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Background 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. Fund The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.

Rontgen's discovery of a new type of radiation is the epochal event in a series of highlights of physics emerging within only a few decades of the late 19th century. As these discoveries are directly or indirectly rooting in the study of the phenomenon of electric discharge in gases a brief look at the physics scenario in the immediate pre-X-ray era is presented in the first section. Rather than just the fortune with the bold it is Rontgen's character as a diligent, self-critical and ingenious scientist which made his discovery possible. This will be illustrated in the sections on Rontgen's personal life and some specific details of his experiments leading to the discovery of X-rays. Finally, a short overview is given on the potential and the large variety of applications of X-rays.

Purpose Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. Methods Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. Results Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data (‘panomics’) challenges the medical physicist as member of the radiooncology team. Conclusions The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.

Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients’ characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients’ death and disease progression at 2 years. Pre-treatment and treatment models were compared. The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.

Introduction Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. Methods After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Results Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Discussion Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. Conclusion This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Positron-emission tomography (PET) with hypoxia specific tracers provides a noninvasive method to assess the tumor oxygenation status. Reaction-diffusion models have advantages in revealing the quantitative relation between in vivo imaging and the tumor microenvironment. However, there is no quantitative comparison of the simulation results with the real PET measurements yet. The lack of experimental support hampers further applications of computational simulation models. This study aims to compare the simulation results with a preclinical [18F]FMISO PET study and to optimize the reaction-diffusion model accordingly. Nude mice with xenografted human squamous cell carcinomas (CAL33) were investigated with a 2 h dynamic [18F]FMISO PET followed by immunofluorescence staining using the hypoxia marker pimonidazole and the endothelium marker CD 31. A large data pool of tumor time-activity curves (TAC) was simulated for each mouse by feeding the arterial input function (AIF) extracted from experiments into the model with different configurations of the tumor microenvironment. A measured TAC was considered to match a simulated TAC when the difference metric was below a certain, noise-dependent threshold. As an extension to the well-established Kelly model, a flow-limited oxygen-dependent (FLOD) model was developed to improve the matching between measurements and simulations. The matching rate between the simulated TACs of the Kelly model and the mouse PET data ranged from 0 to 28.1% (on average 9.8%). By modifying the Kelly model to an FLOD model, the matching rate between the simulation and the PET measurements could be improved to 41.2–84.8% (on average 64.4%). Using a simulation data pool and a matching strategy, we were able to compare the simulated temporal course of dynamic PET with in vivo measurements. By modifying the Kelly model to a FLOD model, the computational simulation was able to approach the dynamic [18F]FMISO measurements in the investigated tumors.

Cancer Medicine published by John Wiley & Sons Ltd. BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated. METHODS: One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET-PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI-based," and "FET-PET/CT-based" models, as well as combinations. Treatment features were combined with all other features. RESULTS: Of all single feature class models, the MRI-based model had the highest prediction performance on the validation set for OS (C-index: 0.61 [95% confidence interval: 0.51-0.72]) and PFS (C-index: 0.61 [0.50-0.72]). The combination of all features did increase performance above all single feature class models up to C-indices of 0.70 (0.59-0.84) and 0.68 (0.57-0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C-indices of 0.73 (0.62-0.84) and 0.71 (0.60-0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. CONCLUSIONS: MRI-based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

Image-guided radiotherapy (IGRT) has been integrated into daily clinical routine and can today be considered the standard especially with high-dose radiotherapy. Currently imaging is based on MV- or kV-CT, which has clear limitations especially in soft-tissue contrast. Thus, combination of magnetic resonance (MR) imaging and high-end radiotherapy opens a new horizon. The intricate technical properties of MR imagers pose a challenge to technology when combined with radiation technology. Several solutions that are almost ready for routine clinical application have been developed. The clinical questions include dose-escalation strategies, monitoring of changes during treatment as well as imaging without additional radiation exposure during treatment.

Purpose In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features (“radiomics”) of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment. Methods CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation. Results Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints. Conclusion This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics.

For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information. 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan–Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined. For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. “Multilocality,” “deep white-matter invasion,” “satellites,” and “ependymal invasion” were over proportionally selected for multivariate model generation, underlining their importance. We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

Medical physicists represent a valuable asset at the disposal of a structured and planned response to nuclear or radiological emergencies (NREs), especially in the hospital environment. The recognition of this fact led the International Atomic Energy Agency (IAEA) and the International Organization for Medical Physics (IOMP) to start a fruitful collaboration aiming to improve education and training of medical physicists so that they may support response efforts in case of NREs. Existing shortcomings in specific technical areas were identified through international consultations supported by the IAEA and led to the development of a project aiming at preparing a specific and standardized training package for medical physicists in support to NREs. The Project was funded through extra-budgetary contribution from Japan within the IAEA Nuclear Safety Action Plan. This paper presents the work accomplished through that project and describes the current steps and future direction for enabling medical physicists to better support response to NREs.

Positron-emission tomography (PET) with hypoxia specific tracers provides a noninvasive method to assess the tumor oxygenation status. Reaction-diffusion models have advantages in revealing the quantitative relation between in vivo imaging and the tumor microenvironment. However, there is no quantitative comparison of the simulation results with the real PET measurements yet. The lack of experimental support hampers further applications of computational simulation models. This study aims to compare the simulation results with a preclinical [

Summary Objectives: To clarify challenges and research topics for informatics in health and to describe new approaches for interdisciplinary collaboration and education. Methods: Research challenges and possible solutions were elaborated by scientists of two universities using an interdisciplinary approach, in a series of meetings over several months. Results and Conclusion: In order to translate scientific results from bench to bedside and further into an evidence-based and efficient health system, intensive collaboration is needed between experts from medicine, biology, informatics, engineering, public health, as well as social and economic sciences. Research challenges can be attributed to four areas: bioinformatics and systems biology, biomedical engineering and informatics, health informatics and individual healthcare, and public health informatics. In order to bridge existing gaps between different disciplines and cultures, we suggest focusing on interdisciplinary education, taking an integrative approach and starting interdisciplinary practice at early stages of education.* See more detailed authors´ affiliations at the end of the article.

To describe an emerging concept of high-precision radiotherapy, a modality characterized by adaptation to patient and organ movements, which might occur between fractions or even during radiation delivery. Today’s unprecedented technical capabilities to visualize the target volume and create conformal dose distributions allow for avoidance of critical structures or targeted treatment intensification within a conventionally imaged, anatomically defined tumor. The success of selective dose escalation depends on (1) correct staging and target volume identification, which can be improved by biological imaging, and (2) identification of biologically relevant subvolumes, which determine tumor control. Current efforts are directed at different methods, such as positron emission tomography and magnetic resonance spectroscopy, and integrating them into treatment planning. Early clinical trials assessing the safety and efficacy of image- and biology-guided radiotherapy are ongoing. The same modalities might be used to determine the individual tumor response during treatment and to adapt therapy. Temporal changes in tumor biology, which might represent both a challenge and a chance with regard to adaptation of treatment, need to be addressed in greater detail.

The present study evaluated a hull-volume definition strategy for the planning organ at risk volume (PRV) for the rectum in the planning of radiotherapy of prostate cancer. The bounding volumes of rectum contours of 1 to 5 CT scans were compared on the basis of the rectum coverage probabilities for 5 patients. In addition, IMRT treatment plans were optimized using the rectum hull PRV5 of 5 CTs and each of the conventional rectum contours PRV1. The plans were compared on the basis of the organ doses caused by the individual organ motion. PRV5 allowed to cover the rectum with a probability of nearly 90% (PRV1 67%). Rectal wall dose showed a great variability for PRV1, while planned and treatment dose agreed well for PRV5 due to the improved geometric information which resulted in a better rectal sparing. In conclusion, the rectum hull-volume PRV5 is a well suited PRV for planning of IMRT dose distributions allowing dose escalation as well as rectal sparing.

Background and purpose To evaluate an optimization approach where coverage probabilities are incorporated into the optimization of intensity modulated radiotherapy (IMRT) to overcome the problem of margin definition in the case of overlapping planning target volume and organs at risk. Patients and methods IMRT plans were generated for three optimization approaches: based on a planning CT plus margin (A), on prostate and rectum contours from five pre-treatment CT plus margin (B), and on coverage probabilities (C). For approach (C), the probability of organ occupation was computed for each voxel from five pre-treatment CTs and the population distribution of systematic setup error and it was used as local weight in the costfunctions. Monte Carlo simulations of treatment courses were used to compute the probability distribution of prostate and rectal wall equivalent uniform dose (EUD). Results Treatment simulations showed best and most robust results for prostate and rectal wall EUD within the population for (C). For (A) the rectal wall EUD was on average about 1.5 Gy greater than in (C), while the prostate EUD was lower than those from (C) for most of the patients for (B) (especially for those with great organ motion). Conclusions The incorporation of coverage probabilities as local weights allows for dose escalation as well as improved rectal sparing and results in a safer and more robust IMRT treatment.

Measurements as well as Monte Carlo simulations are presented to investigate the deviation between the dose to water and the value measured by an ionization chamber. These deviations are evaluated at different depths (1.5 and 10 cm) and at an off-axis position of 15 cm. It is shown that an ionization chamber can produce a measuring signal, which is up to 2.5% too low, compared to the dose, when measurements are performed at shallow depths and far off-axis. The reason for this underresponse is found in the variation of the wall correction factor. As a result of the variation of the radiation spectra with depth and position the dose to the air volume, which originates from the wall, varies and therefore changes the wall correction factor.

On occasion of its 50th anniversary, the International Organization for Medical Physics (IOMP) from now on is going to celebrate annually an International Day of Medical Physics for which the 7th November, the birthday of Marie Sklodowska Curie, a most exceptional character in science at all and a pioneer of medical physics, has been chosen. This article briefly outlines her outstanding personality, sketches her fundamental discovery of radioactivity and emphasizes the impact of her various achievements on the development of medical physics at large.

Geometric uncertainties arise during treatment planning and treatment and mean that dose-dependent parameters such as EUD are random variables with a patient specific probability distribution. Treatment planning with highly conformal treatment techniques such as intensity modulated radiation therapy requires new evaluation tools which allow us to estimate this influence of geometrical uncertainties on the probable treatment dose for a planned dose distribution. Monte Carlo simulations of treatment courses with recalculation of the dose according to the daily geometric errors are a gold standard for such an evaluation. Distribution histograms which show the relative frequency of a treatment quality parameter in the treatment simulations can be used to evaluate the potential risks and chances of a planned dose distribution. As treatment simulations with dose recalculation are very time consuming for sufficient statistical accuracy, it is proposed to do treatment simulations in the dose parameter space where the result is mainly determined by the systematic and random component of the geometrical uncertainties. Comparison of the parameter space simulation method with the gold standard for prostate cases and a head and neck case shows good agreement as long as the number of fractions is high enough and the influence of tissue inhomogeneities and surface curvature on the dose is small.

Nowadays, multileaf collimation of the treatment fields from medical linear accelerators is a common option. Due to the design of the leaf sides, the tongue and groove effect occurs for certain multileaf collimator applications such as the abutment of fields where the beam edges are defined by the sides of the leaves. In this study, the tongue and groove effect was measured for two pairs of irregular multileaf collimator fields that were matched along leaf sides in two steps. Measurements were made at 10 cm depth in a polystyrene phantom using Kodak EDR2 films for a photon beam energy of 6 MV on an Elekta Sli-plus accelerator. To verify the measurements, full Monte Carlo simulations were done. In the simulations, the design of the leaf sides was taken into account and one component module of BEAM code was modified to correctly simulate the Elekta multileaf collimator. The results of measurements and simulations are in good agreement and within the tolerance of film dosimetry.

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.

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.

An algorithm for the optimization of the direction of intensity-modulated beams is presented. Although the global optimum dose distribution cannot be predicted, usually a large number of equivalent beam configurations exists. This degeneracy facilitates beam direction optimization (BDO) through a number of possible approximations and because the target set of good beam configurations is very large. Usually, the target volume is accessible through a finite number of paths of little resistance, which are defined by the properties of the objective function and the global optimum dose distribution. Since these paths can be occupied by a finite number of beams, it is reasonable to assume that a minimum number of beams for a configuration that is degenerate to the global optimum exists. Efficiency of the BDO will be characterized by detecting this degeneracy threshold. Beam configurations are altered by adding and deleting beams. A fast exhaustive (up to 3500 non-coplanar orientations) search finds beam directions that improve a configuration. Redundant beams of a configuration can be identified by a fast criterion based on second-order derivative information of the objective function. This offers a fast means of iteratively substituting redundant beams from a configuration. Inferior stationary states can be evaded by adding more beams than the desired number to the current configuration, followed by the subsequent cancellation of superfluous beams. The significance of BDO is examined in a coplanar and a non-coplanar test case. The existence of a threshold number for the minimum configuration and its dependence on the complexity of the problem are shown. BDO outperforms manual configurations and equispaced coplanar beam arrangements in both example cases.

A smoothing algorithm based on an optimization procedure is presented and evaluated for single electron and photon beams and a full intensity modulated radiation therapy (IMRT) delivery. The algorithm iteratively reduces the statistical noise of Monte Carlo (MC) calculated dose distributions. It is called IRON (iterative reduction of noise). By varying the dose in each voxel, the algorithm minimizes the second partial derivatives of dose with respect to X, Y and Z. An additional restoration term ensures that too large dose changes are prevented. IRON requires a MC calculated one-dimensional or three-dimensional dose distribution with or without known statistical uncertainties as input. The algorithm is tested using three different treatment plan examples, a photon beam dose distribution in water, an IMRT plan of a real patient and an electron beam dose distribution in a water phantom with inhomogeneities. It is shown that smoothing can lead to an additional reduction of MC calculation time by factors of 2 to 10. This is especially useful if MC dose calculation is part of an inverse treatment planning system. In addition to this, it is shown that smoothing a noisy dose distribution may introduce some bias into the final dose values by converting the statistical uncertainty of the dose distribution into a systematic deviation of the dose value.

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.

In general, the deposition of a given target dose requires a longer radiation time for intensity-modulated photon beams (IMBs) than for unmodulated beams. Hence, the routine use of intensity-modulated radiotherapy (IMRT) has repercussions both on the exposure of the patient to scatter and institutional radiation safety. A rule of thumb is presented to assess the maximum prolongation of radiation time for a case class in an idealized setting using static superimposed field segments. The method considers only the degree to which risk structures have to be blocked to meet specified dose restrictions.

On the basis of two clinical cases, we present fluence-modulated radiotherapy with a sequencer integrated into the optimization of our treatment-planning software HYPERION. In each case, we achieved simple relations for the dependence of the total number of segments on the complexity of the sequencing, as well as for the dependence of the dose-distribution quality on the number of segments. For both clinical cases, it was possible to obtain treatment plans that complied with the clinical demands on dose distribution and number of segments. Also, compared to the widespread concept of equidistant steps, our method of sequencing with fluence steps of variable size led to a significant reduction of the number of segments, while maintaining the quality of the dose distribution. Our findings substantiate the value of the integration of the sequencer into the optimization for the clinical efficiency of IMRT.

To compare intensity-modulated treatment plans of patients with head and neck cancer generated by forward and inverse planning. Ten intensity-modulated treatment plans, planned and treated with a step&shoot technique using a forward planning approach, were retrospectively re-planned with an inverse planning algorithm. For this purpose, two strategies were applied. First, inverse planning was performed with the same beam directions as forward planning. In addition, nine equidistant, coplanar incidences were used. The main objective of the optimisation process was the sparing of the parotid glands beside an adequate treatment of the planning target volume (PTV). Inverse planning was performed both with pencil beam and Monte Carlo dose computation to investigate the influence of dose computation on the result of the optimisation. In most cases, both inverse planning strategies managed to improve the treatment plans distinctly due to a better target coverage, a better sparing of the parotid glands or both. A reduction of the mean dose by 3-11Gy for at least one of the parotid glands could be achieved for most of the patients. For three patients, inverse planning allowed to spare a parotid gland that had to be sacrificed by forward planning. Inverse planning increased the number of segments compared to forward planning by a factor of about 3; from 9-15 to 27-46. No significant differences for PTV and parotid glands between both inverse planning approaches were found. Also, the use of Monte Carlo instead of pencil beam dose computation did not influence the results significantly. The results demonstrate the potential of inverse planning to improve intensity-modulated treatment plans for head and neck cases compared to forward planning while retaining clinical utility in terms of treatment time and quality assurance

Das lokal begrenzte Prostatakarzinom kann durch interstitielle Brachytherapie mit Jod-125-Seeds erfolgreich therapiert werden. Es wird ein hierzu geeignetes klinikinternes physikalisch-technisches Qualitatssicherungsprogramm vorgestellt. Weiterhin werden die Anforderungen der neuen deutschen Strahlenschutzverordnung (StrlSchV), die am 1. August 2001 in Kraft getreten ist, berucksichtigt. Material und Methoden: Fur die Jod-125-Monotherapie der Prostata werden RAPID STRANDS® (Amersham Health, Braunschweig, Germany) verwendet. Die Ermittlung der Korperdosis des beruflich strahlenexponierten Personals wird entsprechend den Vorgaben der neuen StrlSchV nach den neuen Messgrosen Hp (10) und Hp (0,07) vorgenommen. Die Kenndosisleistung der Seeds wird mit einer kalibrierten Schachtkammer vom Typ HDR 1000 Plus und mit einem Elektrometer vom Typ MAX 4000 gemessen. Die sonographischen Schichtbilder der Prostata werden mit einem Falcon-Ultraschallgerat vom Typ 2101 (B-K Medical A/S, Danemark) aufgenommen. Fur die Bestrahlungsplanung wird das Programm VariSeed (Fa. Varian, Darmstadt, Deutschland) verwendet. Die korrekte Beladung der Nadeln wird vor Applikation autoradiographisch kontrolliert. Nach Applikation werden im OP-Raum Strahlenschutzmessungen durchgefuhrt. Ergebnisse: Beim Personal ergaben sich, bezogen auf jeweils zwei Applikationen, fur die Tiefen-Personendosis Hp (10) Messwerte zwischen 0 μSv und 14 μSv. Bei der Uberwachung der Strahlenexposition der Hande ergaben sich Oberflachen-Personendosiswerte Hp (0,07) von bis zu 1 mSv. Die Kenndosisleistungswerte der RAPID STRANDS® lagen alle im 95%-Konfidenzintervall, das vom Hersteller zugesichert wird. Die Autoradiographieaufnahmen dokumentierten – bis auf einen Fall – die korrekte Nadelbeladung. Die interstitielle transperineale Applikation der Jod-125-Seeds konnte bei allen Patienten wie geplant erfolgen. Der OP-Raum zeigte bisher bei den Strahlenschutzmessungen keine Kontamination. Schlussfolgerung: Das vorgestellte physikalisch-technishce Qualitatssicherungsprogramm deckt den physikalisch-technischen Bereich der klinikinternen Qualitatssicherung ab und lies sich problemlos in den Behandlungsablauf integrieren. Es hat folgende Vorteile: Die autoradiographische Dokumentation der korrekt beladenen Nadeln ist der Nachweis dafur, dass die Voraussetzung fur die Erzeugung der verordneten Dosisverteilung erfullt ist. Die klinikinterne Bestimmung der Kenndosisleistung ist die Grundlage zur korrekten Dosisapplikation.

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.

Durch interstitielle Brachytherapie mit Jod-125-Seeds kann das lokal begrenzte Prostatakarzinom erfolgreich therapiert werden. Es wird die Technik der transperinealen Permanentimplantation von Jod-125-Seeds mit intraoperativer Bestrahlungsplanung vorgestellt, die fur eine Therapie des Prostatakarzinoms bis zum Studium T2a geeignet ist. Material und Methode: Einige Wochen vor der Seedimplantation wird mithilfe von transrektalem Ultraschall (TRUS) das Prostatavolumen bestimmt, um die benotigte Anzal der Jod-125-Seeds abzuschatzen. Zu Beginn der Behandlung wird die Prostata durch zwei perineal gesetzte Nadeln fixiert. Anschliesend wird eine ultraschallbasierte, intraoperative Bestrahlungsplanung durchgefuhrt, mit der die Verteilung der Seeds in der Prostata optimiert wird. Zur interstitiellen Brachytherapie werden RAPID STRANDS® verwendet, d. h. die Jod-125-Seeds sind in einem versteiften Vicrylgewebe in 1-cm-Abstanden eingebettet. Bei der Applikation der Jod-125-Seeds wird die transversale Positionierung der Applikationsnadeln mit TRUS und die kraniokaudale Positionierung der Applikationsnadeln mithilfe einer Durchleuchtungseinheit und TRUS uberwacht. Ungefahr 4 Wochen nach der Seedimplantation wird mithilfe von CT-Schichtbildern eine postoperative Berechnung der Dosisverteilung der applizierten Seeds durchgefuhrt. Ergebnisse: Das Verfahren hat den Vorteil, dass Ultraschallbildaufnahme, Bestrahlungsplanung und Seedapplikation bei unveranderter Prostatalage durchgefuhrt werden. Bei der Applikation ermoglicht die kombinierte Bildgebung aus TRUS und Durchleuchtung eine sichere Platzierung der Seeds in der Prostata. Schlussfolgerung: Die Methoden zur Berechnung der tatsachlich erreichten Dosisverteilung mussen noch optimiert werden, da die postoperative Uberprufung des individuellen Ergebnisses bisher nur mit methodisch bedingten Schwierigkeiten moglich ist.

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.

Konformationstechniken, wie sie heutzutage routinemasig fur Patienten mit Prostatakarzinom angewandt werden, erlauben, durch Adaption der Bestrahlungsfelder an das Planungszielvolumen (PTV) umliegendes Normalgewebe zu schonen. Um die Vorteile dieser Techniken jedoch vollstandig nutzen zu konnen, ist die korrekte Patientenpositionierung eine wichtige Voraussetzung. In dieser Studie wurde mittels Portal-Imaging (EPID) untersucht, welche Positionierungsgenauigkeiten im Beckenbereich unter Verwendung unterschiedlicher Lagerungshilfen moglich sind.

The present paper introduces a concept for the description of treatment objectives of IMRT which emphasizes the assurance of an acceptable dose distribution in risk organs. A number of DVH manipulation tools are available to take into account both the volume effects of normal tissue and the influence of dose fractionation. The optimization of the dose distribution strictly obeys the prescribed risks of complications, as well as the limits of dose homogeneity in the target volume. The application of IMRT is made more efficient by limiting the modulation of the fluence profiles. The use of this algorithm could simplify IMRT in a way that a larger number of patients can profit from this type of treatment.

106Ru-Augenapplikatoren der Fa. Bebig (Berlin) werden zur strahlentherapeutischen Behandlung von intraokularen Tumoren eingesetzt. Die Applikatoren werden sklerakontaktierend am Bulbus fixiert und nach mehreren Tagen wieder entfernt. Durch eine klinikinterne Eingangsprufung wurden folgende therapierelevante Mangel bei den Augenapplikatoren entdeckt: mangelhafte Dichtheit und inkonsistente Dosisleistungsangaben des Herstellers. Ein Teil dieser klinikinternen Eingangsprufung wurde inzwischen vom Hersteller der 106Ru-Augenapplikatoren ubernommen und erfolgreich eingesetzt.

A step and shoot sequencer was developed that can be integrated into an IMRT optimization algorithm. The method uses non-uniform fluence steps and is adopted to the constraints of an MLC. It consists of a clustering, a smoothing and a segmentation routine. The performance of the algorithm is demonstrated for eight mathematical profiles of differing complexity and two optimized profiles of a clinical prostate case. The results in terms of stability, flexibility, speed and conformity fulfil the criteria for the integration into the optimization concept. The performance of the clustering routine is compared with another previously published one (Bortfeld et al 1994 Int. J. Radiat. Oncol. Biol. Ph.vs. 28 723-30) and yields slightly better results in terms of mean and maximum deviation between the optimized and the clustered protile. We discuss the specific attributes of the algorithm concerning its integration into the optimization concept.

The present study investigates the application of compensators for the intensity modulated irradiation of a thorax phantom. Measurements are compared with Monte Carlo and standard pencil beam algorithm dose calculations. Compensators were manufactured to produce the intensity profiles that were generated from the scientific version of the KonRad IMRT treatment-planning system for a given treatment plan. The comparison of dose distributions calculated with a pencil beam algorithm, with the Monte Carlo code EGS4 and with measurements is presented. By measurements in a water phantom it is demonstrated that the method used to manufacture the compensators reproduces the intensity profiles in a suitable manner. Monte Carlo simulations in a water phantom show that the accelerator head model used for simulations is sufficient. No significant overestimations of dose values inside the target volume by the pencil beam algorithm are found in the thorax phantom. An overestimation of dose values in lung by the pencil beam algorithm is also not found. Expected dose calculation errors of the pencil beam algorithm are suppressed, because the dose to the low density region lung is reduced by the use of a non-coplanar beam arrangement and by intensity modulation.

Monte-Carlo-Dosisberechnungsverfahren fur klinische Photonen- und Elektronenstrahlung besitzen das Potential, die Dosis mit einer Genauigkeit von besser als ±2% vorhersagen zu konnen. Um diese Prazision zu erreichen, ist jedoch die Kenntnis der Wechselwirkungseigenschaften in jedem Volumenelement essenziell.

Intensity modulated radiotherapy (IMRT) allows dose distributions which adequately consider organs at risk (OAR) and dose homogeneity to the target volume. This is practically reached by conforming the beam profiles to the shape of the planning target volume (PTV), by shaping the fluence with multileaf collimators (MLC) or compensators. Though compensator production is time consuming and seems less convenient than the use of MLC, compensators offer much easier quality assurance. In this study the effects of certain simplifications of compensator production were studied. Compensators were produced and ionization chamber measurements in a water phantom and film measurements in a solid phantom were performed to verify the compensators. The results of the measurements were compared to the fluence distributions given by the planning system. The measurements were meant to show how realistic the investigated simplifications were, and to reveal a suitable and reliable testing method for compensators. Monte-Carlo calculations employing the EGS 4 Code were further performed to support the measurements.

Zusammenfassung Ein Vergleich des kurzlich entwickelten XVMC-Programms, ein schneller Monte-Carlo-Algorithmus (MC) zur Dosisberechnung von Photonen- und Elektronenfeldern in der Strahlentherapieplanung, mit EGSnrc wird vorgestellt. EGSnrc ist eine verbesserte Version des bekannten EGS4-Systems, welches aufgrund der zahlreichen und genauen Verifikationsmessungen als goldener Standard betrachtet werden kann. Der Vergleich erfolgte anhand von Phantomen, die aus Wasser, Lungengewebe und Knochen bestehen. Innerhalb der Genauigkeitsanforderungen zeigen Dosisprofile und Differenzverteilungen gute Ubereinstimmung. Da Abweichungen zwischen den Resultaten zweier MC-Algorithmen zum einen systematische Fehler und zum anderen statistische Fluktuationen als Ursache haben, ist eine Separationsmethode eingesetzt worden, mit der sich die systematischen Diskrepanzen quantifizieren lassen. Es wurde gezeigt, das mit dieser Methode, die durch XVMC und EGSnrc berechneten dreidimensionalen Dosisverteilungen ubereinstimmen, wenn man systematische Abweichungen von maximal 2 % zulast.

A model for the simulation of the accelerator heads of two identical linear accelerators was designed at the University Hospital of Tubingen, using the BEAM program developed at the National Research Council of Canada. Both linear accelerators are equipped with multileaf collimators (MLCs) and backup jaws (y-direction) with curved leaf-ends. The accelerator models were divided into two parts. The first part consisted of target, primary collimator, flattening filter, monitor chamber, and mirror. After the Monte Carlo simulation of these parts, the phase-space characteristics below the mirror were stored in a file and used as source for the second part of the accelerator head (jaw, MLC). The electron source was assumed to deliver a gaussian energy spectrum, with parallel direction to the beam axis. With this electron source, there was good agreement between the measured and simulated depth dose curves in water, with difference < 2%. A new module was created for the BEAM program to simulate backup jaws, while the standard MLCQ module from BEAM was used to simulate a MLC with curved leaf-ends. As a result, MLCs and backup jaws with curved leaf-ends make the shoulder of the y-profile higher than the straight-end MLCs.