The presented virtual energy fluence (VEF) model of the patient-independent part of the medical linear accelerator heads, consists of two Gaussian-shaped photon sources and one uniform electron source. The planar photon sources are located close to the bremsstrahlung target (primary source) and to the flattening filter (secondary source), respectively. The electron contamination source is located in the plane defining the lower end of the filter. The standard deviations or widths and the relative weights of each source are free parameters. Five other parameters correct for fluence variations, i.e., the horn or central depression effect. If these parameters and the field widths in the X and Y directions are given, the corresponding energy fluence distribution can be calculated analytically and compared to measured dose distributions in air. This provides a method of fitting the free parameters using the measurements for various square and rectangular fields and a fixed number of monitor units. The next step in generating the whole set of base data is to calculate monoenergetic central axis depth dose distributions in water which are used to derive the energy spectrum by deconvolving the measured depth dose curves. This spectrum is also corrected to take the off-axis softening into account. The VEF model is implemented together with geometry modules for the patient specific part of the treatment head (jaws, multileaf collimator) into the XVMC dose calculation engine. The implementation into other Monte Carlo codes is possible based on the information in this paper. Experiments are performed to verify the model by comparing measured and calculated dose distributions and output factors in water. It is demonstrated that open photon beams of linear accelerators from two different vendors are accurately simulated using the VEF model. The commissioning procedure of the VEF model is clinically feasible because it is based on standard measurements in air and water. It is also useful for IMRT applications because a full Monte Carlo simulation of the treatment head would be too time-consuming for many small fields.

Image guided radiotherapy has the potential to improve both tumour control and normal tissue sparing by including temporal patient specific geometry information into the adaptive planning process. In this study we present a practical method of image guided adaptive inverse planning based on computed tomography (CT) and portal image feedback during the treatment course. The method is based on a general description of the radiotherapy optimization problem subject to dynamic geometrical variations of the patient/organs. We will demonstrate the feasibility of off-line image feedback into the inverse planning process with the example of three prostate cancer patients. CT and portal images acquired during the early course of the treatment are used to predict the geometrical variation distribution of a patient and to re-optimize the treatment plan accordingly. We will study the convergence of the optimization problem with respect to the number of image measurements and adaptive feedback loops.

A method for the quantitative four-dimensional (4D) evaluation of discrete dose data based on gradient-dependent local acceptance thresholds is presented. The method takes into account the local dose gradients of a reference distribution for critical appraisal of misalignment and collimation errors. These contribute to the maximum tolerable dose error at each evaluation point to which the local dose differences between comparison and reference data are compared. As shown, the presented concept is analogous to the gamma-concept of Low et al (1998a Med. Phys. 25 656-61) if extended to (3+1) dimensions. The pointwise dose comparisons of the reformulated concept are easier to perform and speed up the evaluation process considerably, especially for fine-grid evaluations of 3D dose distributions. The occurrences of false negative indications due to the discrete nature of the data are reduced with the method. The presented method was applied to film-measured, clinical data and compared with gamma-evaluations. 4D and 3D evaluations were performed. Comparisons prove that 4D evaluations have to be given priority, especially if complex treatment situations are verified, e.g., non-coplanar beam configurations.

a b s t r a c tBackground: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. Methods: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. Findings: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. Interpretation: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas.

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One approach to the computation of photon IMRT treatment plans is the formulation of an optimization problem with an objective function that derives from an objective density. An investigation of the second-order properties of such an objective function in a neighborhood of the minimizer opens an intuitive access to many traits of this approach. A general finding is that only a small subset of the parameter space has nonzero curvature, while the objective function is entirely flat in a neighborhood of the minimizer in most directions. The dimension of the subspace of vanishing curvature serves as a measure for the degeneracy of the solution. This finding is important both for algorithm design and evaluation of the mathematical model of clinical intuition, expressed by the objective function. The structure of the subspace of great curvature is found to be imposed on the problem by conflicts between objectives of target and critical structures. These conflicts and their corresponding modes of resolution form a common trait between all reasonable treatment plans of a given case. The high degree of degeneracy makes the use of a conjugate gradient optimization algorithm particularly favorable since the number of iterations to convergence is equivalent to the number of different eigenvalues of the curvature tensor and is hence independent from the number of optimization parameters. A high level of degeneracy of the fluence profiles implies that it should be possible to stipulate further delivery-related conditions without causing severe deterioration of the dose distribution.

The purpose of this study was to investigate beam output factors (OFs) for conformal radiation therapy and to compare the OFs measured with different detectors with those simulated with Monte Carlo methods. Four different detectors (diode, diamond, pinpoint and ionization chamber) were used to measure photon beam OFs in a water phantom at a depth of 10 cm with a source-surface distance (SSD) of 100 cm. Square fields with widths ranging from 1 cm to 15 cm were observed; the OF for the different field sizes was normalized to that measured at a 5 cm x 5 cm field size at a depth of 10 cm. The BEAM/EGS4 program was used to simulate the exact geometry of a 6 MV photon beam generated by the linear accelerator, and the DOSXYZ-code was implemented to calculate the OFs for all field sizes. Two resolutions (0.1 cm and 0.5 cm voxel size) were chosen here. In addition, to model the detector four kinds of material, water, air, graphite or silicon, were placed in the corresponding voxels. Profiles and depth dose distributions resulting from the simulation show good agreement with the measurements. Deviations of less than 2% can be observed. The OF measured with different detectors in water vary by more than 35% for 1 cm x 1 cm fields. This result can also be found for the simulated OF with different voxel sizes and materials. For field sizes of at least 2 cm x 2 cm the deviations between all measurements and simulations are below 3%. This demonstrates that very small fields have a bad effect on dosimetric accuracy and precision. Finally, Monte Carlo methods can be significant in determining the OF for small fields.

A diamond detector type 60003 (PTW Freiburg) was examined for the purpose of dosimetry with 4-20 MeV electron beams and 4-25 MV photon beams. Results were compared with those obtained by using a Markus chamber for electron beams and an ionization chamber for photon beams. Dose distributions were measured in a water phantom with the detector connected to a Unidos electrometer (PTW Freiburg). After a pre-irradiation of about 5 Gy the diamond detector shows a stability in response which is better than that of an ionization chamber. The current of the diamond detector was measured under variation of photon beam dose rate between 0.1 and 7 Gy min(-1). Different FSDs were chosen. Furthermore the pulse repetition frequency and the depth of the detector were changed. The electron beam dose rate was varied between 0.23 and 4.6 Gy min(-1) by changing the pulse-repetition frequency. The response shows no energy dependence within the covered photon-beam energy range. Between 4 MeV and 18 MeV electron beam energy it shows only a small energy dependence of about 2%, as expected from theory. For smaller electron energies the response increases significantly and an influence of the contact material used for the diamond detector can be surmised. A slight sublinearity of the current and dose rate was found. Detector current and dose rate are related by the expression i alpha Ddelta, where i is the detector current, D is the dose rate and delta is a correction factor of approximately 0.963. Depth-dose curves of photon beams, measured with the diamond detector, show a slight overestimation compared with measurements with the ionization chamber. This overestimation is compensated for by the above correction term. The superior spatial resolution of the diamond detector leads to minor deviations between depth-dose curves of electron beams measured with a Markus chamber and a diamond detector.