Iuliana Toma-Dasu

The use of risk estimation models for the induction of secondary cancers following radiotherapy.

Theoretical predictions of cancer risk from radiotherapy may be used as a complementary criterion for the selection of successful treatment plans together with the classical approach of estimating the possible deterministic effects. However, any such attempts must take into consideration the specific features of radiation treatment. This paper explores several possible methods for estimating the risk of cancer following radiotherapy in order to investigate the influences of the fractionation and the non-uniformity of the dose to the irradiated organ. The results indicate that dose inhomogeneity plays an important role in predicting the risk for secondary cancer and therefore for predictive purposes it must be taken into account through the use of the dose volume histograms. They also suggest that the competition between cell killing and the induction of carcinogenic mutations has to be taken into consideration for more realistic risk estimations. Furthermore, more realistic parameters could be obtained if this competition is also included in analyses of epidemiological data from radiotherapy applications.

Theoretical simulation of tumour oxygenation and results from acute and chronic hypoxia

The tumour microenvironment is considered to be responsible for the outcome of cancer treatment and therefore it is extremely important to characterize and quantify it. Unfortunately, most of the experimental techniques available now are invasive and generally it is not known how this influences the results. Non-invasive methods on the other hand have a geometrical resolution that is not always suited for the modelling of the tumour response. Theoretical simulation of the microenvironment may be an alternative method that can provide quantitative data for accurately describing tumour tissues. This paper presents a computerized model that allows the simulation of the tumour oxygenation. The model simulates numerically the fundamental physical processes of oxygen diffusion and consumption in a two-dimensional geometry in order to study the influence of the different parameters describing the tissue geometry. The paper also presents a novel method to simulate the effects of diffusion-limited (chronic) hypoxia and perfusion-limited (acute) hypoxia. The results show that all the parameters describing tissue vasculature are important for describing tissue oxygenation. Assuming that vascular structure is described by a distribution of inter-vessel distances, both the average and the width of the distribution are needed in order to fully characterize the tissue oxygenation. Incomplete data, such as distributions measured in a non-representative region of the tissue, may not give relevant tissue oxygenation. Theoretical modelling of tumour oxygenation also allows the separation between acutely and chronically hypoxic cells, a distinction that cannot always be seen with other methods. It was observed that the fraction of acutely hypoxic cells depends not only on the fraction of collapsed blood vessels at any particular moment, but also on the distribution of vessels in space as well. All these suggest that theoretical modelling of tissue oxygenation starting from the basic principles is a robust method that can be used to quantify the tissue oxygenation and to provide input parameters for other simulations.

Prostate alpha/beta revisited – an analysis of clinical results from 14 168 patients

Abstract Purpose. To determine the dose response parameters and the fractionation sensitivity of prostate tumours from clinical results of patients treated with external beam radiotherapy. Material and methods. The study was based on five-year biochemical results from 14 168 patients treated with external beam radiotherapy. Treatment data from 11 330 patients treated with conventional fractionation have been corrected for overall treatment time and fitted with a logit equation. The results have been used to determine the optimum α/β values that minimise differences in predictions from 2838 patients treated with hypofractionated schedules. Results. Conventional fractionation data yielded logit dose response parameters for all risk groups and for all definitions of biochemical failures. The analysis of hypofractionation data led to very low α/β values (1–1.7 Gy) in all mentioned cases. Neglecting the correction for overall treatment time has little impact on the derivation of α/β values for prostate cancers. Conclusions. These results indicate that the high fractionation sensitivity is an intrinsic property of prostate carcinomas and they support the use of hypofractionation to increase the therapeutic gain for these tumours.

Dose prescription and treatment planning based on FMISO-PET hypoxia

Abstract Purpose. The study presents the implementation of a novel method for incorporating hypoxia information from PET-CT imaging into treatment planning and estimates the efficiency of various optimization approaches. Its focuses on the feasibility of optimizing treatment plans based on the non-linear conversion of PET hypoxia images into radiosensitivity maps from the uptake properties of the tracers used. Material and methods. PET hypoxia images of seven head-and-neck cancer patients were used to determine optimal dose distributions needed to counteract the radiation resistance associated with tumor hypoxia assuming various scenarios regarding the evolution of the hypoxic compartment during the treatment. A research planning system for advanced studies has been used to optimize IMRT plans based on hypoxia information from patient PET images. These resulting plans were compared in terms of target coverage for the same fulfilled constraints regarding the organs at risk. Results. The results of a planning study indicated the clinical feasibility of the proposed method for treatment planning based on PET hypoxia. Antihypoxic strategies would lead to small improvements in all the patients, but higher effects are expected for the fraction of patients with hypoxic tumors. For these, individualization of the treatment based on hypoxia PET imaging could lead to improved treatment outcome while creating the premises for limiting the irradiation of the surrounding normal tissues. Conclusions. The proposed approach offers the possibility of improved treatment results as it takes into consideration the heterogeneity and the dynamics of the hypoxic regions. It also provides early identification of the clinical cases that might benefit from dose escalation as well as the cases that could benefit from other counter-hypoxic measures.

Should single or distributed parameters be used to explain the steepness of tumour control probability curves

Linear quadratic (LQ) modelling allows easy comparison of different fractionation schedules in radiotherapy. However, estimating the radiation effect of a single fractionated treatment introduces many questions with respect to the parameters to be used in the modelling process. Several studies have used tumour control probability (TCP) curves in order to derive the values for the LQ parameters that may be used further for the analysis and ranking of treatment plans. Unfortunately, little attention has been paid to the biological relevance of these derived parameters, either for the initial number of cells or their intrinsic radiosensitivity, or both. This paper investigates the relationship between single values for the TCP parameters and the resulting dose-response curve. The results of this modelling study show how clinical observations for the position and steepness of the TCP curve can be explained only by the choice of extreme values for the parameters, if they are single values. These extreme values are in contradiction with experimental observations. This contradiction suggests that single values for the parameters are not likely to explain reasonably the clinical observations and that some distributions of input parameters should be taken into consideration.

Dose prescription and optimisation based on tumour hypoxia

Introduction. Tumour hypoxia is an important factor that confers radioresistance to the affected cells and could thus decrease the tumour response to radiotherapy. The development of advanced imaging methods that quantify both the extent and the spatial distribution of the hypoxic regions has created the premises to devise therapies that target the hypoxic regions in the tumour. Materials and methods. The present study proposes an original method to prescribe objectively dose distributions that focus the radiation dose to the radioresistant tumour regions and could therefore spare adjacent normal tissues. The effectiveness of the method was tested for clinically relevant simulations of tumour hypoxia that take into consideration dynamics and heterogeneity of oxygenation. Results and discussion. The results have shown that highly heterogeneous dose distributions may lead to significant improvements of the outcome only for static oxygenations. In contrast, the proposed method that involves the segmentation of the dose distributions and the optimisation of the dose prescribed to each segment to account for local heterogeneity may lead to significantly improved local control for clinically-relevant patterns of oxygenation. The clinical applicability of the method is warranted by its relatively easy adaptation to functional imaging of tumour hypoxia obtained with markers with known uptake properties.

Disregarding RBE variation in treatment plan comparison may lead to bias in favor of proton plans

PURPOSE Currently in proton radiation therapy, a constant relative biological effectiveness (RBE) equal to 1.1 is assumed. The purpose of this study is to evaluate the impact of disregarding variations in RBE on the comparison of proton and photon treatment plans. METHODS Intensity modulated treatment plans using photons and protons were created for three brain tumor cases with the target situated close to organs at risk. The proton plans were optimized assuming a standard RBE equal to 1.1, and the resulting linear energy transfer (LET) distribution for the plans was calculated. In the plan evaluation, the effect of a variable RBE was studied. The RBE model used considers the RBE variation with dose, LET, and the tissue specific parameter α/β of photons. The plan comparison was based on dose distributions, DVHs and normal tissue complication probabilities (NTCPs). RESULTS Under the assumption of RBE=1.1, higher doses to the tumor and lower doses to the normal tissues were obtained for the proton plans compared to the photon plans. In contrast, when accounting for RBE variations, the comparison showed lower doses to the tumor and hot spots in organs at risk in the proton plans. These hot spots resulted in higher estimated NTCPs in the proton plans compared to the photon plans. CONCLUSIONS Disregarding RBE variations might lead to suboptimal proton plans giving lower effect in the tumor and higher effect in normal tissues than expected. For cases where the target is situated close to structures sensitive to hot spot doses, this trend may lead to bias in favor of proton plans in treatment plan comparisons.

Dose-effect models for risk - relationship to cell survival parameters

There is an increased interest in estimating the induction of cancers following radiotherapy as the patients have nowadays a much longer life expectancy following the treatment. Clinical investigations have shown that the dose response relationship for cancer induction following radiotherapy has either of two main characteristics: an increase of the risk with dose to a maximum effect followed by a decrease or an increase followed by a levelling-off of the risk. While these behaviours have been described qualitatively, there is no mathematical model that can explain both of them on mechanistic terms. This paper investigates the relationship between the shape of the dose-effect curve and the cell survival parameters of a single risk model. Dose response relationships were described with a competition model which takes into account the probability to induce DNA mutations and the probability of cell survival after irradiation. The shape of the curves was analysed in relation to the parameters that have been used to obtain them. It was found that the two main appearances of clinical data for the induction of secondary cancer following radiotherapy could be the manifestations of the particular sets of parameters that describe the induction of mutations and cell kill for fractionated irradiations. Thus, the levelling off appearance of the dose response curve could be either a sign of moderate to high inducible repair effect in cell survival (but weak for DNA mutations) or the effect of heterogeneity, or both. The bell-shaped appearance encompasses all the other cases. The results also stress the importance of taking into account the details of the clinical delivery of dose in radiotherapy, mainly the fractionated character, as the findings of our study did not appear for single dose models. The results thus indicate that the shapes of clinically observed dose response curves for the induction of secondary cancers can be described by using one single competition model. It was also found that data for cancer induction may be linked to in vivo cell survival parameters that may be used for other modelling applications.

Radiobiological description of the LET dependence of the cell survival of oxic and anoxic cells irradiated by carbon ions

Light-ion radiation therapy against hypoxic tumors is highly curative due to reduced dependence on the presence of oxygen in the tumor at elevated linear energy transfer (LET) towards the Bragg peak. Clinical ion beams using spread-out Bragg peak (SOBP) are characterized by a wide spectrum of LET values. Accurate treatment optimization requires a method that can account for influence of the variation in response for a broad range of tumor hypoxia, absorbed doses and LETs. This paper presents a parameterization of the Repairable Conditionally-Repairable (RCR) cell survival model that can describe the survival of oxic and hypoxic cells over a wide range of LET values, and investigates the relationship between hypoxic radiation resistance and LET. The biological response model was tested by fitting cell survival data under oxic and anoxic conditions for V79 cells irradiated with LETs within the range of 30–500 keV/µm. The model provides good agreement with experimental cell survival data for the range of LET investigated, confirming the robustness of the parameterization method. This new version of the RCR model is suitable for describing the biological response of mixed populations of oxic and hypoxic cells and at the same time taking into account the distribution of doses and LETs in the incident beam and its variation with depth in tissue. The model offers a versatile tool for the selection of LET and dose required in the optimization of the therapeutic effect, without severely affecting normal tissue in realistic tumors presenting highly heterogeneous oxic and hypoxic regions.

Impact of variable RBE on proton fractionation

PURPOSE To explore the impact of variable proton relative biological effectiveness (RBE) on dose fractionation for clinically relevant situations. A generic RBE = 1.1 is generally used for isoeffect calculations, while experimental studies showed that proton RBE varies with tissue type, dose, and linear energy transfer (LET). METHODS An analytical expression for the LET and α∕β dependence of the linear-quadratic (LQ) model has been used for proton simulations in parallel with the assumption of a generic RBE = 1.1. Calculations have been performed for ranges of LET values and fractionation sensitivities to describe clinically relevant cases, such as the treatment of head and neck and prostate tumors. Isoeffect calculations were compared with predictions from a generic RBE value and reported clinical results. RESULTS The generic RBE = 1.1 appears to be a reasonable estimate for the proton RBE of rapidly growing tissues irradiated with low LET radiation. However, the use of a variable RBE predicts larger differences for tissues with low α∕β (both tumor and normal) and at low doses per fraction. In some situations these differences may appear in contrast to the findings from photon studies highlighting the importance of accurate accounting for the radiobiological effectiveness of protons. Furthermore, the use of variable RBE leads to closer predictions to clinical results. CONCLUSIONS The LET dependence of the RBE has a strong impact on the predicted effectiveness of fractionated proton radiotherapy. The magnitude of the effect is modulated by the fractionation sensitivity and the fractional dose indicating the need for accurate analyses both in the target and around it. Care should therefore be employed for changing clinical fractionation patterns or when analyzing results from clinical studies for this type of radiation.