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1 Grupo Física Médica de Sevilla (CTS-233) Antonio Leal Plaza Dept. Fisiología Médica y Biofísica, Fac. Medicina. Univ. Sevilla
2 Overview Introduction to dose painting Dose calculation proposal Optimization approaches Some results
3 Classic role of the image in RT planning Gross Tumor Volume (GTV) Clinical Target Volume (CTV) Planning Target Volume (PTV) GTV<= CTV <= PTV
4 Classic role of the image in RT planning
5 Classic role of the image in RT planning
6 Classic role of the image in RT planning
7 Classic role of the image in RT planning
8 Classic role of the image in RT planning
9 Classic role of the image in RT planning
10 Classic role of the image in RT planning
11 Classic role of the image in RT planning
12 Classic role of the image in RT planning
13 dosis LUZ SAINT SAUVEUR SEPTEMBER 2016 RADIOTHERAPY MODELLING Classic role of the image in RT planning campo 3x3 fotones 6mv inplane Medidas con microcamara Calculos Helax mm
14 Classic role of the image in RT planning We were happy with this model but, in the same hospital, following the same protocols, ones patients survaive and others not...
15 Novel role of the image in RT planning Physiological information about the tumor before, along the treatment and for follow Well-established Under research PET; SPECT; fmri; Dynamic contrast-enhanced CT and MRI; Diffusion Weighted MRI
16 Novel role of the image in RT planning The new scenario for RT planning Gross Tumor Volume (GTV) Clinical Target Volume (CTV) Planning Target Volume (PTV) GTV<= CTV <= PTV Biological Target Volume (BTV) New protocol is still to be established under a normalized calibration of scanners for the prescription dose assigning
17 Novel role of the image in RT planning The new scenario for RT planning: Which BTV? FDG FLT CuATSM Jeraj et al, 2010
18 Novel role of the image in RT planning The new scenario for RT planning: Adaptive radiotherapy CT MRI (T2) FDG-PET PRE-R/ (Week 2) WEEK 3 (Week 4) WEEK 5
19 *Ling et al. Red Journal, 2000 LUZ SAINT SAUVEUR SEPTEMBER 2016 RADIOTHERAPY MODELLING Dose painting in RT planning While several issues are resolved, dose painting* is being applied in clinical studies Two approaches for planning dose painting: Dose painting by contour (DPBC) Dose painting by Number (DPBN)
20 Dose painting in RT planning New scenario for planning RT: Conventional dose distribution Dose painting Homogeneus dose to volume Few targets Conventional prescription Fluence optimization Heterogeneous dose to volume Multiple targets Dose escalation Biological optimization
21 New scenario for planning RT New tools should be incorporated to TPS - Dose calculation: Dose painting in RT planning (Our proposal) Higher dose gradients within the target require the most accurate dose calculation engine: Monte Carlo Treatment Planning (MCTP) - Optimization procedure: A more important role for image into the algorithms BIOMAP New restrictions at the voxel level Linear Programming (LP)
22 Overview Introduction to dose painting Dose calculation proposal Optimization approaches Some results
23 Monte Carlo Treatment Planning (MCTP) for dose painting EGSnrc/DOSXYZnrc/BEAMDOSE. Full Monte Carlo (fmc) Linac head: BEAMnrc (Rogers et al., 2011; Walters et al., 2009) CT patient: BEAMDOSE (Salguero Castaño, 2008)
24 Monte Carlo Treatment Planning (MCTP) for dose painting Modelling Explicit transport
25 Monte Carlo Treatment Planning (MCTP) for dose painting
26 Monte Carlo Treatment Planning (MCTP) for dose painting Squema for MC treatment planning Initial PSD1 correspondig to non-specific patient geometry PSD2 under the last beam modifier as source (beamlet) BEAMDOSE for dose calculation (dose deposition coefficient of beamlet for each voxel) PSD1 PSD2
27 SL-18;Synergy; Axesse LUZ SAINT SAUVEUR SEPTEMBER 2016 RADIOTHERAPY MODELLING Primus; Oncor Varian 2100 DC Monte Carlo Treatment Planning (MCTP) for dose painting Linacs simulated by the Medical Physics Sevilla group Elekta Siemens Varian Radiosurgery VMAT IMRT Step & Shoot IMRT Dynamic
28 Overview Introduction to dose painting Dose calculation proposal Optimization approaches Some results
29 Optimization algorithm for dose painting Two approaches: two planning modalities Inverse planning -- Small gometries to reach so heterogeneous prescription - How the restrictions are imposed to voxels?... Forward planning DPBN - Volumes could be shaped by larger segments - How to implement an automatic process? - How consider morpho and functional entities in the segmentation process?... DPBC
30 Optimization algorithm for dose painting Inverse planning for DPBN (MCTP) j PSD divided in beamlets (IAEA format) i Patient image divided in voxels
31 Optimization algorithm for dose painting Inverse planning for DPBN (MCTP) Beamlet weighting Quadratic method Objective function: + Dose-volume restrictions. Wu, Q., & Mohan, R. (2000).
32 Optimization algorithm for dose painting Inverse planning for DPBN (MCTP) Linear programming (LP)
33 Optimization algorithm for dose painting Inverse planning for DPBN (MCTP) Linear programming (LP)
34 (DAO) Optimization algorithm for dose painting Forward planning for DPBC (MCTP) Anatomy-based algorithms What about functional data?...
35 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) Combination of matrices corresponding to multiple information Biophysical Map Sequencing
36 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm A k l, B k m, C k r, E k l,r M MxN k = 1,2,, Number of incidences l = 0,1,2,, Number of targets m = 1,2,, Number of OARs r = 0,1,2,, Number of prescriptions from PET
37 For each l, r y k, LUZ SAINT SAUVEUR SEPTEMBER 2016 RADIOTHERAPY MODELLING Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm H k l,r 1 E l,r ij e μ k ij, H k l,r M MxN For each l, r y k, X l,r k A l k, B 1 k,, B m r k, C k I M NxN, α l k + γ r k = 1,, α l k, γ r k 0,1 β m k 1,0 α k l I β k 1 I β k m I γ k r I X k l,r M MxN T k l,r ij l,r X k X l,r k, 0 ij ij l,r 0, X < 0,, T l,r k M MxN k ij D k l,r T k l,r H k l,r,, D k l,r M MxN
38 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm BM k l,r A k l, C k r, D k l,r λ k l I ω k r I, BM k l,r M MxN τ k l,r I λ k l + ω k r + τ k l,r = 1 λ k l, ω k r, τ k l,r 0,1 A. Ureba et al., Medical Physics 41, (2014).
39 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm In-house sequencer Salguero et al. Radiother & Oncol. Vol. 93, pp: , 2009.
40 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm Overlapping beetween segments form different incidences
41 Optimization algorithm for dose painting Forward planning for DPBC (MCTP) BIOMAP algorithm Boolean algebra for weighting each matrix Target OAR m = 1,, M M m=1 OAR m k = 1,, K Contorno l=1,,l Valores PET r=1,,r Contorno l=1,,l Valores PET r=1,,r Contorno l=1,,l Valores PET r=1,,r (α k l β k γ k r ) (λ k l ω k l τ k l,r ) (0,, 1 (m,, 0) (0,, 1 (m,, 0)
42 Optimization algorithm for dose painting Improved efficiency for MCTP i j BIOMAP Reduction of beamlets by weighting segments LP Few voxel restrictions can be enough for big volumes
43 Clinical application of MCTP for dose painting Monte Carlo Treatment Planning System (CARMEN) CARMEN
44 Clinical application of CARMEN for dose painting Optimization Workflow Aperture Algorithm Aperture Algorithm BIOMAP Inverse Biophyscial Maps Fluence Maps Penalty Factors IMAGING
45 Clinical application of CARMEN for dose painting CARMEN Platform based on Matlab (CUDA-enabled GPUs)
46 Clinical application of CARMEN for dose painting HP ProLiant DL585 G7 Performance 4 procesadores Opteron GHz (80Wx4) de 12 kernels 48 processors 2.2Ghz 64GB RAM 2 Hard disks 500GB 4 ports 1Gbit
47 Overview Introduction to dose painting Dose calculation proposal Optimization approaches Some results
48 Clinical application of CARMEN for dose painting Head and Neck case for escalation dose to hypoxic region (HR) CARMEN for DPBC BIOMAP algorithm Prescription doses, restrictions HR (PTV82); D % D p (82 Gy) PTV72; D % D p (72 Gy) Courtesy from Karolinska Institute PTV60; D 95 D p (60 Gy) Bone marrow; V 38Gy (63.3% D p ) 0% Parotid gland left.; V 38Gy (63.3% D p ) 5% Parotid gland right.; V 38Gy (63.3% D p ) 5% Jaw; V 30Gy (50% D p ) 1% D y delivered dose to y% of volume, V xgy the volume receiving more than x Gy. D p prescription dose (60 Gy).
49 Clinical application of CARMEN for dose painting Head and Neck case for escalation dose to hypoxic region (HR) CARMEN for DPBC BIOMAP algorithm
50 Clinical application of CARMEN for dose painting Head and Neck case for escalation dose to hypoxic region (HR) BIOMAP 3D for DPBC Processing time Apertures generation (min) (1ª/2ªopt) Apertures simulation (PSDs) (min) Dose calculation (min) Optim. (min) Total time (min) BIOMAP-3D Inverse 5/30 0/80 25/40 180/ BIOMAP provide us less apertures for MC simulation keeping the statistic uncertainty
51 Clinical application of MCTP for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN - CARMEN TPS
52 Clinical application of MCTP for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN - CARMEN TPS
53 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level Foster s algorithms based on Affine Propagation (AP) for clustering (7 levels of clustering)
54 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level Assignation of prescription dose
55 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN - CARMEN TPS
56 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN - CARMEN TPS Optimization LP (beamlet dose)
57 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level Optimized fluence map Sequenced map
58 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level Thick lines: Beamlets dose; Thin lines: Apertures dose
59 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level 90.47% Q=1 ± 0.05 Solid lines: Beamlets QVH; Dash lines: Apertures QVH
60 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN7 DPBN5 DPBN3 DPBNA DPBN_final
61 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level N. of SEGMENTS MONITOR UNITS QVH BEAMLETS QVH SEGMENTS DPBN % 80.37% DPBN % 87.15% DPBN % 90.47%
62 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level 95.64% Q=1 ± 0.05
63 Clinical application of CARMEN for dose painting NSCLC case for escalation dose Linear programming for DPBN Prescription dose at voxel level DPBN native N. of SEGMENTS MONITOR UNITS QVH SEGMENTS %
64 Clinical application of CARMEN for dose painting Work in progress Generate a collaboration environment for the extended use of CARMEN (included updates and new solutions) New radiotracers for evaluation Connect the system to other machines (Tomo, Ciberknyfe, ) Include experimental verification (*QuAArC system) *AR. Pereira et al., PLOS ONE Under revision
65 Merci beaucoup Javier Salguero Rita Pereira José Antonio Baeza Elisa Jiménez Ana Ureba Antonio Leal Bianey Palma
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2007 Institute of Physics Publishing. Reprinted with permission.
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