.488 voxels 1x1x1 mm

resection probability

RPM observed

EoR in % 39% 43%

residue in mL

14 13

use case before surgery

Quality of surgical care: modern reporting

best practice standardn=94

patient cohortn=56

significant differences

Understanding the brain example: brain regions at risk for attention deficit

brain surgerywithout deficit

brain surgery with deficit differences p-values q-values

Gliomamaps

Thank youFrederik BarkhofJan de MunckMarnix WitteMarcel van HerkLuc DewitAnne-Marie BruynzeelFrank Lagerwaard

Roelant EijgelaarDomenique MullerMartin Visser

Uli MezgerBalint Varkuti

Linda Ackermans – MUMCHilko Ardon – St Elisabeth ZHSytske Boomstra – MSTWim Bouwknegt - SLZWimar van den Brink – Isala ZwolleClemens Dirven – Erasmus MCNiels van der Gaag – MCHBas Idema - MCAFred Kloet – MCHJan Koopmans - MZGMark ter Laan – UMCN/CWZPierre Robe – UMCUMarco Verstegen - LUMCMichiel Wagemakers – UMCG

Jan BotMathijs KattenbergLykle Voort

Stefan KleinRita Azevedo

Funding

p.dewitthamer@vumc.nl

the project