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BACKGROUND AND PURPOSE: To update the digital online atlas for organs at risk (OARs) delineation in neuro-oncology based on high-quality computed tomography (CT) and magnetic resonance (MR) imaging with new OARs. MATERIALS AND METHODS: In this planned update of the neurological contouring atlas published in 2018, ten new clinically relevant OARs were included, after thorough discussion between experienced neuro-radiation oncologists (RTOs) representing 30 European radiotherapy-oncology institutes. Inclusion was based on daily practice and research requirements. Consensus was reached for the delineation after critical review. Contouring was performed on registered CT with intravenous (IV) contrast (soft tissue & bone window setting) and 3 Tesla (T) MRI (T1 with gadolinium & T2 FLAIR) images of one patient (1 mm slices). For illustration purposes, delineation on a 7 T MRI without IV contrast from a healthy volunteer was added. OARs were delineated by three experienced RTOs and a neuroradiologist based on the relevant literature. RESULTS: The presented update of the neurological contouring atlas was reviewed and approved by 28 experts in the field. The atlas is available online and includes in total 25 OARs relevant to neuro-oncology, contoured on CT and MRI T1 and FLAIR (3 T & 7 T). Three-dimensional (3D) rendered films are also available online. CONCLUSION: In order to further decrease inter- and intra-observer OAR delineation variability in the field of neuro-oncology, we propose the use of this contouring atlas in photon and particle therapy, in clinical practice and in the research setting. The updated atlas is freely available on www.cancerdata.org. ispartof: RADIOTHERAPY AND ONCOLOGY vol:160 pages:259-265 ispartof: location:Ireland status: published

This study reports on an Evidence Centered Design (ECD) project in the Netherlands, involving the theory exam for prospective car drivers. In particular, we illustrate how cognitive load theory, ta...

PURPOSE: Dose escalation yields higher complete response to rectal tumors, which may enable the omission of surgery. Dose escalation using 50 kVp contact x-ray brachytherapy (CXB) allow the treatment of a selective volume, resulting in low toxicity and organs-at-risk preservation. However, the use of CXB devices is limited because of its high cost and lack of treatment planning tools. Hence, the MAASTRO applicator (for HDR Ir-192 sources) was developed and characterized by measurements and Monte Carlo simulations to be a cost-effective alternative to CXB devices.METHODS AND MATERIALS: A cylindrical applicator with lateral shielding was designed to be used with a rectoscope using its tip as treatment surface. Both the applicator and the rectoscope have a slanted edge to potentially allow easier placement against tumors. The applicator design was achieved by Monte Carlo modeling and validated experimentally with film dosimetry, using the Papillon 50 (P50) device as reference.RESULTS: The applicator delivers CXB doses in less than 9 min using a 20375 U source for a treatment area of approximately 20 x 20 mm(2) at 2 mm depth. Normalized at 2 mm, the dose falloff for depths of 0 mm, 5 mm, and 10 mm are 130%, 70%, and 43% for the P50 and 140%, 67%, and 38% for the MAASTRO applicator, respectively.CONCLUSIONS: The MAASTRO applicator was designed to use HDR Ir-192 sources to deliver a dose distribution similar to those of CXB devices. The applicator may provide a cost-effective solution for endoluminal boosting with clinical treatment planning system integration. (C) 2020 Published by Elsevier Inc. on behalf of American Brachytherapy Society.

Background and Purpose: To investigate the impact of organ motion on hypoxia-guided proton therapy treatments for non-small cell lung cancer (NSCLC) patients. Materials and Methods: Hypoxia PET and 4D imaging data of six NSCLC patients were used to simulate hypoxia-guided proton therapy with different motion mitigation strategies including rescanning, breath-hold, respiratory gating and tumour tracking. Motion-induced dose degradation was estimated for treatment plans with dose painting of hypoxic tumour sub-volumes at escalated dose levels. Tumour control probability (TCP) and dosimetry indices were assessed to weigh the clinical benefit of dose escalation and motion mitigation. In addition, the difference in normal tissue complication probability (NTCP) between escalated proton and photon VMAT treatments has been assessed. Results: Motion-induced dose degradation was found for target coverage (CTV V95% up to −4%) and quality of the dose-escalation-by-contour (QRMS up to 6%) as a function of motion amplitude and amount of dose escalation. The TCP benefit coming from dose escalation (+4–13%) outweighs the motion-induced losses (, Radiotherapy & Oncology, 176, ISSN:0167-8140, ISSN:1879-0887

Introduction In the Netherlands, young cyclists are extremely vulnerable in traffic, which may partly be due to their still underdeveloped higher-order cycling skill. So far, knowledge on their actual level of skill is lacking. Using a computerized test battery mimicking real-life risky traffic conditions, this study assessed the level of higher-order cycling skill in children 11 and 12 years of age and tested the hypothesis that these skills show caveats. Furthermore, factors potentially influencing the development and impact of these skills were studied, such as cycling experience, risky road behavior, crash involvement, and self-assessed skill. Method A total of 335 students (49% female) completed computerized tests on hazard perception, gap acceptance, blind spot strategies, and priority decisions in traffic, and completed questionnaires on cycling experience, risky cycling behavior, crashes, and self-assessment of cycling skill. Results On the hazard perception test, one-third of the participants missed at least half of the number of hazards. They made errors in about 50% of the priority decisions, accepted critical gaps when crossing the road, and conversely rejected safe gaps; only 1% of the participants identified all blind spots of a truck correctly, while 69% made unsafe decisions when interacting with trucks in traffic scenarios. Overall, in complex traffic situations performance was worse than in simple ones. The hypothesis of lack of skills was therefore accepted. However, the study failed to demonstrate consistent relationships between subtest performance and cycling experience, risky behavior, crashes, and self-assessed skill, which weakens the theoretical assumptions concerning the subtests. Conclusions The results suggest that children at the end of primary school are still lacking elementary skills for safe cycling, calling for measures to accelerate skill development. Practical applications Test batteries are essential tools for systematically monitoring skill development in cyclists, evaluating education programs, and for guiding the development of effective road safety education. The next step is the validation of such batteries.

Highlights • There is a dosimetric advantage for IMPT over VMAT in patients receiving radiation treatment for pilocytic astrocytoma. • The novel RPSS is a practical scoring system making translation of dose differences into clinically relevant endpoints possible. • Following the lower RPSS toxicity scores for IMPT, less toxicity is likely to be expected in patients treated with IMPT vs VMAT in this pilocytic astrocytoma cohort., Background and purpose Proton therapy is expected to outperform photon-based treatment regarding organs at risk (OAR) sparing but to date there is no method to practically measure clinical benefit. Here, we introduce the novel ROCOCO Performance Scoring System (RPSS) translating dose differences into clinically relevant endpoints and apply this to a treatment plan comparison of volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT) in 20 pilocytic astrocytoma patients. Material and methods The RPSS was developed on the basis of expert-based weighting factors and toxicity scores per OAR. The imaging datasets of 20 pilocytic astrocytoma patients having undergone radiotherapy were included in this in silico dosimetric comparison trial as proof of principle. For each of these patients, treatment plans to a total dose of 54 Gy (RBE) were generated for VMAT and IMPT and these were compared regarding radiation dose to the clinical target volume (CTV) and OARs. The RPSS was calculated for each treatment plan comparing VMAT and IMPT. Results In 40 analysed treatment plans, the average and low dose volumes to various OARs were significantly reduced when using IMPT compared to VMAT (p

In a secondary data analysis based on data from a Dutch national science assessment in the fall of 2015, the added value of dynamic testing for determining research skills in the domain of Nature & Technology of grade 6 students in elementary school was examined compared to static testing. To this end, the research data (n=203) were used from one practical assignment, designed according to the principles of dynamic testing and involved the design and implementation of an experiment and a follow-up experiment with a ramp task, the Marble Run. Depending on progress on the subtasks, reading, process, and substantive help was provided, following a protocol of graduated prompts. Analyses aimed at determining the progress made in experiment 2 after supported participation in experiment 1 revealed groups with different learning potential. The largest progression observed was made by students who did not reach a solution to a subtask in experiment 1 even after all support, but succeeded independently in doing so in experiment 2. The relative proportion of nonsolvers in experiment 1 who made this progression was 36% setting the experimental variables 42% in repeated measurement and 56% drawing conclusions. Latent class analyses resulted in three groups, each of which went through a different history of support. The biggest difference concerned responsiveness to support with a crucial sub-skill for experimentation: control-of-variables. Furthermore, in one group, giving reading/ process support appeared to play an important part in finding solutions. The group of students who benefited the least from help appeared to have less knowledge of N&T and a more negative view of their own research skills.

OBJECTIVE: Locally recurrent disease is of increasing concern in (non-)small cell lung cancer [(N)SCLC] patients. Local reirradiation with photons or particles may be of benefit to these patients. In this multicentre in silico trial performed within the Radiation Oncology Collaborative Comparison (ROCOCO) consortium, the doses to the target volumes and organs at risk (OARs) were compared when using several photon and proton techniques in patients with recurrent localised lung cancer scheduled to undergo reirradiation. METHODS: 24 consecutive patients with a second primary (N)SCLC or recurrent disease after curative-intent, standard fractionated radio(chemo)therapy were included in this study. The target volumes and OARs were centrally contoured and distributed to the participating ROCOCO sites. Remaining doses to the OARs were calculated on an individual patient’s basis. Treatment planning was performed by the participating site using the clinical treatment planning system and associated beam characteristics. RESULTS: Treatment plans for all modalities (five photon and two proton plans per patient) were available for 22 patients (N = 154 plans). 3D-conformal photon therapy and double-scattered proton therapy delivered significantly lower doses to the target volumes. The highly conformal techniques, i.e., intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), CyberKnife, TomoTherapy and intensity-modulated proton therapy (IMPT), reached the highest doses in the target volumes. Of these, IMPT was able to statistically significantly decrease the radiation doses to the OARs. CONCLUSION: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. They, however, significantly differ in the dose deposited in the OARs. The therapeutic options, i.e., reirradiation or systemic therapy, need to be carefully weighed and discussed with the patients. ADVANCES IN KNOWLEDGE: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. In light of the abilities of the various highly conformal techniques to spare specific OARs, the therapeutic options need to be carefully weighed and patients included in the decision-making process.

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multifaceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine. (C) 2016 Published by Elsevier B.V.

Dose escalation yields higher complete response to rectal tumors, which may enable the omission of surgery. Dose escalation using 50 kVp contact x-ray brachytherapy (CXB) allow the treatment of a selective volume, resulting in low toxicity and organs-at-risk preservation. However, the use of CXB devices is limited because of its high cost and lack of treatment planning tools. Hence, the MAASTRO applicator (for HDRA cylindrical applicator with lateral shielding was designed to be used with a rectoscope using its tip as treatment surface. Both the applicator and the rectoscope have a slanted edge to potentially allow easier placement against tumors. The applicator design was achieved by Monte Carlo modeling and validated experimentally with film dosimetry, using the Papillon 50 (P50) device as reference.The applicator delivers CXB doses in less than 9 min using a 20375 U source for a treatment area of approximately 20 × 20 mmThe MAASTRO applicator was designed to use HDR

A possible path towards preserving and remediating driving skills while aging is driver training. Previous studies have yielded mixed results with respect to various types of interventions, such as classroom-based training, on-road driving classes and functional abilities training. The present study-incorporated training features found to be effective in previous older driver training studies, into both a computer-based and driving simulator-based intervention. The purpose of the present study was to investigate the effect of both training formats on general driving ability and specific aspects of driving in older drivers at risk of reduced driving abilities. Additionally, the effect of training on perceived task demand was studied, and learning capacity was considered as an influencing factor on training effectiveness. A total of 31 older drivers were randomly assigned to three groups: driving simulator-based training, computer-based training, and an active control group. The participants completed a cognitive assessment including evaluation of learning capacity and a self-assessment of task demand in various traffic situations. Additionally, participants took a driving simulator assessment. Knowledge of road signs, general driving and specific aspects of driving (i.e., average speed, response time to hazards) improved with training, although improvement was found to be evenly strong in all groups. Learning capacity did not influence training effectiveness, and no difference was found in perceived task demand before and after training. The proposed methodology to evaluate training effectiveness, focusing both on clinically relevant and detailed transfer effects can serve as an example for further studies in the field of driver training.

In constructing tests it is vital that sources of construct irrelevant variance are minimized, in order to enable valid interpretations about the test taker. One important source of construct irrelevance is inaccessibility to the test and its items. In this chapter a framework is presented for design and review of test items, or more broadly, assessments tasks, to ensure their accessibility. An application is presented in the context of theory exams to obtain a drivers’ license in the Netherlands.

Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1–3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4). Our existing clinical practice generates discrete, quantitative, and structured patient-specific data (eg, images, doses, and volumes) that position us well to exploit and participate in big data initiatives. The well-established electronic infrastructure within radiation oncology should facilitate the retrieval and aggregation of much of the needed data. With additional efforts to integrate structured data collection of patient outcomes and assessments into the clinical workflow, the field of radiation oncology has a tremendous opportunity to generate large, comprehensive patient-specific data sets (5). However, there are major challenges to realizing this goal. For example, existing data are presently housed across different platforms at multiple institutions and are often not stored in a standardized manner or with common terminologies to enable pooling of data. In addition, many important data elements are not routinely discretely captured in clinical practice. There are cultural, structural, and logistical challenges (eg, computer compatibility and workflow demands) that will make the dream of big data research difficult. The big data research workshop provided a forum for leaders in cancer registries, incident report quality-assurance systems, radiogenomics, ontology of oncology, and a wide range of ongoing big data and cloud computing development projects to interact with peers in radiation oncology to develop strategies to harness data for research, quality assessment, and clinical care. The workshop provided a platform to discuss items such as data capture, data infrastructure, and protection of patient confidentiality and to improve awareness of the wide-ranging opportunities in radiation oncology, as well as to enhance the potential for research and collaboration opportunities with NIH on big data initiatives. The goals of the workshop were as follows: To discuss current and future sources of big data for use in radiation oncology research, To identify ways to improve our current data collection methods by adopting new strategies used in fields outside of radiation oncology, and To consider what new knowledge and solutions big data research can provide for clinical decision support for personalized medicine.

To quantitatively assess the effectiveness of proton therapy for individual patients, we developed a prototype for an online platform for proton decision support (PRODECIS) comparing photon and proton treatments on dose metric, toxicity and cost-effectiveness levels. An evaluation was performed with 23 head and neck cancer datasets. (C) 2016 The Authors. Published by Elsevier Ireland Ltd.

Purpose: A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate-risk prostate cancer. Extending the TCP model for low and high-risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon-ions are becoming increasingly available. Consequently, there is a need for a complete TCP model. Methods: A TCP model was fitted and validated to a primary endpoint of 5-year biological no evidence of disease clinical outcome data obtained from a review of the literature for low, intermediate, and high-risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon-ions. The review followed the preferred reporting item for systematic reviews and meta-analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied. Results: The TCP model achieves a good level of fit overall, linear regression results in a p-value of

Purpose: To compare dose to organs at risk (OARs) and dose-escalation possibility for 24 stage I non-small cell lung cancer (NSCLC) patients in a ROCOCO (Radiation Oncology Collaborative Comparison) trial. Methods: For each patient, 3 photon plans [Intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT) and CyberKnife], a double scattered proton (DSP) and an intensity-modulated carbon-ion (IMIT) therapy plan were created. Dose prescription was 60 Gy (equivalent) in 8 fractions. Results: The mean dose and dose to 2% of the clinical target volume (CTV) were lower for protons and ions compared with IMRT (p < 0.01). Doses to the lungs, heart, and mediastinal structures were lowest with IMIT (p < 0.01), doses to the spinal cord were lowest with DSP (p < 0.01). VMAT and CyberKnife allowed for reduced doses to most OARs compared with IMRT. Dose escalation was possible for 8 patients. Generally, the mediastinum was the primary dose-limiting organ. Conclusion: On average, the doses to the OARs were lowest using particles, with more homogenous CTV doses. Given the ability of VMAT and CyberKnife to limit doses to OARs compared with IMRT, the additional benefit of particles may only be clinically relevant in selected patients and thus should be carefully weighed for every individual patient. (C) 2018 Elsevier B.V. All rights reserved.

We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a 'proof-of-concept' methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

Eekers et al. have recently proposed a neuro-oncology atlas, which was co-authored by most centers associated in the European Proton Therapy Network (EPTN; Figure 1). With the introduction of new treatment techniques, such as integrated magnetic resonance imaging and linear accelerators (MR-linac) or particle therapy, the prediction of clinical efficacy of these more costly treatment modalities becomes more relevant. One of the side-effects of brain irradiation, being cognitive decline, is one of the toxicities most difficult to measure and predict. In order to validly compare different treatment modalities, 1) a uniform nomenclature of the organs at risk (OARs), 2) uniform atlas-based delineation [e.g., Eekers et al.], 3) long-term follow-up data with standardized cognitive tests, 4) a large patient population, and 5) (thus derived) validated normal tissue complication probability (NTCP) models are mandatory. Apart from the Gondi model, in which the role of the dose to 40% of both hippocampi (HC) proves to be significantly related to cognition in 18 patients, no similar models are available. So there is a strong need for more NTCP models, on HC, brain tissue and possible other relevant brain structures. In this review we summarize the available evidence on the role of the posterior cerebellum as a possible new organ at risk for cognition, which is deemed relevant for irradiation of brain and head and neck tumors. (C) 2017 The Authors. Published by Elsevier Ireland Ltd on behalf of European Society for Radiotherapy and Oncology.

The current increasing amount of digitalized medical data in healthcare demands for solutions to store, share, mine, and analyze these data. Today, medical knowledge and evidence is based on outdated data. Tomorrow we aim to have a rapid learning healthcare (RLHC) system in which evidence can be generated instantly, based on the most recent data available. The development of this system requires dedication and support of healthcare providers, politicians, and patients on many levels. The aims of this system are improvement of healthcare quality and support in clinical decision making. Full integration of data handling systems within the clinic and between institutes is inevitable in the near future.

Background. Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.Aim. The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm.Discussion. Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

As part of the Dutch post-license young driver coaching program, Drive Xperience (DX), the level of moral reasoning was explored in relation to self-reported violating driving behaviors. Drawing from literature in the field of juvenile crime, three online assessments were developed to measure: a) social driving behavior; b) moral justification for rule compliance, and: c) cognitive distortions in relation to socially undesirable driving behavior. The assessments were administered between fall 2014 and fall 2016 to1660 participants in the DXprogram. The results show that immature levels of moral reasoning and prevalence of cognitive distortions are strongly associated with self-reported speed choice, space competition and traffic law violations.

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Maintaining independent mobility and the ability to drive contributesgreatly to the quality of life of elderly. However, decline of functional abilities,such as cognitive decline, can reduce elderly’s fitness to drive and pose a risk fortraffic safety. The current study aims to compose a test battery of functionalability tests to predict fitness to drive in elderly with subjective cognitive declinein the doctor’s office. Functional ability tests were used as predictors for on-roaddriving assessment outcome. Passing or failing the on-road test was best predictedby a battery of tests addressing visual and motor ability and knowledge factors.This selection of tests can be administered in an office setting within a reasonabletime frame and allows medical professionals to provide patients with well-arguedadvice concerning their fitness to drive. Further research is needed to improvediagnostic accuracy of off-road fitness to drive screenings for elderly drivers.

Background and purpose: To apply target probabilistic planning (TPP) approach to intensity modulated radiotherapy (IMRT) plans for head and neck cancer (HNC) patients.Material and methods: Twenty plans of HNC patients were re-planned replacing the simultaneous integrated boost IMRT optimization objectives for minimum dose on the boost target and the elective volumes with research probabilistic objectives: the latter allow for explicit handling of systematic and random geometric uncertainties, enabling confidence level based probabilistic treatment planning. Monte-Carlo evaluations of geometrical errors were performed, with endpoints D98%, D2% and Dmean, calculated at a confidence level of 90%. The dose distribution was expanded outside the patient to prevent large bilateral elective treatment volumes ending up in air for probabilistic shifts.Results: TPP resulted in more regular isodoses and in reduced dose, on average, to organs at risk (OAR), up to more than 6 Gy, while maintaining target coverage and keeping the maximum dose to limiting structures within requirements. In particular, when the surrounding OARs overlap with the planning target volume (PTV) but not with the clinical target volume (CTV), better results were achieved.Conclusion: The TPP approach was evaluated in HNC patients, and proven to be an efficient tool for managing uncertainties. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

Purpose An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. Material and results Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. Conclusion Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making. ?? 2013 The Authors. Published by Elsevier Ireland Ltd.

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner. Lambin, P. et al. Nat. Rev. Clin. Oncol. 10, 27-40 (2013); published online 20 November 2012; doi:10.1038/nrclinonc.2012.196

Introduction: Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field. Material and methods: In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method. Results: The average time per case to collect the NSCLC data manually was 10.4 +/- 2.1 min and 4.3 +/- 1.1 min when using the automated method (p

A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate-risk prostate cancer. Extending the TCP model for low and high-risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon-ions are becoming increasingly available. Consequently, there is a need for a complete TCP model.A TCP model was fitted and validated to a primary endpoint of 5-year biological no evidence of disease clinical outcome data obtained from a review of the literature for low, intermediate, and high-risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon-ions. The review followed the preferred reporting item for systematic reviews and meta-analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied.The TCP model achieves a good level of fit overall, linear regression results in a p-value of0.000 01 with an adjusted-weighted-R(2) value of 0.77 and a weighted root mean squared error (wRMSE) of 1.2%, to the fitted clinical outcome data. Validation of the model utilizing three independent datasets obtained from the literature resulted in an adjusted-weighted-R(2) value of 0.78 and a wRMSE of less than 1.8%, to the validation clinical outcome data. The weighted mean absolute residual across the entire dataset is found to be 5.4%.This TCP model fitted and validated to clinical outcome data, appears to be an appropriate model for the inclusion of all clinical prostate cancer risk categories, and allows evaluation of current EBRT modalities with regard to tumor control prediction.

Introduction The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. Methods We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. Results There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. Conclusions As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.

This study reports on the development and usefulness of an assessment procedure for teachers’ coaching competence. We examined the usefulness of the developed procedure with respect to making reliable judgments. Video portfolios were constructed by researchers, which consisted of deliberately planned video recordings of teachers’ coaching performance in the classroom and additional data sources providing context information. Six trained assessors scored three video portfolios. Interrater agreement was determined and assessors were interviewed. Judgments across assessors were relatively similar. Both supportive and hindering aspects in the procedure for making reliable judgments could be derived from the interviews. Theoretical insights about assessor scoring are discussed, and implications for optimizing the assessment procedure are described.

According to changed views on driver training and driver instructor preparation, a competence-based instructor exam was introduced in the Netherlands. The exam consists of two parts: (1) multimedia theory tests; (2) a performance lesson for driving instruction and coaching. An implicit idea behind the innovated exam is that it can have a positive backwash effect on the quality of driver instructor preparation programs. This study aims to evaluate the reliability, validity and fairness of the theoretical tests, which appear in different versions for successive groups of PDIs (prospective driving instructors). Data of 4,741 PDIs, enrolled during the period between January 2010 and October 2012, were used for analysis. The results of psychometric analyses show that the theory tests yielded reliable and fair decisions about instructor certification. The predictive validity of the theory tests for the final performance assessment was low. Implications for the design and on-the-fly maintenance of exam item banks are discussed. Follow-up studies will focus on the question, whether the improved instructor exam produces safer drivers in the end.

Advancements in radiation oncology are driving more specific, and thus improved, treatment opportunities. This creates challenges on the assessment of treatment options, as more information is needed to make an informed decision. One of the methods is to use machine-learning techniques to develop predictive models. Although prediction models, embedded in clinical decision support systems (CDSSs), are the foreseen solution, developing/training such prediction models requires large amounts of detailed patient information to reach decisive power. The amount of patients needed to train a reliable prediction model rapidly outgrows the numbers available in a single institution, hence the need for multicenter machinelearning. To be able to learn over multiple centers, several infrastructural prerequisites need to be addressed. First, data needs to be extracted from multiple source systems and represented using standardized terminologies, preferably including the semantics (the actual description) of the represented data. For research and model training purposes, this means that value representations (e.g. “m” or “f” indicating gender) need to be converted into standardized terms (the NCI Thesaurus codes C20197 or C16576, respectively), and that patient-identifiable information (e.g. name, institutional ID, address, etc.) needs to be removed or changed in a non-identifiable way. If datasets from different institutions use the same standardized terminology and data structure, data can be merged. Finally, after merging, prediction models can be learned on the complete dataset, in this chapter known as centralized learning.

In line with changed views on driver training and driver instructor preparation a competence-based instructor exam was introduced in the Netherlands. The exam consists of two parts: 1) multimedia theory tests, 2) a performance assessment. An implicit idea behind the innovated exam is that it can have a positive backwash effect on the quality of driver instructor preparation programs. This study aims to evaluate the reliability, validity and fairness of the theoretical tests, which appear in different versions across time. Data of 4741 prospective driving instructors, enrolled during the period between January 2010 and October 2012, were used for analysis. The results of item response theory (IRT)-analyses show that the theory tests yielded reliable and fair decisions, although misclassifications occurred across versions. The predictive validity of the theory tests for the final performance assessment was low. Implications for the design and maintenance of exam programs are discussed. Follow-up studies will focus on the question, whether the improved instructor exam contribute to safer drivers in the end.

In the last ten years, a number of innovations, mainly inspired by con- structivist notions of learning, have been introduced at various levels of the Dutch educa- tional system. However, constructivist learning environments are rarely implemented. Teachers tend to stick to expository and structured learning environments. This consist- ent finding requires research in order to gain insight into teachers' preferences for learn- ing environments and to determine the factors that support and impede the realization of these learning environments. Regarding the influence of social backgrounds on student learning, is it also important to take stock of parental views on learning environments. This study is focused on teachers' preferences for learning environments, their reported teaching behavior, and how these match with parents' preferences. Three parallel ques- tionnaires were developed for teachers (n = 285), students (n = 951), and parents (n = 636) to measure preferences and behavior at different levels of education, for three types of learning environments: direct instruction; discovery learning; and authentic pedagogy. The results show that teachers often prefer direct instruction, and seldom promote dis- covery learning. While teachers sometimes realize authentic pedagogy, constructive learn- ing tasks are seldom used. Teachers' reported practice and parents' preferences for their children appear to correspond reasonably. Results of multiple regression analyses show that the use of the three types of learning environments yield different predictors. For the use of discovery learning and authentic pedagogy, confidence in students' regulative skills is an important predictor. In predict- ing the use of direct instruction, the teacher's own conception of learning turns out to be an important predictor.

This review article provides a systematic overview of the currently available evidence on the clinical effectiveness of particle therapy for the treatment of non-small cell lung cancer and summarizes findings of in silico comparative planning studies. Furthermore, technical issues and dosimetric uncertainties with respect to thoracic particle therapy are discussed.

In line with European developments, a Dutch second phase coaching program, referred to as the DX- (Driver Xperience) program, was developed for young novice drivers to counteract their high accident risk. More specifically, the aim of the DX-program was to enable young drivers to make responsible decisions and develop positive attitudes regarding four levels of the driving task: combining life style and driving, planning and navigation, participating in different traffic situations and handling the vehicle. In this paper, the design principles of the program are described. The empirical study focused on the entry characteristics of the participating young drivers (n = 3,117) as compared to a reference group of young drivers (n = 345). RESULTS show that the DX-program attracted young drivers that, in some respects, showed a more risky profile than average young drivers in terms of speed violations, anger and the number of fines. In addition, four groups of participants with sharply differing driving styles could be distinguished. Implications for educational design and follow-up research are discussed within the theoretical framework of self-regulated learning. Language: en

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.

Introduction This multicentric in silico trial compares photon and proton radiotherapy for non-small cell lung cancer patients. The hypothesis is that proton radiotherapy decreases the dose and the volume of irradiated normal tissues even when escalating to the maximum tolerable dose of one or more of the organs at risk (OAR). Methods Twenty-five patients, stage IA-IIIB, were prospectively included. On 4D F18-labeled fluorodeoxyglucose-positron emission tomography-computed tomography scans, the gross tumor, clinical and planning target volumes, and OAR were delineated. Three-dimensional conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) photon and passive scattered conformal proton therapy (PSPT) plans were created to give 70 Gy to the tumor in 35 fractions. Dose (de-)escalation was performed by rescaling to the maximum tolerable dose. Results Protons resulted in the lowest dose to the OAR, while keeping the dose to the target at 70 Gy. The integral dose (ID) was higher for 3DCRT (59%) and IMRT (43%) than for PSPT. The mean lung dose reduced from 18.9 Gy for 3DCRT and 16.4 Gy for IMRT to 13.5 Gy for PSPT. For 10 patients, escalation to 87 Gy was possible for all 3 modalities. The mean lung dose and ID were 40 and 65% higher for photons than for protons, respectively. Conclusions The treatment planning results of the Radiation Oncology Collaborative Comparison trial show a reduction of ID and the dose to the OAR when treating with protons instead of photons, even with dose escalation. This shows that PSPT is able to give a high tumor dose, while keeping the OAR dose lower than with the photon modalities.

The current increasing amount of digitalized medical data in health-care demands for solutions to store, share, mine, and analyze these data. Today, medical knowledge and evidence is based on outdated data. Tomorrow, we aim to have a rapid learning health medicine system in which evidence can be generated instantly, based on the most recent data available. The development of this system requires dedication and support of health-care providers, politicians, and patients on many levels. The aim of this system is improvement of health-care quality and support in clinical decision making. Full integration of data handling systems within the clinic and between institutes is inevitable in the near future.

Summary This article describes the results of a second school improvement study directed at schools with mixed‐age classes, and compares the results of this second study with the first study. The second improvement study examines the effects of a staff development programme which introduced selected findings from teaching effectiveness research into ongoing school settings with mixed‐age classes. In addition to the staff development programme the effects of coaching are also evaluated. In contrast to the first improvement study the staff development programme was implemented by teacher educators and school counsellors in the regular educational support context (in‐service teacher education). A quasi‐experimental, treatment‐control group investigation was designed to test the effects of the staff development programme ‘Dealing with mixed‐age classes’, and the effects of coaching. Based on pre‐ and post‐training classroom observations, the second improvement study‐‐like the first one‐‐revealed a significant...