<p>Biological treatment optimization aim at improving radiation therapy by accounting for the radiobiological tumour and normal tissues response properties when optimizing the dose delivery. Generally traditional methods, using only dosimetrical measures, disregard the nonlinear radiation response of different tumours and normal tissues. The accumulated knowledge on tissue response to radiation, in the form of more accurate dose response relations, cell survival models and their associated biological parameters, alongside with the tools for biological treatment plan optimization, has allowed the present investigation on the potential merits of biologically based treatment optimization in radiation therapy.</p><p>With a more widespread implementation of intensity modulated radiation therapy in the clinic, there is an increasing demand for faster and safer treatment delivery techniques. In this thesis biological treatment plan optimization, using the probability to achieve complication free tumour control as the quantifier for treatment outcome, was applied to radiation therapy of early breast cancer and advanced cervix cancer. It is shown that very conformal dose distributions can generally be produced with 3 or 4 optimally orientated coplanar intensity modulated beams, without having clinically significant losses in treatment outcome from the optimal dose distribution.</p><p>By using exhaustive search methods, the optimal coplanar beam directions for intensity modulated photon beams for early breast cancer and the optimal non-coplanar directions for an advanced cervix cancer were investigated. Although time consuming, exhaustive search methods have the advantage of revealing most features involving interactions between a small number of beams and how this may influence the treatment outcome. Thus phase spaces may serve as a general database for selecting an almost optimal treatment configuration for similar patients. Previous knowledge acquired with physically optimized uniform beam radiation therapy may not apply when intensity modulated biological optimization is used. Thus unconventional treatment directions were sometimes found.</p>

Biological treatment optimization aim at improving radiation therapy by accounting for the radiobiological tumour and normal tissues response properties when optimizing the dose delivery. Generally traditional methods, using only dosimetrical measures, disregard the nonlinear radiation response of different tumours and normal tissues. The accumulated knowledge on tissue response to radiation, in the form of more accurate dose response relations, cell survival models and their associated biological parameters, alongside with the tools for biological treatment plan optimization, has allowed the present investigation on the potential merits of biologically based treatment optimization in radiation therapy. With a more widespread implementation of intensity modulated radiation therapy in the clinic, there is an increasing demand for faster and safer treatment delivery techniques. In this thesis biological treatment plan optimization, using the probability to achieve complication free tumour control as the quantifier for treatment outcome, was applied to radiation therapy of early breast cancer and advanced cervix cancer. It is shown that very conformal dose distributions can generally be produced with 3 or 4 optimally orientated coplanar intensity modulated beams, without having clinically significant losses in treatment outcome from the optimal dose distribution. By using exhaustive search methods, the optimal coplanar beam directions for intensity modulated photon beams for early breast cancer and the optimal non-coplanar directions for an advanced cervix cancer were investigated. Although time consuming, exhaustive search methods have the advantage of revealing most features involving interactions between a small number of beams and how this may influence the treatment outcome. Thus phase spaces may serve as a general database for selecting an almost optimal treatment configuration for similar patients. Previous knowledge acquired with physically optimized uniform beam radiation therapy may not apply when intensity modulated biological optimization is used. Thus unconventional treatment directions were sometimes found.

Protonen töten Zellen wirksamer ab als Photonen. Die klinisch verwendete konstante relative biologische Wirksamkeit (RBW) für Protonen vernachlässigt jedoch erste klinische Evidenz einer RBW-Variabilität, die vom linearen Energietransfer (LET) abhängt. Diese Arbeit trägt dazu bei, die RBW-Variabilität in Protonen-Bestrahlungsplänen zu berücksichtigen, um potenzielle Nebenwirkungen zu vermindern. Zuerst wurde ein erhöhtes Risiko für RBW-induzierte Nebenwirkungen bei Hirntumorpatienten festgestellt. Dies konnte jedoch nicht systematisch durch klinische Planungsstrategien reduziert werden. Zweitens ergab eine multizentrische europäische Studie, dass die zentrums-spezifischen, nicht standardisierten LET-Berechnungen erheblich voneinander abweichen. Eine harmonisierte LET-Definition wurde vorgeschlagen und reduzierte die Variabilität zwischen den Zentren auf ein klinisch akzeptables Niveau, was künftig eine einheitliche Dokumentation des Therapieergebnisses ermöglicht. Abschließend wurden vier Strategien zur RBW-Reduktion in der Planoptimierung bei Hirntumorpatienten angewandt, die das Risiko für Nekrose und Erblindung erheblich reduzierten. LET-Optimierung in Hochdosisregionen erscheint besonders geeignet, um die Sicherheit der Patientenbehandlung künftig weiter zu verbessern.:List of Figures vii List of Tables viii List of Acronyms and Abbreviations ix 1 Introduction 1 2 Theoretical background 3 2.1 Proton interactions with matter 4 2.2 Biological effect of radiation 8 2.2.1 Linear-quadratic model 8 2.2.2 Relative biological effectiveness 9 2.3 Proton beam delivery and field formation 13 2.4 Treatment planning 14 2.4.1 Patient modelling and structure definition 15 2.4.2 Treatment plan optimisation 16 2.4.3 Treatment plan evaluation 19 2.5 Proton therapy uncertainties and mitigation strategies 22 2.5.1 Clinical mitigation strategies 23 2.5.2 Optimisation approaches beyond absorbed dose 26 3 Variable biological effectiveness in PBS treatment plans 29 3.1 LET and RBE recalculations of proton treatment plans with RayStation 30 3.1.1 Monte Carlo dose engine 30 3.1.2 Monte Carlo scoring extensions 32 3.1.3 Graphical user interface 33 3.2 LET assessment and the role of range uncertainties 36 3.2.1 Patient cohort and treatment plan creation 37 3.2.2 Simulation of range deviations 38 3.2.3 Treatment plan recalculation settings 39 3.2.4 Resulting impact of range deviations 40 3.3 Patient recalculations in case of side effects 46 3.3.1 Image registration and range prediction 48 3.3.2 Retrospective treatment plan assessment 49 3.4 Benefit of an additional treatment field 50 3.4.1 Patient and treatment plan information 50 3.4.2 Results of variable RBE recalculations 51 3.5 Discussion 51 3.6 Summary 59 4 Status of LET and RBE calculations in European proton therapy 61 4.1 Study design 62 4.1.1 Treatment planning information 64 4.1.2 Data processing and treatment plan evaluation 67 4.2 Treatment plan comparisons in the water phantom 68 4.2.1 Absorbed dose evaluation 69 4.2.2 Centre-specific LET calculations 69 4.2.3 Harmonised LET calculations 71 4.3 Treatment plan comparisons in patient cases 72 4.3.1 Dose-averaged linear energy transfer for protons 73 4.3.2 Centre-specific RBE models and parameters 76 4.4 Discussion 77 4.5 Summary 82 5 Biological treatment plan optimisation 83 5.1 Treatment plan design 84 5.1.1 Clinical goals 86 5.1.2 Novel treatment plan optimisation approaches 87 5.2 Treatment plan quality assessment with a constant RBE 90 5.3 Assessment of NTCP reductions with a variable RBE 90 5.4 Discussion 95 5.5 Conclusion 100 6 Summary 103 7 Zusammenfassung 107 Bibliography 111 Danksagung 137 / Protons are more effective in cell killing than photons. However, the clinically applied constant proton relative biological effectiveness (RBE) neglects emerging clinical evidence for RBE variability driven by the linear energy transfer (LET). This thesis aims to safely account for RBE variability in proton treatment plans to mitigate potential side effects. First, an elevated risk for RBE induced overdosage was found in brain tumour patients. However, this could not be mitigated systematically by clinical planning strategies. Second, a multicentric European study revealed that centre-specific non-standardised LET calculations differed substantially. A harmonised LET definition was proposed which reduced the inter-centre variability to a clinically acceptable level and allows for future consistent outcome reporting. Finally, four strategies to include RBE variability in treatment plan optimisation were applied to brain tumour patients, which considerably reduced the estimated risk for necrosis and blindness. Of these, LET optimisation in high dose regions may be suited for clinical practice to further enhance patient safety in view of a variable RBE.:List of Figures vii List of Tables viii List of Acronyms and Abbreviations ix 1 Introduction 1 2 Theoretical background 3 2.1 Proton interactions with matter 4 2.2 Biological effect of radiation 8 2.2.1 Linear-quadratic model 8 2.2.2 Relative biological effectiveness 9 2.3 Proton beam delivery and field formation 13 2.4 Treatment planning 14 2.4.1 Patient modelling and structure definition 15 2.4.2 Treatment plan optimisation 16 2.4.3 Treatment plan evaluation 19 2.5 Proton therapy uncertainties and mitigation strategies 22 2.5.1 Clinical mitigation strategies 23 2.5.2 Optimisation approaches beyond absorbed dose 26 3 Variable biological effectiveness in PBS treatment plans 29 3.1 LET and RBE recalculations of proton treatment plans with RayStation 30 3.1.1 Monte Carlo dose engine 30 3.1.2 Monte Carlo scoring extensions 32 3.1.3 Graphical user interface 33 3.2 LET assessment and the role of range uncertainties 36 3.2.1 Patient cohort and treatment plan creation 37 3.2.2 Simulation of range deviations 38 3.2.3 Treatment plan recalculation settings 39 3.2.4 Resulting impact of range deviations 40 3.3 Patient recalculations in case of side effects 46 3.3.1 Image registration and range prediction 48 3.3.2 Retrospective treatment plan assessment 49 3.4 Benefit of an additional treatment field 50 3.4.1 Patient and treatment plan information 50 3.4.2 Results of variable RBE recalculations 51 3.5 Discussion 51 3.6 Summary 59 4 Status of LET and RBE calculations in European proton therapy 61 4.1 Study design 62 4.1.1 Treatment planning information 64 4.1.2 Data processing and treatment plan evaluation 67 4.2 Treatment plan comparisons in the water phantom 68 4.2.1 Absorbed dose evaluation 69 4.2.2 Centre-specific LET calculations 69 4.2.3 Harmonised LET calculations 71 4.3 Treatment plan comparisons in patient cases 72 4.3.1 Dose-averaged linear energy transfer for protons 73 4.3.2 Centre-specific RBE models and parameters 76 4.4 Discussion 77 4.5 Summary 82 5 Biological treatment plan optimisation 83 5.1 Treatment plan design 84 5.1.1 Clinical goals 86 5.1.2 Novel treatment plan optimisation approaches 87 5.2 Treatment plan quality assessment with a constant RBE 90 5.3 Assessment of NTCP reductions with a variable RBE 90 5.4 Discussion 95 5.5 Conclusion 100 6 Summary 103 7 Zusammenfassung 107 Bibliography 111 Danksagung 137