This presentation explains the aim of the Knowledge Engineering division of MAASTRO Clinic. AAPM Conference, 2013

1. MAASTRO©2013
The fun(ction) of Medical Physics…
Erik Roelofs, MSc.
M e d i c a l P h ys i c ist ,
Info. / Prog. Manager
A A P M – 2 0 1 3 - 0 8 - 0 6
MAASTRO Knowledge Engineering

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MAASTRO
©2013
Overview
• Background
• What we want to do
• Where it became “Knowledge Engineering”
• Conclusion / tips

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MAASTRO
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Setting the stage (my background)
• Born 1971
• Education
– Bachelor Physics
– Master “Mechatronics” linkedin://roelofserik

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MAASTRO
©2013
Setting the stage (my background)
• Born 1971
• Education
– Bachelor Physics
– Master “Mechatronics”
• System engineer (3 yr)
– Integrator: cross-border, optics, electronics, mechanics
– Informatics: control systems architecture & design

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MAASTRO
©2013
Setting the stage (my background)
• Education
– Bachelor Physics
– Master “Mechatronics”
• System engineer (3 yr)
– Integrator: cross-border, opto-, electro-mechanics,
system control & architecture
• MAASTRO clinic (2003)
– Project leader (1 yr)
– Med.Phys. trainee (4yr)
– QMP & PhD candidate (5? yr)
– Information / program manager (1 yr)
• Substantial time for side-tracks
– some to optimize treatment quality, workflow, proceure, etc.
– others to start a new research line

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MAASTRO
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Netherlands

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MAASTRO
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Netherlands

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MAASTRO
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Southern Limburg

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MAASTRO
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MAASTRICHT

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MAASTRO
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Overview
• Background
• What we want to do
• Where it became “Knowledge Engineering”
• Conclusion / tips

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MAASTRO
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MAASTRO
house

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Life Sciences and Healthcare are converging
Predictive, Preventive, Personalized and Participatory Healthcare
HEALTHCARELIFE SCIENCES
“Trial and
Error”
Healthcare
“Evidence
Based”
Healthcare
“Precision”
Healthcare
Blockbusters
and mass-
production of
novel drugs
Targeted
Therapies
Increased regulation
and efficacy
standards
Analytics
LIFE SCIENCES HEALTHCARE
DNA chemistry
and advanced
technology
“Managed”
Healthcare
Paper based
Records
Electronic Data
Capture
Pharmacovigilance
and Risk Mgmt
Safety at
Point of Care
Electronic
Medical Records
Paper based
Systems
Personalized
Healthcare
Patient Care and Disease Mgmt
Translational Med
© 2010 Oracle and/or its affiliates. All rights reserved.

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MAASTRO
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The Problem is in the patient
• Remember the girl?
– Young smoker
– Numerous co-founding factors!
– It’s not just a tumor to treat…
– but a complex, cross-domain system!
• Need for individualized treatment

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MAASTRO
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European Journal of Cancer, Volume 48, Issue 4, March 2012
Towards individualized treatment

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MAASTRO
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European Journal of Cancer, Volume 48, Issue 4, March 2012
Entering the OMICS era…

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MAASTRO
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European Journal of Cancer, Volume 48, Issue 4, March 2012
Entering the OMICS era…

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The doctor is drowning
• Explosion of data
• Explosion of decisions
• Explosion of „evidence‟*
• 3 % in trials, bias
*2010: 1574 & 1354 articles on lung cancer & radiotherapy = 7.5 per day
Half-life of knowledge estimated at 7 years
J Clin Oncol 2010;28:4268
JMI 2012 Friedman, Rigby

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The problem: Individualized Medicine
It is unethical to ask
any person
(even a doctor)
to predict the best
treatment…
You fool!

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Prediction by MDs? Two year survival
• Non Small Cell Lung Cancer
• 2 year survival
• 30 patients
• 8 MDs
• Retrospective
• AUC: 0.57
Cary Oberije et al.

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MAASTRO
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Results: Models always significantly better then RO
Death at 2 years
Dyspnea
Dysphagia
RO’s
models

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MAASTRO
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Decision support in Radiation Oncology

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Rapid Learning
In [..] rapid-learning [..] data routinely
generated through patient care and
clinical research feed into an ever-
growing [..] set of coordinated
databases.
•Abernethy, J Clin Oncol 2010;28:4268
•[..] rapid learning [..] where we can
learn from each patient to guide
practice, is [..] crucial to guide
rational health policy and to contain
costs [..].
•Lancet Oncol 2011;12:933

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MAASTRO
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Computer Assisted Theragnostic (CAT): Predictive model
allowing treatment individualization - A holistic approach

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MAASTRO
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Treatment
administered
Real Outcome
(Complications, S
urvival)
Prospective gathering of
pre-treatment data (+CI)
Feed-back Loop
Computer Assisted Theragnostic (CAT): Predictive model
allowing treatment individualization - A holistic approach
- Survival: National Database (GBA)
- Complications: Module
EHR, (e)Questionnaire CTC like GP-
Patients-Long specialist
Biological
Data
Clinical Data
Image Data
Data-based &
Knowledge
based models:
Probability
of Survival &
Complications
(+ CI)
for treatment
x, y, z…

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The five components of Radiation Oncology
Clinic Biology
Physics
Molecular
Imaging
Computer
science
Radiation
Oncology

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MAASTRO
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Overview
• Background
• What we want to do
• Where it became “Knowledge Engineering”
• Conclusion / tips

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MAASTRO
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CAT ~ 2005 = MAASTRO Knowledge Engineering
Build Decision Support Systems to
individualize patient care
by using machine learning
to extract multifactorial
personalized prediction models
from existing databases
containing all data on all patients
that are validated in external datasets
Theme 2
Learning
(5%)
Theme 1
Data
(95%)

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Data warehousing
doi://10.1016/j.radonc.2012.09.019

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MAASTRO
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Centralized Data for Research
Hospital 1
Research System
data domains
clinical
imaging
biobanking
integrated
data
e.g.
tranSMART
e.g.
caTissue
NBIA
Open
Clinica
HIS
PACS
LIS
Hospital 2
HIS
PACS
LIS

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MAASTRO
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www.cancerdata.org
MAASTRO

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MAASTRO
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MISTIR framework : www.mistir.info
Multicenter
In Silico
Trials In
Radiotherapy
ROCOCO:
Photon, Proton, C-ion
Comparison project
Roelofs, et al. Radiother. Oncol. Dec 2010
Reporters
Principal
Investigators
Data centre
Participants
Secure
DB
TP
Initialisation
Collaboration
Protocol
MTA
Collaboration
Protocol
MTA
Reporting
Dummy
Run**
Preparation
Analysis
Institute n
Perform statistics
Biological modelling
Derive parameters
Generate DVH
Perform statistics
Biological modelling
Derive parameters
Generate DVH
Database (DB)
CT/PET Calibration
DICOM datasets*
Database (DB)
CT/PET Calibration
DICOM datasets*
Institute 1
QA
Limited nr. of slices
Contour names
Orientation, offsets
Grid spacing
Limited nr. of slices
Contour names
Orientation, offsets
Grid spacing
Roelofs, et al. J. Thorac. Onc., Jan 2012
Van der Laan, et al. Acta Oncol. Apr 2013

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MAASTRO
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ROCOCO network
Europe (13 partners):
•Aken
•Amsterdam (NKI)
•Vienna
•Darmstadt
•Gent
•Groningen
United States (3 partners):
•Boston
•Pennsylvania
•Madison Wisconstin
Japan:
•Chiba
•Hasselt
•Heidelberg
•Luik
•Lyon
•Maastricht (PI)
•Paris
•Villigen Switserland

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MAASTRO
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Problems, problems…
Barriers
Administrative
(time to capture,
time to curate)
Political
(value,
authorship)
Ethical
(privacy)
Technical
[..] the problem is not really
technical […]. Rather, the
problems are ethical,
political, and administrative.
Lancet Oncol 2011;12:933
Solutions: Distributed learning
from federated databases

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MAASTRO
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Data extraction system - Federated

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MAASTRO
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Federated Data for Research
Hospital 1
integrated
data
e.g.
euroCAT
HIS
PACS
LIS
Hospital 2
HIS
PACS
LIS
integrated
data
e.g.
euroCAT

36. Distributed
Learning
Architecture Update Model
Learn Model
from Local Data
Central Server
Model Server
RTOG
Send Model
Parameters
Final Model Created
Learn Model
from Local Data
Learn Model
from Local Data
Model Server
MAASTRO
Model Server
Roma
Send Model
Parameters
Send Model
Parameters
Send Average
Consensus
Model
Send Average
Consensus
Model
Send Average
Consensus
Model
Only aggregate data is exchanged between the Central Server and the local Servers

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The realiztion of the dream:
euroCAT (see www.eurocat.info), ameriCAT, duCAT
Active or funded CAT partners (10)
Prospective centers (4)
2
5

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Data>Model>Decision Support
1. Modeling
“Learn a model from data”
2. Validation
“Estimate model performance”
3. Decision Support
“Impact of the model on clinical practice”

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Dehing-Oberije, IJROBP 2009;74:355
Learn a model from data
•Training cohort
– 322 patients (MAASTRO)
•Clinical variables
•Support Vector Machines
•Nomogram

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Estimate model performance (survival)
• INDEPENDENT
Validation cohort
– 36 patients (Leuven)
– 65 patients (Ghent)
• Discrimination, Calibra
tion, Reclassification
• AUC 0.75
Dehing-Oberije (MAASTRO), IJROBP 2009;74:355

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Power of DSS (compare to TNM)
Stage IIIA 10 (14%)
Stage IIIB 13 (19%)
T4 12 (17%)

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Data>Model>Decision Support
•Prediction Models: Revolutionary in Principle, But Do They Do
More Good Than Harm?
•“we are drowning in prediction models [..] more than 100
prediction models on prostate cancer alone”
•“currently [..] a large number of models [..] are not
independently validated at all”
•J Clin Oncol 2011;29:2951

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MAASTRO
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Models built & validated : PredictCancer
• Lung cancer
– Survival
– Lung dyspnea
– Lung dysphagia
• Rectal cancer
– Tumor response
– Local recurrences
– Distant metastases
– Overall survival
• Larynx cancer
– Local recurrences
– Overall survival
www.predictcancer.org

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Cost-effectiveness at www.predictcancer.org
The increased effectiveness of
IMPT does not seem to outweigh
the higher costs for all head-and-
neck cancer patients.
However, when assuming equal
survival among both
modalities, there seems to be
value in identifying those patients
for whom IMPT is cost-effective.

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MAASTRO
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No
No
Proton therapy reimbursement decision tree for the Netherlands
Treatment with
PROTONS
Yes
Yes Isthisamodel
basedindication?
Create“stateoftheart”
PHOTONandPROTON
treatmentplans
No
NoYes Clinicallyrelevant
benefitexpected?
Comparebothplans
accordingtoprotocol
Treatment with
PHOTONS
Integratecomplication
probabilitymodelling
Yes Isthisdiseasea
standardindication?
Evidentdosimetric
benefitwithprotons?
PRODECIS : clinical grade decision support system
• Up-front
dosimetric and
complication rate
comparison
• Referrers needs
answers fast
• Reuse as much
information (data)
as possible

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MAASTRO
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PRODECIS : clinical grade decision support system

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MAASTRO
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Overview
• Background
• What we want to do
• Where it became “Knowledge Engineering”
• Conclusion / tips

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MAASTRO
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You!
Yes, you!
(Stand still laddie!)
Some word of advise from
my sponsor:
André Dekker

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MAASTRO
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How to start a successful research line
• Don‟t choose physics research,
choose medical physics research
– Take an engineering approach
– Choose translational research
• Pick a real world clinical problem
• Make friends
– Cross-border: companies, other researchers
• Make sure the problem has not been solved
• Make sure you have or can generate the data

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MAASTRO
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Pick a real world problem that your department has
• Pick a cancer
– Don’t think every cancer is the same
• Pick a subtype/subgroup in that cancer
– Don’t think every cancer is the same
• Pick a problem
– Ours: In inoperable stage I-IIIB Non-Small Cell
Lung Cancer 2 year overall survival is below 50%
• Make sure it is a problem for your department
• Make sure it is still a problem in 10 years (it will
take time to develop your expertise)

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Focus
• It will take year before you become an expert in a
certain problem
• Be ruthless in your focus, say no etc.

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Other
• Make sure your department is representative /
state-of-the-art in that problem
– Don’t try to do research to improve your process
• Publish or perish is still the norm
• Develop a steady stream of BSc, MSc, PhD
students and ultimately post-docs and first focus
on pumping out manuscripts

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MAASTRO
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Why choose a real problem?
• You can make friends with a radiation oncologist
• You now have sessions to go to at a conference
• You can do the research on-the-job
• Your department might actually use the results
• You can more easily identify experts
• Almost every grant mechanism requires you to
actually apply what you dreamt up
• Last but not least: Patients should benefit from it!

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MAASTRO
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DO NOT…
• choose a problem that is only interesting for
physicists
• fiddle around with the last % of dose distribution,
monte carlo models etc unless you can prove that
it has a clinical benefit
• try to do research in your spare time, it will not
work. Do it on-the-job or in protected research
time.
• try to do it alone

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MAASTRO
©2013
Thank you for your attention
Visit us at:
www.maastro.nl
www.eurocat.info
www.predictcancer.org
www.mistir.info
www.cancerdata.info