Goal of Radiation Therapy

Outcome-Driven Automated Treatment Planning Medical College of Wisconsin AAPM, July 28, 2008, MO-D-AUD A-3 BGRT Additional Biological Assays and I...
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Outcome-Driven Automated Treatment Planning

Medical College of Wisconsin

AAPM, July 28, 2008, MO-D-AUD A-3

BGRT

Additional Biological Assays and Imaging (during treatment)

Outcomes, Imaging, Assays (after (after treatment)

Refined biological parameters

Outcomes, Imaging, Assays (after (after treatment)

PopulationPopulation-based prescription (Phase I)

Stewart & Li, MP, 2007

TCP (1 – NTCP)

Normal Tissue Complication Probability (NTCP) Radiation Dose

Why use outcome models? Outcomes, Imaging, Assays (after (after treatment)

Analysis (assays, images, outcomes)

Biological Assays and Additional Imaging (before treatment)

Tumor Control Probability (TCP)

Individualized adaptive BGRT (Phase III)

Refined biological parameters

Boost dose to selected tumor regions (Phase II)

Probability of Outcome

X. Allen Li

Goal of Radiation Therapy

Cell culture and animal experiments

Refined biological parameters

New and refined biological models

• To fully describe responses as a function of any dose to any volume • To predict responses based historical data • To supplement or replace dose-volume criteria for plan optimization and evaluation.

Define tumor targets and organs at risk Anatomical and Biological Imaging (before treatment)

1

Model parameterization based on clinical data

Breast cancer

Outcome modeling for treatment planning • • • • •

Survival probability (LQ) TCP (Poisson model) NTCP (LKB, Serial, Parallel) EUD for both tumors and normal tissues Clinical Response Models (Maximum likelihood analysis)

Problems:

Three Clinical studies: •

Resch et al (2002): BCS then 48Gy + 20Gy LDR or 52 Gy + 9.7Gy HDR. Same TCP.



Fourquet et al (1995): (1995): RT alone 58Gy + 20Gy boost using 192Ir LDR or 60Co EBRT TCPIr=76% vs TCPCo=61%.



Mazeron et al (1991): (1991): RT alone 45Gy + 37Gy 192Ir LDR R=0.32R=0.32-0.49Gy/h TCP=60% R=0.5R=0.5-0.59Gy/h TCP=72% R=0.6TCP=84% R=0.6-0.9Gy/h

Still phenomenological rather than predictive Unreliable model parameters (QUANTEC mission)

Prostate cancer

α = 0.3 Gy -1 α / β = 10 Gy T rep = 1 hour

Guerrero & Li, PMB 3307,2003

Malignant gliomas • α = 0.15± ±0.05 Gy-1

MG

• α/β β = 3.1 ± 2.0 Gy • Clonogenic cell number: 106~107

= 0.06 ± 0.05 Gy-1 / = 10.0 ± 15.1 Gy

Grade 1&2

= 0.35 ± 0.07 Gy-1 / = 4.3 ± 5 Gy

Grade 3

= 0.11 ± 0.10 Gy-1 / = 5.8 ± 11.8 Gy

Grade 4

= 0.04 ± 0.06 Gy-1 / = 5.6 ± 9.4 Gy

Wang, Guerrero & Li, IJROBP 2003

Qi, Schultz, Li , IJROBP, 2006.

2

LKB NTCP: Lung

Liver Cancer = 0.029±0.004 Gy-1 / = 9.9±1.8 Gy Td = 100±18 days

Patient no.

Median Dose (Gy)

Fraction scheme (Gy/fx)

Reference

Liang

128

53.6

4.88

Cancer Vol103,218 (2005)

Dawson

128

61.5

1.5

J. Clin Onco Vol18, 2210 (2000) Int. J. Rad. Onco Biol. Phys. 55 329 (2003)

Reference Lung Burman et al. 1991 Martel et al. 1994 Kwa et al. 1998

n

m

TD50 (Gy)

0.87 0.87 1

0.18 0.18 0.30

24.5 28 30.5

Pneumonitis SWOG grade SWOG grade

Seppenwoolde et al. 2003 Moiseenko et al. 2003

0.99 1 1.02

0.37 0.28 0.26

30.8 43 21.0

SWOG grade 2 RP SWOG grade 3 RP Symptomatic pneumonitis

0.80

0.37

21.9

Radiographic and symptomatic pneumonitis

Observed RP Rate

0.7 0.6 0.5

55

1.8

Seong M

51

45

1.8

0.2

1.8

0.1

32.5

1 RP 2 RP

Graham et al. 1999 (Washington U) - RTOG grade >=2 Seppenwoolde et al. 2003 (Netherlands) - SWOG grade >=2 Moiseenko et al. 2003 (Canada) - RTOG grade >=2 Willner et al. 2003 (Germany) - NCI CTC grade >=2 Kim et al. 2005 (Korea) - RTOG grade >=3 Yorke et al. 2005 (MSKCC) - RTOG grade >=3 Chang et al. 2006 (U of Florida) - NCI CTC grade >=2 Maximum likelihood fit - all RP

0.8

83

24

Fractionation Scheme 1.8-2 Gy q.d. 1.8-2 Gy q.d. 1-2.7 Gy q.d.; normalized to 2 Gy/fr using / of 2.5 or 3 Gy 1-2.7 Gy q.d.; normalized to 2 Gy/fr using / of 2.5 or 3 Gy 1-2 Gy q.d.; normalized to 2 Gy/fr using / of 3 Gy

1 0.9

Seong H

Seong L

Endpoint

0.4 0.3

n=1 m = 0.39 TD50 = 28.6 Gy

0 0

Tai et al, IJROBP, 2008

Liver NTCP: BED=D*(1+d/ / +f*N) D: total dose, d: fraction dose, N: # of fractions

10

20

30

40

50

Mean Lung Dose (Gy)

Semenenko & Li 2007

Use of outcome models in computerized treatment planning • Plan evaluation • Plan optimization

Tai et al, 2008

3

Equivalent Uniform Dose

Problems to evaluate complex plans with DVH • • • • •

Complicated anatomy, multiple OARs Complicated/crossing DVHs Difficult for visual inspection Plan merit not quantified DVH failure for spatial tumor heterogeneity

EUD: the dose that, if distributed uniformly, will lead to the same biological effect as the actual non-uniform dose distribution. ………………..Niemierko. MP. 1997 S =

Vo

i

Vi S (Di ) V0

S = exp( − (α ⋅ EUD + β d ⋅ EUD − 1 . 4 γ Vi: a volume

element

Quantitative evaluation and comparison of complicated plans based on biological effectiveness are desirable.

Alternatively:

EUD =

EUD )) d

− ln( S )

α + β d − 1 .4 γ / d

EUD =

a i

vi D

1 a

,

i

EUD-based Figure-of-merit index (fEUD) fEUD

=

Plan Optimization

1 .0 n 1 .0 + k ⋅ i = 1 m j =1

ω ⋅ EUD i i

OAR



• • •

ω j ⋅ EUD j

Tumor

n, m : number of OARs and targets; ωi, ωj : weighting factors for each OAR and target; k : the relative importance factor between tumor and OAR.



Mathematical forms of treatment goals Increase if goal is not met Good if value less than or equal to 0

Physical (dose-based) cost functions • •

• • Condensing complex DVHs into one # (range: 0-1)

Cost Functions

Overdose/underdose volume constrains Maximum/minimum doses

Biological (dose-response model based) cost function. • •

Target/OAR EUDs TCP/NTCP.

• The larger fEUD, the superior the plan

4

Models used in Monaco

Two commercial biological TPS

Model Name / description

CMS Monaco Phillips Pinnacle

Tumor Poisson cell kill model

OAR

Parameters required

Comments

1. Cell sensitivity (0.1-1.0) 2. EUD prescription (Gy)

Mandatory cost function for targets; no penalty for hot spots

Serial 1. Power law exponent a ( 1) Penalizes for hot complication 2. EUD (Gy) spots model Parallel 1. Reference dose (Gy) Effective for complication 2. Power law exponent a ( 1) reducing mean model 3. Mean organ damage (%) organ dose

Monaco

Models used for optimization in Pinnacle Structure

Target

OAR

model

Parameters

Objectives/ constraints

Comments

Min EUD

Volume parameter (a

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