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PhD proposal

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Richard Rios Patiño

on 12 December 2012

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Transcript of PhD proposal

ADAPTIVE RADIOTHERAY IN PROSTATE CANCER: THROUGH AN
OPTIMAL AND INTEGRAL FORMULATION by
Richard Rios Patiño

Advisors: Jairo J. Espinosa Oviedo
Oscar Ocosta 1. INTRODUCTION 1 2 Figure. Image created by US government agency National Cancer Institute. The prostate cancer is the most widespread cancer in the men population in several countries. 3 Types of treatments:
The surgery.
The chemotherapy.
The external beam radiotherapy.
The brachytherapy. If the external beam radiation therapy is chosen as treatment, then the initial steps are:
To obtain the computed tomography (CT) image.
To segment the pelvic organs.
To define the planning treatment. For example, for a given patient, 70 Gy can be delivered in 35 sessions of 2 Gy. Figure. Image created by Department of Radiation Oncology from USA. Maximum tumor control
(High TCP) Minimum side effects in Normal Tissue
(Low NTCP) 4 The aim of RadioTherapy Uncertainties.
Errors in assessing the extent of macroscopic tumor spread.
Errors in delivered doses.
Error in determining the location and motion of organs. Clinical Target Volume (CTV). The region that encompasses the gross tumor volume and the area at risk of microscopic spread ([8]).
The Planning Target Volume (PTV). The volume that results of introducing an error margin in order to
guarantee the irradiated volume under uncertainties. Basic concepts of RadioTherapy There are two models used for assessing the success of the RT treatment:
The tumor control probability (TCP).
The normal-tissue complication probability (NTCP). Intensity modulated radiation therapy (IMRT). It is a mode of high-precision radiotherapy that uses computer-controlled linear accelerators that adjust the radiation dose to the target volumes shape. Image Guided RadioTherapy (IGRT). Frequent imaging in the treatment room that allows to guide the treatment process lying on information obtained from these images. 5 6 2. STATE OF ART 7 8 Adaptive Radiotherapy 20 Final comments 3. RESEARCH PROBLEM 21 Conventional radiotherapy techniques cannot properly manage intra-fraction variations of the patient due to the following difficulties:
The intra-fraction motion and deformation of OaR is not regular and rhythmic.
The inter-fraction geometrical variations are multidimensional due to organs can move and deform to each other in a complex manner .
The information gathered with IGRT depends on the accuracy and quality of the image registration.
The intra-fraction variations of OaR lead to errors in the voxel-specific delivered dose.
The steep dose gradients around the target. Therefore, the research problem will be tackled in developing an adaptive radiotherapy scheme for overcoming uncertainties and improving the efficiency and accuracy of the radiotherapy treatment. The research problem is focused:
To develop a dose compensation technique, which based in optimization tools, determines treatment modification decisions by integrating patient-specific treatment variations.
To obtain a quantitative characterization of inter-fraction variation of OaR.
To develop an objective function that involves efficiency (cumulative dose), effectiveness (TCP/NTCP models) and workload. Figure. Image obtained from LTSI. Conventional Radiotherapy Technique Adaptive Radiotherapy Technique Figure. Image obtained from LTSI. Figure. Image obtained from LTSI. Figure. Image obtained from LTSI. Decrease error between
planned dose and cumulative dose Low NTCP and high TCP 4. PROJECT FORMULATION Research Hypothesis Objectives METHODOLOGY To develop an adaptive radiotherapy technique that overcomes uncertainties and perturbations by integrating feedback information from the patient-treatment variations, a dynamic model of intra-fraction motion and deformation of OaR and treatment constraints. General Objective Specific objective To propose an optimization scheme that obtains optimal planning dose by integrating treatment constraint like fraction size and toxicity events.
To develop a dynamic model that describes inter-fraction motion and deformation of OaR.
To propose at least an objective function that improves the efficiency and accuracy of the therapy.
To propose at least an adaptive radiotherapy that determine optimal treatment modifications by integrating information of the treatment systems, model predictions of the organ motion and deformation of the OaR and treatment constraints. The radiotherapy treatment defines a dynamic system with its own characteristics and uncertainties, where conventional treatment planning does not allow a proper management from inter-treatment patient variations. Thus, radiotherapy treatment is a system that is not under control and addresses to undesirable outcomes. Three stages methodology will be followed to reach the objectives proposed.

1. On the first stage will be tackled to develop a stochastic dynamic model that describes the motion and deformation of the OaR.
The model will be derived by using the CBCT images of 500 patients and weekly CT scans of 20 patients.
The method for developing the model will be based in PCA and tensor decomposition for deriving a set of variability spatial basis functions.
2. On the second stage will be tackled to develop an experimental benchmark.
To analyze and to implement the adaptive radiotherapy schemes available in the literature.
3. On the third stage will be tackled to propose an optimization problem that integrates:
The intra-fraction patient variations.
The predictive stochastic model.
The treatment constraints,
To test different configurations of functional costs and treatment modification actions. Results The main contributions from these research can be summarized as follows:

To obtain an adaptive radiotherapy technique that integrates several aspects of the radiotherapy treatment like information of the intra-fraction patient variations, predictions of motion and deformation of OaR and treatment constraints.
A modeling technique for describing the motion and deformation of OaR that could be applied in other kind of cancer.
Objective functions that are based in new criterions or in the synergy that rises from the interaction of developed criterions.
Adaptive radiotherapy technique that includes toxicity constraints.

The aim is to develop an adaptive radiotherapy scheme that customizes the therapy to the patient by using information between each treatment fraction. Decrease error between
planned dose and cumulative dose Low NTCP and high TCP Statistical Approach Statistical Approach A dynamic model was obtained using mechanical laws.
The pelvic organs are deformable bodies with specific geometries and material properties.
These anatomic entities move and interact with each other under kinematic constraints (rigid bodies and soft tissues). Mechanical Approach 3 Reference.
M. B. Boubaker, M. Haboussi, J.-F. Ganghoffer, and P. Aletti, “Finite element simulation of interactions between pelvic organs: predictive model of the prostate motion in the context of radiotherapy,” J Biomech, vol. 42, no. 12, pp. 1862–1868, Aug. 2009.

X. Chai, M. van Herk, J. B. van de Kamer, M. C. C. M. Hulshof, P. Remeijer, H. T. Lotz, and A. Bel, “Finite element based bladder modeling for image-guided radiotherapy of bladder cancer,” Medical Physics, vol. 38, no. 1, pp. 142–150, 2011.

D. Yan, D. A. Jaffray and J.W. Wong. A model to accumulate fractionated dose in a deforming organ. Int; J. Radiation Oncology Biol. Phys., vol. 44 (3), pp. 665-675, 1999. Assumptions To simulate the pelvic organs interactions in order to predict the prostate location during the radiotherapy treatment.
To promote a long term a real-time tracking radiotherapy system integrated with a dosimetry system.
A method to determine the cumulative dose in a deforming organ during the course of the treatment. The aim A mechanical model of the rectum motion (Yan, 1999).
A mechanical model of the prostate, rectum and bladder motion is developed by combining (Boubaker, 2009):
Image analysis (CT scans).
Identification of organ tissues mechanical properties.
Non FE analysis.
A mechanical model to simulate the interaction between pelvic organs solely caused by bladder volume changes ( it is based in the planning image and bladder volume changes as input). The dose reconstruction during the course of the treatment delivery can be used as an important feedback for online optimization of individual treatment plans (Yan, 1999). Cancer Prostate Modeling 14 These models used for describing the prostate cancer behavior can be sorted in three groups:
• Organ motion and deformation models.
• Models based in cell population.
• Toxicity models. Some objectives of these models are:
• To simulate pelvic organs interactions for predicting its location during the treatment.
• To deliver doses more accurately and efficiently to the target volumes.
To determine the cumulative dose during the treatment.
• To predict side effects in OaR (toxicity events) . A dynamic model was obtained using mechanical laws.
The pelvic organs are deformable bodies with specific geometries and material properties.
These anatomic entities move and interact with each other under kinematic constraints (rigid bodies and soft tissues). The NTCP models are used for estimating risk of toxicity regarding to the radiation dose.
The NTCP models are based on the dose volume histograms (DVH) . Figure. created by FreeVectors Why to set up predictive models of the pelvic organ variations? Predictive models may provide an efficient tool in the framework of prostate cancer of RT, in order to deliver dose more accurately and efficiently to the CTV. Decrease error between
planned dose and cumulative dose Low NTCP and high TCP Mechanical Approach (Boubaker, 2009), (Chai, 2011) and (Yan, 1999). Figure. Obtained from FreePik Assumptions A mechanical model of the rectum motion.
A mechanical model of the prostate, rectum and bladder motion. To model inter-fractional organ variations of adjacent organ structures in term of a spectral decomposition of eigenmodes. Statistical Approach (Söhn, 2005) and (Budiarto, 2011) Basic idea Individual based model
The coefficient "ci" are found by minimizing the error of projecting the geometry Pi over the space spanned by the eigenmodes.
The method is applied to four patient datasets of prostate/rectum/bladder with 15-18 CTs.
Population based model
Population data and PCA for deriving a model that can be used for an arbitrary patient.
The method is applied to a set of 21 patients with 4 CT scans (18) 18 patients and 3 random patients used for validation. The eigenmodes are the bases that describe the maximum variability of displacement and deformation of the organs.

The method is based in PCA for reducing the large-dimensionality of the geometric information obtained from the CT scans. A limitation of the DVH models is the lack of the spatial accuracy because is considered a homogenous delivered dose.

There is a lack of models that describes explicitly the toxicity as a function of the radiation dose and the spatial variations of the OaR.
The spatial location plays an important role in toxicity prediction (Kupchak, 2009).
Recent works have described a subtle correlation between the toxicity and the irradiated dose at some regions of the organ (Acosta and Dréan, 2012). The inherent variability in the individual's treatment has been an obstacle for the improvement of radiotherapy. Adaptive Radiotherapy It is introduced as a feedback control strategy to include patient treatment variation explicitly in the treatment planning and delivering during the treatment course (Yan, 2010). Patient variations cannot be properly managed by conventional treatment techniques. To include feedback knowledge of patient variations obtained with IGRT during the treatment in order to customize the therapy to the patient. According to INCa in France:
It was reported around 71.000 cases and around 8.700 deaths in 2011.
Its incidence rate is growing with the life expectation increasing. In Colombia, according to Instituto Nacional de Cancerología:
From 2002 to 2006 there were almost 40 000 cases and 10.897 deaths.
Its incidence is the second most higher after stomach cancer.
In addition, adaptive treatment modifications can be performed:
Online. Image information available before delivering the radiation dose in the current fraction for adapting its treatment plan.
Offline. Image information available from certain fraction is used to adapt the treatment plan for the next fraction. Typical actions of treatment modifications are:
Re-optimization of planning dose distribution.
Re-definition of PTV.
Re-definition of dose fraction size. It was the first adaptive technique.
The objective is to adapt the PTV according to information obtained during the first few fractions using CT scans.
The average position of prostate.
The average rectum deformation. Re-definition of PTV (Yan, 1997), (Yan, 2000), (Nuver,2007) and (Femke, 2009) Re-optimization of planning dose
distribution. (Wu, 2006) and (De la Zerda, 2007) Re-optimization of the dose distribution was performed between fraction in order to compensate the cumulative dose errors.
The adaptive schemes were based in CBCT scans for estimating the current cumulative dose.
De la Zerda in ([10]) developed an algorithm that uses a prediction of the geometrical anatomy of OaR in a rolling horizon for the rest of the treatment course.
The prediction model was very simple.
The objective functions based in the error between the actual cumulative dose and the planning cumulative dose (accuracy). Re-definition of dose fraction size The goal is to adapt the fraction size according to the relative position of the OaR and tumor, while the planned cumulative dose of the tumor was kept. (Lu, 2008), (Ramakrishnan, 2012), (Webb, 2008) and (Chen, 2008) The objective functions used for deriving optimal solutions were based:
The amount of dose received by the OaR
The biologically effective dose (BED) model.

It is still open to use radiobiological models like TCP/NTCP. Organ motion and deformation
models Toxicity models Conventional radiotherapy technique How to deliver sufficient doses to the CTV without damaging the OAR? There are technological possibilities to achieve a better control tumor but without accurate predictive models.... this is no devised!!... Although, IGRT and IMRT help to compensate uncertainties of the treatment system, they lie in the quality and accuracy of the registration process. In addition, the intra-fraction motion of OaR is not regular and rhythmic and lead to errors in the voxel- specific delivered dose. Therefore, a treatment modification technique should be addressed in order to overcome uncertainties and perturbations that may to yield to an undesirable outcome of the treatment process. Hence, it is possible to develop:
A dynamic model that describes intra-fraction motion and deformation of OaR.
An adaptive radiotherapy technique that based in optimization tool determines the treatment modification decisions that improve the efficiency and accuracy of the therapy.
The adaptive radiotherapy technique integrates feedback knowledge of patient-specific variation from the treatment, predictions of the organ motion and deformation from the model, and treatment constraints that relates toxicity events.
The adaptive radiotherapy technique proposes a different objective function that involves efficiency (cumulative dose), effectiveness (TCP/NTCP models) and workload. First stage How to obtain the model for the pelvic organs movement and deformation? How to use the information of the deformation fields? Third stage To introduce the model in an adaptive scheme for calculating intra-fractional cumulative DVH Schedule OUTLINE Introduction
State of the Art
Formulation problem
Project Formulation 8 8 9 10 11 12 13 15 16 17 18 The purpose of a DVH is to summarize 3D
dose distributions in a graphical 2D format THANKS!!... 19 22 23 24 25 26 27 28
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