automated planning strategies at vumc wilko verbakel,...automated rapidarc •use knowledge of...
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Automated Planning Strategies at VUmc
Wilko Verbakel, Jim Tol, Alex Delaney, Max Dahele
VU university medical center
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Disclosures
• Vumc has a research collaboration with Varian
Medical Systems
• WV has received honoraria/travel fees from
Varian Medical Systems
• WV participates in the Varian RapidPlan council
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Automated planning strategies
• To overcome differences between planners
–Where to place the objectives?
–How to adapt objectives
• To increase efficiency
• To overcome differences between institutes
• To aim for “optimal” plans
–Highest OAR sparing
–Aiming for certain accepted PTV coverage
• To do an optimality check of new plans
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What is a good VMAT plan?
• Depends on institutional clinical protocol
– PTV minimum dose coverage
– RTOG: >95% should receive PD
– EORTC: >95% should receive 95% of PD
– PTV dose homogeneity (Dmax?)
4 H&N patients, RTOG, EORTC and Vumc plans
Tol, Dahele, Doornaert, Slotman, Verbakel, R&O 2014
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Influence planning protocols
Mean 10 pts Mean Dose (Gy)
Planning Protocol PTVB Composite Salivary
Structures Composite Swallowing
Structures
EORTC 71.4 ± 0.1 24.3 ± 8.2 22.9 ± 4.2
VUmc 71.7 ± 0.2 27.0 ± 9.2 25.7 ± 5.2
RTOG 72.6 ± 0.4 31.6 ± 10.3 29.5 ± 4.2
J Tol, R&O 2014
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VMAT for H&N: VUmc strategy
• In 2008 at Vumc: large variation between 8 planners
• Often replanning needed
Systematic evaluation of optimal optimization:
How to get most OAR sparing
Knowledge of VMAT optimizer
• Standardization of optimization
–Number of objectives and priorities
–How to deal with overlap
–How to deal with different PTV doses
–Interactively adapt objectives, keep distance to DVH
• Original time investment pays back in clinical cases
W. Verbakel, IJROBP 2009
P. Doornaert, IJROBP 2011;79:429 P. Doornaert, R&O 2011;6:74
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VMAT for H&N
• Interactive optimization (adapting objectives during
optimization based on DVH)
• This can be automated using an external program
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AIO: Automatic Interactive Optimizer
J. Tol, Radiation Oncology 2015
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AIO: Automatic Interactive Optimizer
AIO
- Replace manual interaction by
computer: Not subject to planner
- More frequent
adaptation
- More objectives
per OAR
– More consistent
sparing
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• AIO takes ~30 minutes optimization (plus 6 min
dose calculation)
AIO: Automatic Interactive Optimizer
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Automated RapidArc
• Use knowledge of interactivity
• Continuously adapt OAR objective positions
• Keep a distance with DVH
• Run a program on the screen
• Plan of early RA (2009, ) versus 2014 ()
PG
PG
J. Tol, Radiation
Oncology 2015
11
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• Results for
70 patients:
Plan Manual
(MP) AIO (APs)
PTVB V95% 99.1±0.3 99.3±0.4
V107% 1.7±2.9 1.3±1.8 NS
PTVE V95% 98.0±1.1 98.0±0.6 NS
V107% 12.3±7.2 11.0±4.8 NS
Contra. Parotid 19.0±6.3 18.1±6.3
Ipsi. Parotid 26.3±8.3 25.1±8.0
Contra. SMG‡ 32.5±7.9 31.7±8.8
Ipsi. SMG‡ 37.1±7.7 36.2±7.8 NS
Compsal** 24.2±6.5 23.2±6.3
Compswal** 29.5±7.2 25.5±7.1
J. Tol, Medical Physics, 2016 in press
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Example: clinical versus AIO
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• Clinical implementation for HNC in Feb 2014
• All HNC patients planned using AIO
• More efficient
• Less work for planners
• No need to wait during optimization
• Less need for multiple plans
• Some attempts to use for st III lung VMAT
Clinical use of AIO
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Commercial AP solutions
• Philips, Pinnacle: Autoplanning
–Multiple optimizations in parallel, increasing OAR
objectives
• Raysearch: multicriteria optimization
–Multiple optimizations, smart interpolation
–User can find optimum trade-off between OARS/PTV
• Varian, RapidPlan: knowledge based planning
–Library of good plans
–Relationship of geometry and achievable OAR dose
–Match new patient with model
–What about Pareto-optimality of library plans?
–Aim for “best” plans in library
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Patient
Geometry
RapidPlan (Varian Medical Systems)
Patient
Dosimetry
WashU: Appenzoller LM,
Med Phys 2012
Duke: Yuan L, Ge Y,
Med Phys 2012
Relationship of geometry and achievable OAR dose
16
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Patient 2 Patient 3
Patient 4 Patient 5
New Patient
RapidPlan
Model
Predict Dose/DVH
Automate
Planning
Patient 1
Geometry Dosimetry
Patient 6 Patient 7
Patient 8
PCA
RapidPlan
1
7
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Contralateral Parotid
Calculate Geometric PCA Score
PCA = 0.1
Obtain DVH PCA score
Principal Component Analysis (PCA)
Fogliata A, Radiother Oncol 2014
1
8
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RapidPlan: model from 60 plans • xx
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RapidPlan: PCM-sup model • xx
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Contralateral Parotid
Line Objective
Prediction Range: mean ± SD
DVH PCA Score: Generates range of expected DVH results
Line of optimization objectives along lower estimation boundary
Prediction range objective
2
1
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Prediction ranges
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Contralateral
Submandibular
Spinal Cord
Elective PTV
Boost PTV
Inferior
PCM
Cricoph
Upper esophageal sphincter
Lower Larynx
Parotids
Oral Cavity
Full optimization window
Combination of generated and fixed objectives
Automated optimization
Optimization Objectives
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Final optimized plan:
Predictions (dashed)
Achieved (solid)
Parotid G
Oral cavity
Inferior PCM
Cricopharynx
Optimization Objectives
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RapidPlan • xx
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RapidPlan • xx
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RapidPlan
clinical RapidPlan
• Composite swallowing 33 Gy 29 Gy
• Left parotid 19.4 Gy 18.4 Gy
• Right parotid 20.1 gy 18.9 Gy
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0
10
20
30
40
50
60
Mean
Do
se (
Gy)
Clinical
M60
OAR mean doses
15 evaluation patients Tol, Delaney, Dahele, Slotman,
Verbakel. IJROBP 2015
Model of 60 compared with clinical
28
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Contralateral Parotid
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
C P
aro
tid
Mean
Do
se (
Gy)
Patient Number
Series1
Series2
Series3
Series4
Cllinical
M30A
M30B
M60
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0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Mean
Do
se (
Gy)
Patient Number
Clinical
M30A
M30B
M60
Few Outlier Patients
1 2 3
Oral Cavity
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Group Volume (cm3) PTVB PTVE Oral Cavity
Model30A Mean 208.1 346.9 70.1
Range 39.1 – 607.0 223.5 - 514.2 14.7 - 186.6
Model30B Mean 150.4 390.3
Range 34.1 - 240.6 - 618.6 - 283.5
Model60 Mean 179.3 368.6 88.4
Range 34.1 - 607.0 223.5 - 618.6 14.7 - 283.5
Group # of Oral Cavities Model30A 27 Model30B Model60 51
Oral Cavity Mean Dose (Gy)
Patient PTVB (cm3)
PTVE (cm3)
Oral Cavity Size (cm3) M30A M30B M60
6 119.2 207.6 22.2 30.0 27.6
11 272.1 30.6 49.9 28.1
14 156.8 361.0 22.9 37.3 22.2
Number of Included OC’s
Included OC
geometries
3 patients higher OC dose:
High Oral Cavity Dose for M30B
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Clinical plan
Insufficient attention to sparing of swallowing muscles
Example DVH comparison
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-Cleaning of models
- Recommended by Varian
- Outlier metrics provided to aid in OAR removal
- Time consuming, for many OAR,
- contrary to aim of reducing time of planning
- Subjective
- Warranted?
Model Cleaning
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- Model M: Originally 113 Outliers, 46 Removed from model
- Model A: Originally 97 Outliers, 33 Removed from model
CA
SE
1
CA
SE
2
CA
SE
3
-Lies above Prediction
-Negative Outlier
-High Cooks Distance
-Lies below Prediction
-Positive Outlier
-High Studentized Residual
Plan DVH
Model Cleaning (OAR)
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What if we deliberately increase outliers?
Replace 5 – 40 plans by no sparing of PG
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Regression plots for C. Parotid
Original Model
Model with 5 outliers
Model with 10 outliers
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Prediction ranges for 3 Patients
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70
Vo
lum
e (
%)
Dose (Gy)
C. Parotid Patient 3
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70
Vo
lum
e (
%)
Dose (Gy)
C. Parotid Patient 5
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70
Vo
lum
e (
%)
Dose (Gy)
C. Parotid Patient 9
Cleaned model
Model with 5 outliers
Model with 10 outliers
Almost same lower range
A. Delaney, IJROBP 2016
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10 Patient Averages
5
7
9
11
13
15
17
19H
I %
Model C
Model CPS5
ModelCPS10
BOOST PTV ELECTIVE PTV
HI=100%*(D2%-D98%)/D50%
15
20
25
30
35
Oral Cavity Ipsi. Parotid Cont. Parotid Cont. Sub. Compsal Compswal
Mean
Do
se (
Gy)
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RapidPlan for plan QA
• 90-plan model
• Cleaned model
• Mid-prediction range
predictive for final
plan
J. Tol, Rad Onc 2015
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RapidPlan for plan QA
• Works well for mean
dose of OAR
• Prediction of entire
DVH: more
deviations at high
dose
J. Tol, Rad Onc 2015
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Conclusion
• Automated planning at VUmc: clinical since 2014
• RapidPlan is comparable or better than clinical plans
–HNC, now also working on lung models
• Expected to make RapidPlan clinical: summer 2016
• Outlier removal?
–Is very time consuming
–Model can look better (regressions)
–Does not necessarily give better plans from a model
• Knowledge Based Planning Future
–Need more knowledge on outcome. What do we need to
spare!
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Acknowledgement
• VUmc: Jim Tol, Alex Delaney, Max Dahele, Patricia
Doornaert, Ben Slotman
• Varian: Jarkko Peltola, Lauri Halko, RapiPlan team