External radiotherapy dosimetry in nonstandard fields

The dosimetry of the small and intensity modulated elds employed in radiotherapy, with high dose gradients involved, is a quite demanding task. The need for reliable measurements in these beams responds not only to quality assurance requirements, but also to the legal regulations of Radiotherapy (in Spain, Real Decreto 1566/1998 sobre Criterios de Calidad en Radioterapia, and also EURATOM 97/43). The complexity of modern radiotherapy techniques led to an extensive incorporation of thorough treatment dosimetric veri cation in the hospital quality assurance programs. This veri cation, previous to the treatment, is performed in order to check that the dose distributions delivered by the radiotherapy machine match the corresponding planned dose distributions within the required tolerances. One work performed in this thesis project consists in the study of di erent commercial detector arrays, devices widely employed for dosimetric treatment veri cation. The response of the detectors involved in these devices is determined in order to study the impact of the detector size, technology and layout on the measurement of intensity modulated dose distributions. The capabilities of detector arrays for the detection of uence variations is also studied, as this is one of the main objectives of treatment veri cation

A mathematical model of thyroid disease response to radiotherapy

We present a mechanistic biomathematical model of molecular radiotherapy of thyroid disease. The general model consists of a set of differential equations describing the dynamics of different populations of thyroid cells with varying degrees of damage caused by radiotherapy (undamaged cells, sub-lethally damaged cells, doomed cells, and dead cells), as well as the dynamics of thyroglobulin and antithyroglobulin autoantibodies, which are important surrogates of treatment response. The model is presented in two flavours: on the one hand, as a deterministic continuous model, which is useful to fit populational data, and on the other hand, as a stochastic Markov model, which is particularly useful to investigate tumor control probabilities and treatment individualization. The model was used to fit the response dynamics (tumor/thyroid volumes, thyroglobulin and antithyroglobulin autoantibodies) observed in experimental studies of thyroid cancer and Graves’ disease treated with 131I-radiotherapy. A qualitative adequate fitting of the model to the experimental data was achieved. We also used the model to investigate treatment individualization strategies for differentiated thyroid cancer, aiming to improve the tumor control probability. We found that simple individualization strategies based on the absorbed dose in the tumor and tumor radiosensitivity (which are both magnitudes that can potentially be individually determined for every patient) can lead to an important raise of tumor control probabilities.This project has received funding from the Instituto de Salud Carlos III (PI17/01428 grant, FEDER co-funding). This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 839135. This project has received funding from FEDER/Ministerio de Ciencia, Innovación y Universidades;Agencia Estatal de Investigación, under grant MTM2017-84446-C2-2-R

Simulation of hypoxia PET-tracer uptake in tumours:Dependence of clinical uptake-values on transport parameters and arterial input function

Poor radiotherapy outcome is in many cases related to hypoxia, due to the increased radioresistance of hypoxic tumour cells. Positron emission tomography may be used to non-invasively assess the oxygenation status of the tumour using hypoxia-specific radiotracers. Quantification and interpretation of these images remains challenging, since radiotracer binding and oxygen tension are not uniquely related. Computer simulation is a useful tool to improve the understanding of tracer dynamics and its relation to clinical uptake parameters currently used to quantify hypoxia. In this study, a model for simulating oxygen and radiotracer distribution in tumours was implemented to analyse the impact of physiological transport parameters and of the arterial input function (AIF) on: oxygenation histograms, time-activity curves, tracer binding and clinical uptake-values (tissue-to-blood ratio, TBR, and a composed hypoxia-perfusion metric, FHP). Results were obtained for parallel and orthogonal vessel architectures and for vascular fractions (VFs) of 1% and 3%. The most sensitive parameters were the AIF and the maximum binding rate (K-max). TBR allowed discriminating VF for different AIF, and FHP for different K-max, but neither TBR nor FHP were unbiased in all cases. Biases may especially occur in the comparison of TBR- or FHP-values between different tumours, where the relation between measured and actual AIF may vary. Thus, these parameters represent only surrogates rather than absolute measurements of hypoxia in tumours.Pontificia Universidad Catolica de Chile (UC) from the German Academic Exchange Service (DAAD) German Cancer Research Center (DKFZ) from the German Academic Exchange Service (DAAD) grant CONICYT Doctorado Nacional 21151353 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 111505601 1117057

eIMRT: a web platform for the verification and optimization of radiation treatment plans

The eIMRT platform is a remote distributed computing tool that provides users with Internet access to three different services: Monte Carlo optimization of treatment plans, CRT & IMRT treatment optimization, and a database of relevant radiation treatments/clinical cases. These services are accessible through a user-friendly and platform independent web page. Its flexible and scalable design focuses on providing the final users with services rather than a collection of software pieces. All input and output data (CT, contours, treatment plans and dose distributions) are handled using the DICOM format. The design, implementation, and support of the verification and optimization algorithms are hidden to the user. This allows a unified, robust handling of the software and hardware that enables these computation-intensive services. The eIMRT platform is currently hosted by the Galician Supercomputing Center (CESGA) and may be accessible upon request (there is a demo version at http://eimrt.cesga.es:8080/ eIMRT2/demo; request access in http://eimrt.cesga.es/signup.html). This paper describes all aspects of the eIMRT algorithms in depth, its user interface, and its services. Due to the flexible design of the platform, it has numerous applications including the intercenter comparison of treatment planning, the quality assurance of radiation treatments, the design and implementation of new approaches to certain types of treatments, and the sharing of information on radiation treatment techniques. In addition, the web platform and software tools developed for treatment verification and optimization have a modular design that allows the user to extend them with new algorithms. This software is not a commercial product. It is the result of the collaborative effort of different public research institutions and is planned to be distributed as an open source project. In this way, it will be available to any user; new releases will be generated with the new implemented codes or upgradesThis work was financed by Xunta de Galicia of Spain through grant PGIDT05SIN00101CT and by the European Community through the BeInGrid projectS

A Mathematical Model of Thyroid Disease Response to Radiotherapy

We present a mechanistic biomathematical model of molecular radiotherapy of thyroid disease. The general model consists of a set of differential equations describing the dynamics of different populations of thyroid cells with varying degrees of damage caused by radiotherapy (undamaged cells, sub-lethally damaged cells, doomed cells, and dead cells), as well as the dynamics of thyroglobulin and antithyroglobulin autoantibodies, which are important surrogates of treatment response. The model is presented in two flavours: on the one hand, as a deterministic continuous model, which is useful to fit populational data, and on the other hand, as a stochastic Markov model, which is particularly useful to investigate tumor control probabilities and treatment individualization. The model was used to fit the response dynamics (tumor/thyroid volumes, thyroglobulin and antithyroglobulin autoantibodies) observed in experimental studies of thyroid cancer and Graves' disease treated with I-131-radiotherapy. A qualitative adequate fitting of the model to the experimental data was achieved. We also used the model to investigate treatment individualization strategies for differentiated thyroid cancer, aiming to improve the tumor control probability. We found that simple individualization strategies based on the absorbed dose in the tumor and tumor radiosensitivity (which are both magnitudes that can potentially be individually determined for every patient) can lead to an important raise of tumor control probabilities

Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model.

Motivated by the capabilities of modern radiotherapy techniques and by the recent developments of functional imaging techniques, dose painting by numbers (DPBN) was proposed to treat tumors with heterogeneous biological characteristics. This work studies different DPBN optimization techniques for virtual head and neck tumors assessing tumor response in terms of cell survival and tumor control probability with a previously published tumor response model (TRM). Uniform doses of 2 Gy are redistributed according to the microscopic oxygen distribution and the density distribution of tumor cells in four virtual tumors with different biological characteristics. In addition, two different optimization objective functions are investigated, which: i) minimize tumor cell survival (OFsurv) or; ii) maximize the homogeneity of the density of surviving tumor cells (OFstd). Several adaptive schemes, ranging from single to daily dose optimization, are studied and the treatment response is compared to that of the uniform dose. The results show that the benefit of DPBN treatments depends on the tumor reoxygenation capability, which strongly differed among the set of virtual tumors investigated. The difference between daily (fraction by fraction) and three weekly optimizations (at the beginning of weeks 1, 3 and 4) was found to be small, and higher benefit was observed for the treatments optimized using OFsurv. This in silico study corroborates the hypothesis that DPBN may be beneficial for treatments of tumors which show reoxygenation during treatment, and that a few optimizations may be sufficient to achieve this therapeutic benefit

Computational tumor models and oxygenation histograms used in the work: Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model

This dataset contains the files defining the four computational head and neck tumor models and the oxygenation histograms used in the work "Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model". Regarding the tumor models, information about the number of total cells, proliferating tumor cells, as well as the tumor vascular fraction is provided for each tumor model. Each tumor model is embedded in a cubic volume having 100 voxels per side (each voxel has a 1.123595 mm side). The total volume of each tumor model consists therefore of a total of 10^6 voxels. All models consist on a spherical tumor of approximately 2 cm diameter placed at the center of the volume. The files provided for each tumor model have the information corresponding to each voxel, presented row by row. Three files are needed to define each tumor model: 1) Totalcells.txt is a file specifying the total number of cells existing at each tumor voxel; 2) Tumorcells.txt is a file with number of tumor (proliferating) cells existing at each tumor voxel; and 3) Capillarycells.txt is a file with the number of capillary cells existing at each tumor voxel (vascular fraction can be calculated as the ratio of capillary to total cells per voxel). There are also normal cells within the tumor. The number of normal cells per voxel can be calculated from subtraction of the number of tumor and capillary cells from the total number of cells. Description of the tumor models: tumor model 1) non-uniform distribution of tumor cells and vf; tumor model 2) uniform distribution of tumor cells and vf; tumor model 3) uniform distritubion of tumor cells and non-uniform distribution of vf; and Tumor model 4) non-uniform distribution of of tumor cells and uniform distribution of vf. Regarding oxygenation histograms, information is provided about the oxygenation of tumoral tissue having different values of vascular fraction and maximum oxygen consumption rate. Oxygen distributions were calculated by solving the reaction diffusion equation in a 2D geometry with randomly distributed parallel vessels of 20 micrometres x 20 micrometres. Nine “.dat” files are provided with oxygenation histograms corresponding to tumor tissue with different values of maximum oxygen consumption rate, from 15 to 3 mmHg s^−1 (in files 150cc.dat and file 030cc.dat, respectively). Each line of the file has one histogram of 16 bins, associated to certain value of vascular fraction, VF. The VF corresponding to each histogram is specified in the first column. Oxygenation histograms are provided for vascular fractions ranging from 0.1% to 15% (one can observe the 0.001 and 0.15 values corresponding to these VF values specified at the beginning of the first and last lines of each file). Vascular fractions change in steps of 0.1% and thus each file has 150 lines. All data provided here is the result of computational simulations (no clinical data)

Treatment gains.

<p>Treatment gains obtained for the studied tumors (T1-T4) using dose distributions optimized with either OF_surv (∘) or OF_std (*) under different adaptive schemes.</p

Tumor response to a uniform dose distribution.

<p>(a) Number of surviving tumor cells with time for the 4 tumor types irradiated with a uniform dose distribution. The weekend treatment breaks lead to small plateaus in the cells survival curves, in which the number of tumor cells increases slightly due to proliferation. (b) TCP curves for the simulated tumors and experimental response of a 200 <i>mm</i>³ xenograft of the H&N FaDu line [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196310#pone.0196310.ref067" target="_blank">67</a>]. The D50_conv values (in Gy) of the simulated TCP curves are 68.6 ± 1.4, 65.6 ± 2.1, 71.3 ± 2.0 and 66.0 ± 2.0, for T1, T2, T3 and T4 respectively.</p

Impact of the adaptive scheme on treatment gain.

<p>Treatment gains associated to the DPBN treatments under the adaptive schemes 3F, FBF3W and FBF4W. Tumors ordered on the x-axis by ascending reoxygenation capability.</p