new methods to ensure target coverage - aapm

7/14/2015

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New methods to ensure target

coverage 7:30: Jeffrey Siebers, Coverage evaluation and probabilistic treatment planning as a margin alternative

8:10 Jan Unkelbach, Stochastic programming methods for handling uncertainty and motion in IMRT planning

8:50 Huijun Xu, Coverage-based treatment planning to accommodate organ deformable motions and contouring uncertainties for prostate treatment

D e p a r t m e n t o f R a d i a t i o n O n c o l o g y

Objectives

• To understand – robust-planning as a clinical alternative to using margin-

based planning

– the conceptual differences between uncertainty and predictable motion

– the fundamental limitations of the PTV concept that probabilistic planning can overcome

– the major contributing factors to target and normal tissue coverage probability

– the similarities and differences of various robust planning techniques

– the benefits and limitations of robust planning techniques

Coverage evaluation and

probabilistic treatment planning as a

margin alternative

Marnix Witte

Jeffrey V Siebers

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Conflict of Interest

• JVS has received funding from

– Philips Medical Systems regarding treatment

planning

– Varian Medical Systems regarding dose delivery

verification

RT Planning Goal • Provide a tumorcidal dose while minimizing normal

tissue toxicity risk • Geometric uncertainties limit efficacy

– Image guidance reduces uncertainties • Daily on-line image alignment • Adaptive Radiation Therapy

– Planning strategies mitigate remaining errors • Margins • Probabilistic/robust planning

Effect of positioning uncertainty

• Daily random setup errors blur dose • Systematic shifts • Results

– Observed dose distribution in TPS not equal to dose received by patient

– Physician does not evaluate actual dose distribution or coverage

• Workaround – PTV coverage “representative” of CTV (Ideally)!

or or =

Planned Fractionation blurs

Systematic errors offset entire dose

distribution

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PTV - ICRU 83

Margins should ensure robustness

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• A robust design has negligible sensitivity to variations in uncontrollable factors G.Taguchi 1958

• Capable of achieving

– treatment prescription for the range of conditions that could occur

<robust treatment plan>

How do we evaluate RT Plan

robustness?

Delivery Simulations

• Plan patient

• Simulate (many) treatment courses including uncertainties

– Random positioning errors

– Systematic positioning errors

– Organ deformation

– ROI delineation

• Score dose / dose-response metrics of interest

Plan Pt

Simulate Tx

Course

Score DVH /

TCP / NTCP /

…

iSim ≤

nSim

Statistical

analysis

Y

N

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• Include slide from CERR uncertainty inclusion

‘PlanJury’ for plan evaluation

Tomas Janssen, Román Bohoslavsky

PTV

Rectum Wall

Viewing probabilistic DVH-like metrics

dose

volu

me

dose

volu

me

dose

volu

me

Different treatments DVHs Dose-volume coverage map (DVCM)

Percentile DVH for coverage q

For (d, v, q)

Prob[Dv≥d]=q

dose

volu

me

DVCM iso-probability

= Percentile DVH

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Observations from robustness analysis

of margin-based plans

• PTVs

– Margin-formula-based PTVs are robust

(designed for p=90%, actual p>90% )

– Coverage is dictated by the treated volume

• OARs

– PRV-less plans underestimate OAR dose

– NTCP estimates generally poor

Clincal observation

Plans are not perfectly conformal

CTV

Position

Plan CTV

Position

PTVmin dose

assumed by

margin formula

Treated Volume

TV

dictates CTV dose

Typically, the TV will not conform to the PTV

In some directions there is gap between the PTV and TV,

which gives the CTV more room to move than is

assumed by the margin formulas.

PTV

CTV-to-PTV

margin

ICRU volumes deficiencies

• GTV – Tumors are heterogeneous

(ICRU assumes homogeneous)

• CTV – Subclinical disease decreases with distance

(CTV margin assumes homogeneous)

• PTV – Small errors are more likely than large ones

(PTV margin assumes equi-probable locations)

• TV (Treated Volume) – Is not perfectly conformal to PTV

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Use of fixed volumes in RT

• Does not reflect the continuous nature of

dose distributions and flexibility of IMRT

– More like a surgical margin

• Inherited from pre-IMRT era

– Easy

– Available

– But likely not optimal

Probabilistic Treatment Planning

PTP • Incorporation of mathematical models of geometric

uncertainties into the plan optimization framework with goal of creating a plan that is “robust” or tolerant to such geometric uncertainties. – Planner does NOT need to specify a fixed margin, permits more

flexibility in tradeoff (overlap or near-overlap) regions

– Creates dose distribution which ensures coverage/sparing – an implicit margin is created

– Directly optimize for desired result • dose-volume coverage probability

• TCP/NTCP

Margin based planning

CTV PTV

Optimization

Objective functions MinDose MaxDose

DVH points EUD

Dose

distribution

90% prob. of

D ≥ 95% Dprescribed

in CTV

M = 2.5Σ+0.7σ

OAR

PRV

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Coverage optimized planning

CTV

Optimization

Probabilistic objectives Confidence levels on

MinDose, MinDVH, …

Dose

distribution

90% prob. of

D ≥ 95% Dprescribed

in CTV

OAR

Population

statistics

Σ, σ

No Margin!

Optimization Comparison

Clinical Treatment Planning

• Margins based on (Gaussian)

probability density functions S and s

S = preparation (systematic) error

s = execution (random) error

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• Current clinical IMRT

• M = 2.5S + 0.7s

PTV

0% 0

100%

Dp Dose

Dose

Dp

0

lung CTV lung

Planned

Prescribed

PTV

Dose Profile DVH of PTV

+7%

- 5%

PTV

0% 0

100%

Dp Dose

Dose

Dp

0

lung CTV lung

PTV


MinDose

cost

Planned

Prescribed

- 5%

+7%


• M = 2.5S + 0.7s

PTV

0% 0

100%

Dp Dose

Dose

Dp

0

lung CTV lung

PTV


Planned

Prescribed

- 5%

+7%

• DCTV>95% at P=90%


• M = 2.5S + 0.7s

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Probabilistic Treatment Planning

• Explicitly optimize using (Gaussian)

probability density functions S and s

CTV

0% 0

100%

Dp Dose

Dose

Dp

0

Dose Profile DVH of CTV

Static Planned

Prescribed

- 5%

+7%

• Probabilistic

PTV

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Static Planned

Prescribed

- 5%

+7%

• Probabilistic

lung CTV lung

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CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Static Planned

Prescribed

- 5%

+7%

Blurred

• Probabilistic

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Prescribed

- 5%

+7%

• Probabilistic

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Prescribed

- 5%

+7%

• Probabilistic

lung CTV lung

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CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Prescribed

- 5%

+7%

• Probabilistic

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Prescribed

- 5%

+7%

• Probabilistic

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0

Dose Profile pDVH of CTV

Prescribed

- 5%

+7%

• Probabilistic

Static representation

of probabilistic solution

• DCTV>95% at P=90%

lung CTV lung

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CTV

0% 0

100%

Dp Dose

Dose

Dp

0


Prescribed

- 5%

+7%

• Margin & Probabilistic

PTV

solution



• DCTV>95% at P=90%

lung CTV lung

CTV

0% 0

100%

Dp Dose

Dose

Dp

0

lung CTV lung


Prescribed

- 5%

+7%

• Margin & Probabilistic

PTV

solution



• DCTV>95% at P=90%

PTV

Moore, J. A. Gordon, J. J., Anscher, M. S., & Siebers, J. V. (2009). Comparisons of treatment optimization directly incorporating random patient

setup uncertainty with a margin-based approach. Medical Physics, 36(9), 3880–3890. http://doi.org/10.1118/1.3176940

Random setup errors only PTP

margin

dose static PTP random margin

dose w random

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Random only PTP findings

• + Physician preference • PTP: 21 / 28 plans

• Margin: 2 / 28 plans

• No preference: 1 / 28 plan

• Both auto generated plans bad: 4 / 28 plans

• = same CTV coverage

• = same TCP

• +,≈ NTCPPTP < NTCPmargin (small)

• + PTP reduces normal tissue integral dose

• - PTP increases complexity (MUs ↑1.6%)

• = Similar sensitivity to uncertainty in random error estimate – Better off over-estimating random error than underestimating

Moore, J. A. Gordon, J. J., Anscher, M. S., & Siebers, J. V. (2009). Comparisons of treatment optimization directly incorporating random patient

setup uncertainty with a margin-based approach. Medical Physics, 36(9), 3880–3890. http://doi.org/10.1118/1.3176940

PTP for systematic setup errors

• Physicians prefer:

– PTP: 21/29

– Margin: 2/29

– PTP or Margin: 4/29

– Neither: 2/29

• PTP coverage probability

– Target = same

– Rectum V75 ↓ 26%

– Rectum V70 ↓ 22%

– Rectum V65 ↓ 17%

Moore, J. A. Gordon, J. J., Anscher, M. S., Silva, J., & Siebers, J. V. (2012). Comparisons of treatment optimization directly incorporating

systematic patient setup uncertainty with a margin-based approach. Medical Physics, 39

Tradeoffs Optimized Margin

*Optimized Margin Coverage = PTP Coverage

Moore, J. A. , Ph.D. Dissertation, Virginia Commonwealth University

7/14/2015

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Planning tool

p

p

COP Setup Error Results

28 prostate patients

COP plan for CTV only

Dose-volume constraints for all OARs

Multiple different OAR criteria to observe

effect

Click Here For Demo

COP Seeks

Coverage

To maintain coverage, isodoses bulge away from constrained normal tissues

Gordon, J. J., & Siebers, J. V. (2009). Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion. Medical

Physics, 36(3), 961–973. http://doi.org/10.1118/1.3075772

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COP Seeks

Coverage




COP Seeks

Coverage




COP Seeks

Coverage




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COP Seeks

Coverage




NKI implementation

• Random errors

• Blurring of planned dose

• Voxel-specific kernel for

rotations/deformations

• Systematic errors

• ROI shift wrt dose

• Multiple ROI instances for

rotations/deformations

Margin based vs probabilistic

Cervix VMAT plan

PTV MinDose

Rotation point

90% conf CTV MinDose

Sagittal view

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Margin based vs probabilistic

Prostate VMAT plan

Rotation point

PTV MinDose 90% conf CTV MinDose

Sagittal view

Margin based vs COP

Liver SBRT

Erik Slooten

Probabilistic evaluation / optimization

• Setup uncertainties

• Organ motion

• Organ deformation

• Contouring uncertainties

• Biological-endpoint evaluation optimization

– Including bio-model uncertainties

• Dose painting

– Including sub-volume definition uncertainties

• …

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Target Distribution of TCP from simulations

• PTV represents CTV

Rectum Distribution of TCP from simulations

• Static evaluation – overestimates NTCP by >5%

for 6/18

– underestimates NTCP by >5% for 4/18

Probabilistic biological planning

CTV

Optimization

Probabilistic objectives

TCP, NTCP

Dose

distribution

Maximum TCP

for given

OAR NTCP

OAR

Population

statistics

Σ, σ

No Margin!

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Pinnacle ProbabilisticPainting Plugin

Georgy Shakirin, Matthieu Bal @Philips; Witte @NKI

Dose Level

SUV

66Gy

CTVmin

86Gy

CTVmax

Dose

Confidence Level (e.g. 0.90)

Probabilistic Dose Painting

Prostate VMAT plan

Rotation point

Tumor estimated from MRI 90% conf CTV DPBN

Sagittal view

Probabilistic biological painting NO_SUV

FDG-PET

GTV

CTV

TCP=45%

PET_SUV

GTV

CTV

TCP=64%

SUV α

α uniform

• Optimization on TCP, NTCP

– Non-uniform models: a(x), r(x), …

– Uncertainties on model parameters (sa)

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Robust lung optimization

Utilization of all 4DCT phases in plan optimization can

increase tumor dose and reduce normal tissue dose

compared with ITV method.

Multiple anatomy optimization (MAO).

MAO plan is delivered to moving anatomy

Multiple Anatomy Optimization (MAO)

Inverse planning of

dose-distributions to

treat 4D- anatomy

• Aperture does not need

to cover the entire ITV,

only needs to ensure

4D-dose is optimized

MAO aperture only needs to ensure dose to the

moving tumor, not entire ITV

MAO Flow

3-phase phantom: In phase 2, the moving target (green) is

aligned with the stationary OAR (red)

phase 1 phase 2 phase 3

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Uniform ITV coverage

results in dose delivery to

OAR

Non-uniform ITV dose results

in uniform target dose to

target and OAR sparing

MAO ITV

Single phase

dose

Accumulated

dose

Single phase

dose

Accumulated

dose

4D phantom solution

Robust multi-anatomy optimization (MAO)

for lung cancer

Simulation

Study

Patients,

Dose

calc.

motion

(cm)

ITV Gating Tracking

Trofimov

et al (2008)

1 lung,

10-phase

0.9

MAO>ITV MAO≈

Gating

MAO≈

Tracking

Zhang

et al

(2008)

4 lungs,

1-phase

0.9-2.9 -- -- -- -- MAO<

Tracking

Heath

et al (2009)

5 lungs,

3-phase

0.8–2.7 MAO>ITV -- -- -- --

Conclusions

• Probabilistic Treatment Planning offers an alternative to the use of margins

– Increased flexibility improves compromise between tumor and OAR dose

– Can recreate clinical plans when asked to

– Easy integration with dose painting & non-uniform Rx

– Framework for geometrical and biological uncertainties

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Why not in clinical use?

1. Clinical unawareness of coverage issues – Physicians believe of our plans

2. Requires change of thinking – Dose confidence level vs. DVHs

3. Requires vendor support – Vendor needs market

Acknowledgements

• Marnix Witte and NKI PTP Team

• My collaborators

– J.J. Gordon

– Joe Moore

– Huijun Xu

– …

new methods to ensure target coverage - aapm

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