Fysica in de marge!

Mijn leeropdracht is Klinische Fysica in de Radiotherapie. Ik doe dus onderzoek aan bestralingstechnieken voor in hoofdzaak kankerpatiënten. Radiotherapie is verre van een marginale geneeskundige discipline. Exclusief de relatief onschuldige vormen van huidkanker wordt per jaar in Nederland bij ca. 50.000 mensen de diagnose kanker gesteld en ongeveer de helft van deze patiënten wordt behandeld met radiotherapie. Op dit moment kan zo’n 50% van de kankerpatiënten genezen worden en maar liefst bijna de helft van al die genezen patiënten, nl. 24 van de 50 %, is bestraald. 14% heeft enkel radiotherapie gehad en de overige 10% is naast de bestralingsbehandeling ook geopereerd (Bron: Gezondheidsraad - ontwerp-planningsbesluit radiotherapie 2000). Het is mijn overtuiging dat door wetenschappelijk onderzoek bestralingen steeds effectiever zullen worden. Tevens zullen de vaak ernstige bijwerkingen substantieel kunnen worden verminderd. Ongetwijfeld zal dit bijdragen aan een verhoogde kans op genezing en een betere kwaliteit van leven. In de rest van dit verhaal zal ik aantonen dat de Klinische Fysica daar in belangrijke mate aan zal kunnen bijdragen.Rede, Ondersteund door bewegende beelden uitgesproken bij de aanvaarding van het ambt van bijzonder hoogleraar met als leeropdracht Klinische Fysica in de Radiotherapie aan het Erasmus MC, faculteit van de Erasmus Universiteit Rotterdam, op 23 juni 200

Per-fraction planning to enhance optimization degrees of freedom compared to the conventional single-plan approach

Objective. In conventional radiotherapy, a single treatment plan is generated pre-treatment, and delivered in daily fractions. In this study, we propose to generate different treatment plans for all fractions (‘Per-fraction’ planning) to reduce cumulative organs at risk (OAR) doses. Per-fraction planning was compared to the ‘Conventional’ single-plan approach for non-coplanar 4 × 9.5 Gy prostate stereotactic body radiation therapy (SBRT). Approach. An in-house application for fully automated, non-coplanar multi-criterial treatment planning with integrated beam angle and fluence optimization was used for plan generations. For the Conventional approach, a single 12-beam non-coplanar IMRT plan with individualized beam angles was generated for each of the 20 included patients. In Per-fraction planning, four fraction plans were generated for each patient. For each fraction, a different set of patient-specific 12-beam configurations could be automatically selected. Per-fraction plans were sequentially generated by adding dose to already generated fraction plan(s). For each fraction, the cumulative- and fraction dose were simultaneously optimized, allowing some minor constraint violations in fraction doses, but not in cumulative. Main results. In the Per-fraction approach, on average 32.9 ± 3.1 [29;39] unique beams per patient were used. PTV doses in the separate Per-fraction plans were acceptable and highly similar to those in Conventional plans, while also fulfilling all OAR hard constraints. When comparing total cumulative doses, Per-fraction planning showed improved bladder sparing for all patients with reductions in Dmean of 22.6% (p = 0.0001) and in D1cc of 2.0% (p = 0.0001), reductions in patient volumes receiving 30% and 50% of the prescribed dose of 54.7% and 6.3%, respectively, and a 3.1% lower rectum Dmean (p = 0.007). Rectum D1cc was 4.1% higher (p = 0.0001) and Urethra dose was similar. Significance. In this proof-of-concept paper, Per-fraction planning resulted in several dose improvements in healthy tissues compared to the Conventional single-plan approach, for similar PTV dose. By keeping the number of beams per fraction the same as in Conventional planning, reported dosimetric improvements could be obtained without increase in fraction durations. Further research is needed to explore the full potential of the Per-fraction planning approach.</p

Data for TROTS – The Radiotherapy Optimisation Test Set

The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset

advanced treatment planning strategies to enhance quality and efficiency of radiotherapy

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TROTS - The Radiotherapy Optimisation Test Set

The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 145 problems (patients), divided over 7 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset. This record is the main landing page for links to the TROTS dataset. Links to the project page, updates and newer versions can be found here, and will be updated if necessary

Automatic configuration of the reference point method for fully automated multi-objective treatment planning applied to oropharyngeal cancer

Purpose: In automated treatment planning, configuration of the underlying algorithm to generate high-quality plans for all patients of a particular tumor type can be a major challenge. Often, a time-consuming trial-and-error tuning procedure is required. The purpose of this paper is to automatically configure an automated treatment planning algorithm for oropharyngeal cancer patients. Methods: Recently, we proposed a new procedure to automatically configure the reference point method (RPM), a fast automatic multi-objective treatment planning algorithm. With a well-tuned configuration, the RPM generates a single Pareto optimal treatment plan with clinically favorable trade-offs for each patient. The automatic configuration of the RPM requires a set of computed tomography (CT) scans with corresponding dose distributions for training. Previously, we demonstrated for prostate cancer planning with 12 objectives th

Coping with interfraction time trends in tumor setup

Purpose: Interfraction tumor setup variations in radiotherapy are often reduced with image guidance procedures. Clinical target volume (CTV)–planning target volume (PTV) margins are then used to deal with residual errors. We have investigated characterization of setup errors in patient populations with explicit modelling of occurring interfraction time trends. Methods: The core of a “trendline characterization” of observed setup errors in a population is a distribution of trendlines, each obtained by fitting a straight line through a patient's daily setup errors. Random errors are defined as daily deviations from the trendline. Monte Carlo simulations were performed to predict the impact of offline setup correction protocols on residual setup errors in patient populations with time trends. A novel CTV-PTV margin recipe was derived that assumes that systematic underdosing of tumor edges in multiple consecutive fractions, as caused by trend motion, should preferentially be avoided. Similar to the well-known approach by van Herk et al. for conventional error characterization (no explicit modelling of trends), only a predefined percentage of patients (generally 10%) was allowed to have nonrandom (systematic + trend) setup errors outside the margin. Additionally, a method was proposed to avoid erroneous results in Monte Carlo simulations with setup errors, related to decoupling of error sources in characterizations. The investigations were based on a database of daily measured setup errors in 835 prostate cancer patients that were treated with 39 fractions, and on Monte Carlo–generated patient populations with time trends. Results: With conventional characterization of setup errors in patient populations with time trends, predicted standard deviations of residual systematic errors ((∑res)) after application of an offline correction protocol could be underestimated by more than 50%, potentially res

Shortening delivery times of intensity modulated proton therapy by reducing proton energy layers during treatment plan optimization

Purpose To shorten delivery times of intensity modulated proton therapy by reducing the number of energy layers in the treatment plan. Methods and Materials We have developed an energy layer reduction method, which was implemented into our in-house-developed multicriteria treatment planning system "Erasmus-iCycle." The method consisted of 2 components: (1) minimizing the logarithm of the total spot weight per energy layer; and (2) iteratively excluding low-weighted energy layers. The method was benchmarked by comparing a robust "time-efficient plan" (with energy layer reduction) with a robust "standard clinical plan" (without energy layer reduction) for 5 oropharyngeal cases and 5 prostate cases. Both plans of each patient had equal robust plan quality, because the worst-case dose parameters of the standard clinical plan were used as dose constraints for the time-efficient plan. Worst-case robust optimization was performed, accounting for setup errors of 3 mm and range errors of 3% + 1 mm. We evaluated the number of energy layers and the expected delivery time per fraction, assuming 30 seconds per beam direction, 10 ms per spot, and 400 Giga-protons per minute. The energy switching time was varied from 0.1 to 5 seconds. Results The number of energy layers was on average reduced by 45% (range, 30%-56%) for the oropharyngeal cases and by 28% (range, 25%-32%) for the prostate cases. When assuming 1, 2, or 5 seconds energy switching time, the average delivery time was shortened from 3.9 to 3.0 minutes (25%), 6.0 to 4.2 minutes (32%), or 12.3 to 7.7 minutes (38%) for the oropharyngeal cases, and from 3.4 to 2.9 minutes (16%), 5.2 to 4.2 minutes (20%), or 10.6 to 8.0 minutes (24%) for the prostate cases. Conclusions Delivery times of intensity modulated proton therapy can be reduced substantially without compromising robust plan quality. Shorter delivery times are likely to reduce treatment uncertainties and costs

Local Dose Effects for Late Gastrointestinal Toxicity After Hypofractionated and Conventionally Fractionated Modern Radiotherapy for Prostate Cancer in the HYPRO Trial

Purpose: Late gastrointestinal (GI) toxicity after radiotherapy for prostate cancer may have significant impact on the cancer survivor's quality of life. To da