Automated Planning Strategies at VUmc

Wilko Verbakel, Jim Tol, Alex Delaney, Max Dahele

VU university medical center

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

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

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

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

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

VMAT for H&N

• Interactive optimization (adapting objectives during

optimization based on DVH)

• This can be automated using an external program

AIO: Automatic Interactive Optimizer

J. Tol, Radiation Oncology 2015

AIO: Automatic Interactive Optimizer

AIO

- Replace manual interaction by

computer: Not subject to planner

- More frequent

adaptation

- More objectives

per OAR

– More consistent

sparing

• AIO takes ~30 minutes optimization (plus 6 min

dose calculation)

AIO: Automatic Interactive Optimizer

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

• 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

Example: clinical versus AIO

• 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

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

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

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

Contralateral Parotid

Calculate Geometric PCA Score

PCA = 0.1

Obtain DVH PCA score

Principal Component Analysis (PCA)

Fogliata A, Radiother Oncol 2014

1

8

RapidPlan: model from 60 plans • xx

RapidPlan: PCM-sup model • xx

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

Prediction ranges

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

Final optimized plan:

Predictions (dashed)

Achieved (solid)

Parotid G

Oral cavity

Inferior PCM

Cricopharynx

Optimization Objectives

RapidPlan • xx

RapidPlan • xx

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

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

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

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

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

Clinical plan

Insufficient attention to sparing of swallowing muscles

Example DVH comparison

-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

- 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)

What if we deliberately increase outliers?

Replace 5 – 40 plans by no sparing of PG

Regression plots for C. Parotid

Original Model

Model with 5 outliers

Model with 10 outliers

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

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)

RapidPlan for plan QA

• 90-plan model

• Cleaned model

• Mid-prediction range

predictive for final

plan

J. Tol, Rad Onc 2015

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

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!

Acknowledgement

• VUmc: Jim Tol, Alex Delaney, Max Dahele, Patricia

Doornaert, Ben Slotman

• Varian: Jarkko Peltola, Lauri Halko, RapiPlan team