In-silico study to compare proton versus photon radiotherapy: a modelization based decision protocol ProtonShare Project

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1 In-silico study to compare proton versus photon radiotherapy: a modelization based decision protocol ProtonShare Project Abdulhamid Chaikh, Jacques Balosso Department of Radiation Oncology and Medical physics, University Hospital of Grenoble (CHU-GA). University Grenoble-Alpes, Grenoble. FranceHADRON, IPNL, Lyon. Pierre-Yves Bondiau Centre Antoine Lacassagne, Nice. France abdulhamedc@yahoo.com Achaikh@chu-grenoble.fr profile/abdulhamid_chaikh/ Proton Share

2 Outline: ProtonShare Project France HADRON: ProtonShare Project In-silico comparison of treatment plans : I. Dosimetric analysis: Dosimetric parameters DVH Quality Indices (QI) II. Radiobiological analysis: Radiotherapy outcomes TCP: Tumor Control Probability NTCP: Normal tissue complication probability Secondary cancer risks: OED III. QALY: Quality-Adjusted Life Year IV. Statistical analysis: Bootstrap simulation method Inter quartile method: elimination of aberrant data Preliminary results of child medulloblastoma : proton vs. photon Conclusion Comparison tools: 3 levels

3 France HADRON: Protonshare France HADRON: National Infrastructure for Biology and Heath To enable the consolidation of all medical, scientific and technical aspects of hadrontherapy in France : Universities CNRS (IN2P3), INSERM, IRSN, CEA. The scientific project : WP1: Clinical research (ProtonShare) WP2: Basic data for TPS WP3: Radiobiology WP4: R&D on instrumentation Contacts : Project leader (preferred contact): j-c.blouzard@ipnl.in2p3.fr Scientific & technical responsible : JBalosso@chu-grenoble.fr Steering committee president: montarou@clermont.in2p3.fr Management committee president: Ferrand.Regis@iuct-oncopole.fr

4 France HADRON: Protonshare This project federates 5 nodes and 29 research teams : Nice (CAL) Toulouse (IUCT Pericles) Lyon (FHARA) Caen (ARCHADE) Orsay (ICPO)

5 In-silico comparison of treatment plans Protonshare: select the better treatment plan Protontherapy: Favorable dose distribution Integrated in many centers, but No-benefit for all patient Proton Cost effectiveness evaluation Photon Survival Costs

6 In-silico comparison of radiotherapy outcomes ProtonShare: What should we know to select the better treatment plan?

7 In-silico comparison of radiotherapy outcomes ProtonShare: What should we know to select the better treatment plan? RT In-silico Clinical trials Photon Proton ProtonShare project Average data: Dose, TCP/NTCP Individualize patient data Treatment: photon or proton? Follow-up

8 In-silico comparison of treatment plans Radiotherapy outcomes: Decision making Main roles of algorithms in TPS: Prescribed dose Radiotherapy outcomes Dosimetric parameters

9 In-silico comparison of radiotherapy outcomes Radiotherapy outcomes: Decision making Standard indication for protontherapy No Yes Comparison tools: 3 levels 2 treatment plans proton vs. photon Dosimetric benefits Yes Radiobiologic: TCP/NTCP Yes No Is this cancer site analyzable? No Cost-effectiveness: QALY proton Yes Clinical benefits No photon

10 In-silico comparison of treatment plans I. DVH: Dosimetric parameters For each case, 2 treatment plans were generated: Plan 1: photon Plan 2: proton Dose Volume Histograms (DVHs) metrics: Target : Dmin, Dmean, Dmax, D95% and Quality Indices (QI) OARs: dose volume constraints 2D gamma (γ) maps and pixels-γ-histograms: Dose difference (% ) Distance-to-agreement (mm)

11 In-silico comparison of treatment plans I. DVH: Dosimetric parameters 2D gamma indices: a method to visualize the dose differences and uncertainty of the prediction of radiobiological models : Dose vs. TCP/ NTCP Gamma criteria 95% of pixels with γ < 1 : ( D/ DTA): 6%/6 mm DVH: ranging from 5 to 10% TCP : ranging from 10 to 20% NTCP : ranging from 20 to 30% pencil kernel vs. point kernel PTV: blue over estimation TCP OAR : red under estimation NTCP

12 In-silico comparison of treatment plans I. DVH: QI Quality factor (FQ): ( i 1 FQ [ exp N W i. X i ) ] Indices Ideally W i : values of weight factor and can be adjusted between zero to unity X i : indices value from plans CI = (Dmin-Dref)/Dp CI PTV = V95%/VT HI = (Dmax-Dmin)/Dp CI 1 CI PTV 1 HI 0 A Chaikh et al. The use of TCP based EUD to rank and compare radiotherapy plans: in-silico study to evaluate the correlation between TCP with DVH metrics and physical quality indices.

13 In-silico comparison of treatment plans II. Radiobiological analysis: Markov Model Markov Model: follow-up of the patient as MC simulation for particles: Translates health stages in terms of toxicity and probabilities Through the transition the model estimates the costs and effects of the treatment Probabilities Photons Choose RT Survive after RT Health outcomes & costs for endpoints NTCP, QALYs, Cost Death from cancer Protons vs. Photons Protons Survive after RT Health outcomes & costs for endpoints NTCP, QALYs, Cost Abdulhamid Chaikh et al. A decision protocol to propose proton versus photon radiotherapy: in silico comparison. Radiotherapy and oncology 2016; 119(1) : EP-2077.

14 In-silico comparison of treatment plans II. Radiobiological analysis: TCP/NTCP and OED Physics parameters Radiotherapy outcomes Dose distribution DVH EUD calculation TCP/NTCP: Sigmoid curves similar OED calculation Secondary cancer: Linear or Exp. curves

15 In-silico comparison of treatment plans II. Radiobiological analysis: TCP based EUD Equivalent Uniform Dose (EUD) model, Andrzej Niemierko 1993, Boston: 1 TCP TCD50 1 EUD a EUD v i. LQED i i / a {v i, Di } from DVH bins 3 parameters are needed: LQED a: tissue specific parameter describes the volume effect ( a = -10 ) for lung TCD 50 : the dose to control 50% of the tumors γ50: describes the slope of the dose-response curve i Di / n 1 / Di 2 1 /

16 Probability In-silico comparison of treatment plans II. Radiobiological analysis: NTCP based EUD Equivalent Uniform Dose (EUD) model, Andrzej Niemierko 1993, Boston: 1/ a 1 a NTCP EUD v i. LQED i TD50 i 1 EUD Di / n {v i, Di } from DVH bins 3 parameters are needed: a: tissue specific parameter describes the volume effect ( a = +1 ) for lung TD 50 : the tolerance dose for 50% complication rate γ50: describes the slope of the dose-response curve LQED i 1 / Di 2 1 / ΔP = γ. (ΔD/D) Chaikh A et al. 24/09/2016 Prescription dose (Gy)

17 In-silico comparison of treatment plans II. Radiobiological analysis: NTCP based LKB Lyman-Kutcher-Burman (LKB) model: NTCP t ( Deff Deff 1 2 TD t 50 2 x e 2 dx ) /( mtd. v i. LQED i i 3 parameters are needed: 50 ) TD 50 : the tolerance dose for 50% complication rate m: the slope of the sigmoid dose response curve n: volume effect large: n = 1 small: n = 0 1/ n n LQED i D i Di / n 1 / 2 1 / {v i, Di } from DVH bins

18 In-silico comparison of treatment plans II. Radiobiological analysis: NTCP based LKB Radiobiological parameter setting: α/β = Gy Daily fraction 2 Gy Endpoint as radiation pneumonitis Literature n m TD 50 (Gy) Estimated LKB Initial data Grade Patients Normalized total dose Patients EPL Patients CS Patients PBC Patients AAA Patients Chaikh A et al. 24/09/2016

19 In-silico comparison of treatment plans II. Radiobiological analysis: secondary cancer evaluation Estimation of secondary cancer incidence in radiotherapy New radiotherapy treatment and modalities Increased cancer cure are expected Benefit Risks IMRT: Substantial increase of beam-on time Proton: neutron production + evidence about RBE Increased number of secondary cancers?

20 In-silico comparison of treatment plans II. Radiobiological analysis: secondary cancer evaluation Organ equivalent dose (OED) concept proposed by Uwe Schneider ( Zurich, Switzerland): Inhomogenous dose distribution = uniformed dose distribution which causes the same radiation induced cancer incidence OED is similar to EUD for NTCP Schneider U, et al. Estimation of radiation induced cancer from 3D-dose distributions:concept of organ equivalent dose. IntJRadiat Oncol BiolPhys, 2005; 61: Dasu A et al. The use of risk estimation models for the inductionof secondary cancers following radiotherapy. Acta Oncol. 2005;44(4):39-47.

21 In-silico comparison of treatment plans II. Radiobiological analysis: secondary cancer evaluation OED models: using differential DVH, the OED can be calculated : Linear Linear exponential Plateau Competition : 1 OED N 1 OED N 1 OED N Effet = (α 1 D+ β 1 D²).exp (-α2d+ β2d²) N i 1 N i 1 N i 1 V. D i i i V. D e the first term represents the cell mutation probability the second term represents cell survival α1 can be derived from epidemiological studies of low dose irradiation i. Di (1 exp(. Di ) Vi. {v i, Di }: from DVH bins α: tissue specific parameter δ: organ-specific doseresponse parameter

22 In-silico comparison of treatment plans II. Radiobiological analysis: secondary cancer evaluation Excess Absolute Risk (EAR): EAR(D, agex, agea) = β.oed.μ (agex, agea) β: the slope of the dose-response curve at low dose µ(agex, agea) = exp[γe(agex - 30)+ γa ln( agea/70)] agex: age at exposure agea : age attained γe, γa : age modifying parameters Schneider et al. Theoretical Biology and Medical Modelling 2011, 8:27

23 In-silico comparison of treatment plans II. Radiobiological analysis: secondary cancer evaluation Application of cancer risk models to radiotherapy (photon vs. proton) EAR Photon /EAR Proton Same patient using 2 DVHs from photon and proton treatments : Same organ Same condition Photon : EAR organe =β.oed.μ (agex, agea) OED =D.e (-αd) OED = D.e (-αd) Proton : EAR organe =β.oed.μ (agex, agea) Ratio EAR EAR photon proton OED OED photon proton Chaikh A et al. 24/09/2016

24 In-silico comparison of treatment plans III. Quality Adjusted Life Years (QALY) Quality Adjusted Life Years (QALY) outcomes : Depending on the health state a weight (utility) is assigned to each period of life Weight can vary from 0 to 1: 0: death 1: perfect health Obtain total QALY per patient : One year in perfect health QALY = 1*1 One year with utility 0.5 QALY = 1*0.5 For 2 years : 1.5 QALYs

25 In-silico comparison of treatment plans III. Quality Adjusted Life Years (QALY) QALY: Questionnaire to measure utilities assigned to each period EQ-5D-3L: Level 1: indicating no problem Level 2: indicating some problems Level 3: indicating extreme problems EQ-5D-5L: Level 1: indicating no problem Level 2: indicating slight problems Level 3: indicating moderate problems Level 4: indicating severe problems Level 5:indicating extreme problems

26 In-silico comparison of treatment plans III. Quality Adjusted Life Years (QALY) EQ-5D-3L

27 In-silico comparison of treatment plans III. Quality Adjusted Life Years (QALY) Incremental cost-effectiveness ratios (ICER) ICER = Δ / ΔQALY ΔCosts + Protons is more expensive and less effective: Reject of treatment with proton Protons is more expensive and more effective: ICER evaluation Effectiveness vs. Costs + Effectiveness Protons is less expensive and less effective: ICER evaluation Protons is less expensive and more effective: Yes: treatment with proton

28 In-silico comparison of treatment plans I V. Statistical analysis: bootstrap simulation method Bootstrap simulation method: Estimate the number needed to observe a significant difference Generate big data Calculate p-value : Wilcoxon test P < 0.05 P > 0.05 Estimate the correlation : Spearman ρ < < ρ < 0.7 ρ > 0.7 Construction of CI 95% Bootstrap: P - value < 0.05 n = 8

29 In-silico comparison of treatment plans IV. Statistical analysis: Assessing the radiotherapy outcomes Data input (n): Dose volume histogram Initial clinical data TCP Radiotherapy outcomes NTCP EUD A.Chaikh, J.Balosso. The bootstrap method to improve statistical analysis of dosimetric data for radiotherapy ( submitted ) Bootstrap simulation Big data P-value Logistic regression Export output: Correlation coefficient New clinical data LKB EUD Graphical representation

30 Planning: ProtonShare Project FranceHADRON: ProtonShare Project In-silico comparison of treatment plans : I. Dosimetric analysis: Dosimetric parameters DVH Quality Indices (QI) II. Radiobiological analysis: Radiotherapy outcomes TCP: Tumor Control Probability NTCP: Normal tissue complication probability Secondary cancer risks III. Statistical analysis: Bootstrap simulation method Inter quartile method: elimination of aberrant data IV. QALY: Quality-Adjusted Life Year Preliminary results of medulloblastoma childhood : proton vs. photon Conclusion

31 Preliminary results of medulloblastoma childhood I. Dosimetric parameters Photon Proton

32 Preliminary results of medulloblastoma childhood I. Dosimetric parameters Optic nerve Brain stem PTV Crystalline R Eye Cochlea Chiasma Lung

33 Preliminary results of medulloblastoma childhood I. Dosimetric parameters Radiotherapy outcomes Proton < Photon Work in progress Secondary cancer QALY DVH TPS Dosimetric parameters: Photon vs. Proton Proton > Photon: Dmin, D95%, V95% Quality indices Dose Distribution Dosimetric parameters Proton > Photon QF Decrease dose to OARs TCP: Proton > Photon NTCP: Proton < Photon Outcomes: TCP NTCP

34 Preliminary results of medulloblastoma childhood II. Radiobiological analysis: TCP/NTCP Critical factors: Impact of dose calculation models The models use different approximations to compute dose distributions Photon plans: pencil kernel or point kernel? influence on EUD,TCP/NTCP and OED values Proton plans: pencil kernel model: wrong results over/under estimation point kernel model is recommended: as AAA, CCC, MC, Acuros XB integrate the contribution of neutron contamination A.Chaikh, J.Balosso. Statistic and dosimetric criteria s to assess the shift of the prescribed dose for lung radiotherapy plans when integrating point kernel models in medical physics: are we ready?. Transl Lung Cancer Res 2016

35 Preliminary results of medulloblastoma childhood II. Radiobiological analysis: TCP/NTCP Critical factors: : pencil kernel model or point kernel model? DVH metrics from same patient, but with 2 dose calculation models Pencil kernel model : PBC Point kernel model : AAA LKB model parameters proposed for PBC and AAA Is it necessary to obtain new parameters for more advanced dose calculation algorithms? A.Chaikh, J.Balosso. What should we know about photon dose calculation algorithms used for radiotherapy? Their impact on dose distribution and medical decisions based on TCP/NTCP: (submitted)

36 Preliminary results of medulloblastoma childhood II. Radiobiological analysis: TCP/NTCP Critical factors: Impact of radiobiological models DVH metrics from the same patient Lung pneumonitis as endpoint EUD : a = 1 ; γ50 = 2; TD 50 = Gy LKB : n = 1 ; m = 0.45 NTCP from LKB # NTCP from EUD Difference in absolute NTCP = 10% Should be aware of the sensitivity to the biological model parameters and should consider: Treatment plan evaluations Medical decision

37 Preliminary results of medulloblastoma childhood II. Radiobiological analysis: TCP/NTCP Critical factors: Impact of radiobiological parameter setting ( n, m, TD50) DVH metrics from same algorithm and same patient LKB model Impact on NTCP > 20%

38 OED and EAR Lung Preliminary results of medulloblastoma childhood II. Radiobiological analysis: OED Photons Protons

39 Preliminary results of medulloblastoma childhood IV. QALY: work in progress (1/5) QoL assesement EQ-5D-L The PedsQL brain tumor module Follow-Up: patients will be assessed systematically : 1. Before the irradiation 2. Over radiation treatment course: during the first and last 2 weeks of treatment 3. Annually thereafter for up to 5 years, more? Results : All of the data should be stored : DVH and DICOM images Cohort with follow-up data will be used for analysis and to improve the radiobiological model individualize patient treatment instead of average data

40 Calibration of a radiobiological model to assess the risk of toxicity Calculated QALY(Qc): relevant DVH metrics NTCP(Qc) NTCP (Qc) = NTCP *U{i=1 to m} where U varied from zero to 1, including age, the number of years of survival expected as a benefit of the radiotherapy cure, grade, etc. NTCP( Measured QALY (Qm): EQ-5D-L The PedsQL Bootstrap Preliminary results of medulloblastoma childhood Qc 1 ) 1 e Correlation coefficient IV. QALY: work in progress (2/5) s n 0 i 1 s. i xi Quant Imaging Med Surg 2016

41 Preliminary results of medulloblastoma childhood IV. QALY: work in progress (3/5) Calibration between measured and calculated NTCP A.Chaikh et al. The use of the equivalent uniform dose based radiobiological model to compare the delivered dose and radiotherapy outcomes from pencil beam and point kernel dose calculation algorithms, (submitted).

42 Preliminary results of medulloblastoma childhood IV. QALY: work in progress (4/5) Correlation QALY vs. NTCP from DVH If healthy: QALY 1 NTCP 0 : radiobiological parameter is good assumed If severe problems: 0 < QALY << 1 - higher NTCP: good calibration - lower estimated NTCP calibration is needed Follow-up (QALY) Treatment Healthy? Toxicity Year =1 Year =2 Year =3 Year =4 Year =5

43 Preliminary results of medulloblastoma childhood IV. QALY: work in progress (5/5) Correlation QALY vs. NTCP from DVH EUD model : a LKB model : n Example : lung Average data: a = 1 a = 1/n EUD = Deff ProtonShare Individualize patient: a = 1±δ

44 Conclusion Medical decision based on TCP/NTCP to rank radiotherapy plans The radiobiological models include several assumptions to simplify the biological processes : structure of tissues : serial or parallel the repopulation process The models can be used to: estimate TCP/NTCP: EUD, LKB, etc predict cancer induction: OER, ERR The use of published radiobiological parameters without local calibration: introduce a significant over/under estimated TCP/NTCP and OED Ideally, the set of radiobiological parameters should be based on the real clinical outcome statistics of the users cohorts of patients

45 Conclusion Medical decision based on TCP/NTCP to rank radiotherapy plans All uncertainties in the assessment should be taken into account to further improvement the estimate of TCP/NTCP and to determine the true dose-response relationship: Reconsidering how the biological effect is modeled is necessary Retrieving new parameters for existing models!! Algorithm dose models: accurately predict the dose-response relationship Incertitude of RBE : 1.1 or variable RBE Neutrons and whole body scattered dose Irradiation technique: 3DRT, IMRT, etc Energy: 6 MV vs. 18 MV: 6 MV is recommended for lung Recommendation : a local calibration of radiobiological parameters for each model is needed to better estimate the real TCP/NTCP and OED: Big data, yes but Real clinical outcomes should be obtained along the follow-up of the patients using QoL and QALY models Collaboration at national and international levels

46 Thank-you for your attention Proton Share