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
