Biologically adapted radiotherapy on the basis of hypoxia imaging with positron emission tomography [Elektronische Ressource] / vorgelegt von Daniela Thorwarth

Sujets

• Gesundheit

Informations

• Publié par: eberhard_karls_universitat_tubingen
• Publié le: 01 janvier 2007
• Nombre de lectures: 22
• Langue: English
• Poids de l'ouvrage: 13 Mo

Extrait

Biologically Adapted
Radiotherapy on the Basis of
Hypoxia Imaging with Positron
Emission Tomography
Dissertation
zur Erlangung des Grades
eines Doktors der Naturwissenschaften
der Fakult¨at fur¨ Mathematik und Physik
der Eberhard-Karls-Universit¨at zu Tubi¨ ngen
vorgelegt von
Daniela Thorwarth
aus Reutlingen
2007i
Tag der mundlic¨ hen Prufung¨ : 30. Januar 2007
Dekan: Prof. Dr. Nils Schopohl
1. Berichterstatter: Prof. Dr. Dr. Fritz Schick
2. Berichtter: Prof. Dr. Hanns Ruder
3. Berichterstatter: Prof. Dr. Fridtjof Nusslin¨iiContents
1 Introduction 1
2 Fundamentals of Tumour Biology 5
2.1 Proliferation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Neovascularization, Angiogenesis . . . . . . . . . . . . . . . . 6
2.3 Hypoxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4 Radiation Resistance . . . . . . . . . . . . . . . . . . . . . . . 7
3 The Basics of Molecular Imaging 11
3.1 Positron Emission Tomography . . . . . . . . . . . . . . . . . 11
3.1.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . 12
3.1.2 Dynamic Acquisition . . . . . . . . . . . . . . . . . . . 12
3.1.3 Image Fusion . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Hypoxia Imaging . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.1 FMISO . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.2 Tracer Transport . . . . . . . . . . . . . . . . . . . . . 17
3.3 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.1 Patient Data . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.2 Tumour Volume Delineation . . . . . . . . . . . . . . . 21
3.3.3 Treatment . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.4 Therapy Outcome . . . . . . . . . . . . . . . . . . . . . 22
4 A new Approach to Molecular Image Guided Radiotherapy 23
5 Step I: Tracer Transport Modelling 27
5.1 General Transport Characteristics . . . . . . . . . . . . . . . . 27
5.1.1 Outline of the Approach . . . . . . . . . . . . . . . . . 28
5.1.2 Diffusion-Reaction-Equation . . . . . . . . . . . . . . . 28
5.1.3 1-D Solution of the Diffusion-Reaction-Equation . . . . 29
5.2 Development of a Compartment Model . . . . . . . . . . . . . 31
5.2.1 Input Function . . . . . . . . . . . . . . . . . . . . . . 34
iiiiv CONTENTS
5.2.2 Parameter Plots . . . . . . . . . . . . . . . . . . . . . . 36
5.3 Data Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.4 Results of the Kinetic Analysis . . . . . . . . . . . . . . . . . 38
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6 Step II: Correlation to Therapy Outcome 49
6.1 Scatter Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.2 Data analysis and statistics . . . . . . . . . . . . . . . . . . . 50
6.3 Patterns of Hypoxia and Perfusion . . . . . . . . . . . . . . . 51
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
7 Step III: A Hypoxia TCP Model 59
7.1 Development of a Tumour Control Model . . . . . . . . . . . . 59
7.1.1 Observations . . . . . . . . . . . . . . . . . . . . . . . 60
7.1.2 Model Design . . . . . . . . . . . . . . . . . . . . . . . 62
7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
7.3 Consequences for HIDE . . . . . . . . . . . . . . . . . . . . . 66
8 Step IV: Dose Painting Planning Study 69
8.1 Planning Study . . . . . . . . . . . . . . . . . . . . . . . . . . 70
8.1.1 Conventional IMRT . . . . . . . . . . . . . . . . . . . . 71
8.1.2 Uniform Dose Escalation . . . . . . . . . . . . . . . . . 71
8.1.3 Hypoxia Dose Painting by Numbers . . . . . . . . . . . 71
8.1.4 Evaluation and Comparison of Treatment Plans . . . . 72
8.2 Results of the Planning Study . . . . . . . . . . . . . . . . . . 73
8.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
9 Design of a Randomized Clinical Study 83
10 Discussion 87
11 Summary and Outlook 91
Bibliography 95
Appendix: Publications 105
18A Akineticmodelfordynamic[ F]-FmisoPETdatatoanalyse
tumour hypoxia 105CONTENTS v
18B Kinetic analysis of dynamic F-fluoromisonidazole PET cor-
relates with radiation treatment outcome in head-and-neck
cancer 123
18 18C Combined uptake of [ F]-FDG and [ F]-FMISO correlates
with radiation therapy outcome in head-and-neck cancer pa-
tients 135
D Amodelofreoxygenationdynamicsofheadandnecktumors
based on serial FMISO-PET investigations 143
E Hypoxia Dose Painting by Numbers: A Planning Study 155vi CONTENTS
List of abbreviations
BTV Biological Target Volume
CT Computed Tomography
DE Dose Escalation
DP Dose Painting
DPBN Dose Painting by Numbers
DRE Diffusion-Reaction Equation
18FDG [ F]-Fluorodeoxyglucose
f-IGRT Functional Image Guided Radiotherapy
18FMISO [ F]-Fluoromisonidazole
HIDE Hypoxia Image Guided Dose Escalation
HNC Head-and-Neck Cancer
IMRT Intensity Modulated Radiotherapy
MI Mutual Information
OAR Organ at Risk
PET Positron Emission Tomography
PFS Progression-Free Survival
PTV Planning Target Volume
pi post injection
RT Radiotherapy
SUV Standardized Uptake Value
TAC Time-Activity Curve
TCP Tumour Control Probability
uniDE uniform Dose EscalationChapter 1
Introduction
The development of molecular imaging modalities has been revolutionizing
the diagnosis of human cancer in the last years. These new imaging tech-
niques, especially positron emission tomography (PET) and functional mag-
netic resonance imaging (f-MRI), are used as complementary medical imag-
ing tools in addition to computed tomography (CT). The purely anatomical
images acquired with CT allow for the determination of position and extent
ofhumantumours. Incontrast, molecularimagingdatarevealthestructural
and functional nature of the tissue, which can vary strongly throughout the
whole tumour. Only with functional imaging it is possible to quantify the
heterogeneity and irregularity of the structural tissue configuration. As a
consequence of the newly developed methods for cancer diagnosis and an
increased understanding of tumour biology, a number of targeted therapy
approaches have recently been proposed and are currently beefing investi-
gated.
Already in the 1950s it was found that heterogeneities in the structural
architecture of a tumour can cause increased resistance to radiation therapy
[21,63]. Only now it is possible to assess these characteristics with new
imaging techniques and use the obtained functional information as a basis
for the design of biologically adapted therapies.
Radiotherapy (RT) treatment especially of the head-and-neck region still
fails frequently. A targeted RT strategy, that makes use of the individual,
internal functional features of the tissue assessed by new molecular imag-
ing techniques could affect a great therapeutic gain. A biologically adapted
treatment approach that targets the tumour according to the information
detected via biological imaging may have high potential with regard to the
overall survival rate of cancer patients [37].
The main idea for the design of a targeted RT treatment is to ’sculpt’ or
’paint’ the radiation dose according to the functional characteristics of the
12 CHAPTER 1. INTRODUCTION
tumour. The required radiation doses for such a ’dose painting’ treatment
maybehighlyirregular. Therefore,aRTtechniqueisnecessarywhichisable
toaccuratelydeliverinhomogeneousandhighlyvaryingdoses. Thetechnical
basis for such an individually adapted radiotherapy is formed by intensity
modulatedradiotherapy(IMRT),asitiscapabletoproducespatiallyvariant
dose distributions [2].
During this PhD work, four main steps were developed which are nec-
essary for the translation of functional imaging data into an individually
adapted and targeted RT treatment. After a short introduction on the ba-
sicsoftumourbiologyinchapter 2andtheprincliplesofmolecularimaging
techniques in chapter 3, these four steps which were developed in the con-
text of this project are discussed.
• In order to interpret reliably the internal properties of a tumour tissue,
aseriesofmolecularimagingdataareneededwhichillustratethechar-
acteristic temporal distribution of a marker substance in the tissue.
For example, a dynamic positron emission tomography data acquisi-
tion enables us to assess this kind of temporal information. In order to
analyze and interpret these dynamic data sets, a mathematical model
which describes this transport phenomenon is necessary. In chapter
5, a physical transport model for the analysis of the uptake and distri-
18bution of the hypoxia PET tracer [ F]-Fluoromisonidazole (FMISO)
is developed. The model is derived from the general three-dimensional
Diffusion-Reaction-Equation. A number of different model parameters
can be determined from a fit of the model to the data curves which en-
able for the estimation of various functional parameters of the tumour.
Thus, the model allows us to draw conclusions about the characteristic
functional architecture of the underlying tumour tissue.
• Inasecondstep,sstatisticalanalysiswascarriedoutinordertoidentify
the main kinetic parameters which are relevant for therapy success.
To reach this goal, parameters deduced from the kinetic analysis were
comp