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Background/purpose: Post radiation mucosal ulcers (PRMU) after treatment for oropharyngeal squamous cell carcinoma (OPSCC) can have a huge negative impact on patients’ quality of life, but little is known concerning risk factors and the impact of fraction size. Therefore, the goal of this study was to determine the pattern of PRMU development and to identify risk factors after a hypofractionated stereotactic body radiotherapy boost (SBRT) compared to conventionally fractionated radiotherapy for OPSCC. Material and methods: We performed a retrospective cohort study (N = 332) of OPSCC patients with ≥ 1-year disease-free survival, treated with 46 Gy Intensity Modulated Radiotherapy (IMRT) (2 Gy fractions) followed by either an SBRT boost of 16.5 Gy (5.5 Gy fractions) (N = 180), or 24 Gy IMRT (2 Gy fractions) (N = 152). PRMU (grade ≥ 2) was scored when observed > three months after the last radiotherapy (RT) fraction (CTCAE v5.0). Potential risk factors were analyzed with Cox regression models using death as competing risk. Dose at the PRMU site was calculated by projecting delineated PRMU on the planning CT. Results: All cases of PRMU (N = 64) occurred within 24 months; all were grade 2. The cumulative incidence at 2 years in the SBRT boost group was 26% (N = 46) vs. 12% (N = 18) for conventional fractionation (p = 0.003). Most PRMU developed within nine months (N = 48). PRMU occurring > nine months (N = 16) were mainly observed in the SBRT boost group (N = 15). Sex (p = 0.048), acute tube feeding (p = < 0.001), tumor subsite tonsil (p = 0.001), and N stage (p = 0.017) were associated with PRMU risk at multivariable regression in the hypofractionated SBRT boost group. All 25 delineated PRMU were located within the high dose regions. Conclusion: The risk of PRMU should be included in the cost benefit analysis when considering future research using a hypofractionated SBRT boost for OPSCC patients.

BACKGROUND AND PURPOSE: Diversity, Equity and Inclusion (DEI) in the medical workforce is linked to improved patient care and innovation, as well as employee retention and engagement. The European Society for Radiotherapy and Oncology launched a survey to provide a benchmark of DEI and engagement among radiation oncology (RO) professionals in Europe.METHODS: An anonymous survey was disseminated among RO professionals in Europe. The survey collected demographics and professional information, and participants were asked if they felt they belonged to a minority group. A DEI and workforce engagement questionnaire by Person et al. evaluated 8 inclusion factors. A favourable score was calculated by adding the percentage of "strongly agreed" or "agreed" answers.RESULTS: A total of 812 complete responses were received from 35 European countries. 21% of respondents felt they belonged to a minority group, mostly based on race/ethnicity (5.9%), nationality (4.8%) and age (4.3%). Compared to benchmark data from the United States, scores were lower for most inclusion factors, and to a greater extent for minority groups. The overall favourable score was 58% for those belonging to a minority group, significantly lower than for other respondents (71%, pCONCLUSIONS: Our work indicates that actions to improve DEI and workforce engagement among RO professionals in Europe are urgently needed, in particular among minority groups. This would potentially improve employee wellbeing and retention, promoting high quality care and innovation.

Perfusion MRI is promising for the assessment, prediction, and monitoring of radiation toxicity in organs at risk in head and neck cancer. Arterial spin labeling (ASL) may be an attractive alternative for conventional perfusion MRI, that does not require the administration of contrast agents. However, currently, little is known about the characteristics and performance of ASL in healthy tissues in the head and neck region. Therefore, the purpose of this study was to optimize and evaluate multidelay pseudocontinuous ASL (pCASL) for the head and neck region and to explore nominal values and measurement repeatability for the blood flow (BF), and the transit time and T1 values needed for BF quantification in healthy tissues. Twenty healthy volunteers underwent a scan session consisting of four repeats of multidelay pCASL (postlabel delays: 1000, 1632, 2479 ms). Regions of interest were defined in the parotid glands, submandibular glands, tonsils, and the cerebellum (as a reference). Nominal values of BF were calculated as the average over four repeats per volunteer. The repeatability coefficient and within-subject coefficient of repeatability (wCV) of BF were calculated. The effect of T1 (map vs. cohort average) and transit time correction on BF was investigated. The mean BF (+/- SE) was 55.7 +/- 3.1 ml/100 g/min for the parotid glands, 41.2 +/- 2.8 ml/100 g/min for the submandibular glands, and 32.3 +/- 2.2 ml/100 g/min for the tonsils. The best repeatability was found in the parotid glands (wCV = 13.3%-16.1%), followed by the submandibular glands and tonsils (wCV = 20.0%-24.6%). On average, the effect of T1 and transit time correction on BF was limited, although substantial bias occurred in individual acquisitions. In conclusion, we demonstrated the feasibility of BF measurements in the head and neck region using multidelay pCASL and reported on nominal BF values, BF repeatability, the effect of T1, and transit time in various tissues in the head and neck region.

Background and purpose: Quality of automatic contouring is generally assessed by comparison with manual delineations, but the effect of contour differences on the resulting dose distribution remains unknown. This study evaluated dosimetric differences between treatment plans optimized using various organ-at-risk (OAR) contouring methods. Materials and methods: OARs of twenty lung cancer patients were manually and automatically contoured, after which user-adjustments were made. For each contour set, an automated treatment plan was generated. The dosimetric effect of intra-observer contour variation and the influence of contour variations on treatment plan evaluation and generation were studied using dose-volume histogram (DVH)-parameters for thoracic OARs. Results: Dosimetric effect of intra-observer contour variability was highest for Heart Dmax (3.4 ± 6.8 Gy) and lowest for Lungs-GTV Dmean (0.3 ± 0.4 Gy). The effect of contour variation on treatment plan evaluation was highest for Heart Dmax (6.0 ± 13.4 Gy) and Esophagus Dmax (8.7 ± 17.2 Gy). Dose differences for the various treatment plans, evaluated on the reference (manual) contour, were on average below 1 Gy/1%. For Heart Dmean, higher dose differences were found for overlap with PTV (median 0.2 Gy, 95% 1.7 Gy) vs. no PTV overlap (median 0 Gy, 95% 0.5 Gy). For Dmax-parameters, largest dose difference was found between 0–1 cm distance to PTV (median 1.5 Gy, 95% 4.7 Gy). Conclusion: Dose differences arising from automatic contour variations were of the same magnitude or lower than intra-observer contour variability. For Heart Dmean, we recommend delineation errors to be corrected when the heart overlaps with the PTV. For Dmax-parameters, we recommend checking contours if the distance is close to PTV (

Highlights • Professional quality of life is crucial for oncology professionals. • Alexythymia and empathy affect the individual capacity to cope with stress. • Alexythimia has a negative impact on radiation therapists’ professional quality of life. • Higher empatic concern increases the quality of professional well-being. • This data may help targeting professionals at risk to implement preventive strategies., Background and purpose Physical and mental well-being are crucial for oncology professionals as they affect performance at work. Personality traits, as alexithymia and empathy, may influence professional quality of life. Alexithymia involves diminished skills in emotion processing and awareness. Empathy is pertinent to the ability to understand another’s ‘state of mind/emotion’. The PROject on Burn-Out in RadiatioN Oncology (PRO BONO) investigates professional quality of life amongst radiation oncology professionals, exploring the role of alexithymia and empathy. The present study reports on data pertinent to radiation therapists (RTTs). Material and methods An online survey targeted ESTRO members. Participants were asked to fill out 3 questionnaires for alexithymia, empathy and professional quality of life: (a) Toronto Alexithymia Scale (TAS-20); (b) Interpersonal Reactivity Index (IRI); (c) Professional Quality of Life Scale (ProQoL). The present analysis focuses on RTTS to evaluate compassion satisfaction (CS), secondary traumatic stress (STS) and Burnout and their correlation with alexithymia and empathy, using generalized linear modeling. Covariates found significant at univariate linear regression analysis were included in the multivariate linear regression model. Results A total of 399 RTTs completed all questionnaires. The final model for the burnout scale of ProQoL found, as significal predictors, the TAS-20 total score (β = 0.46, p

Highlights • 49% of 543 survey participants worked full-time from home during the COVID-19 pandemic. • Perceived productivity was lower, with associated feelings of guilt. • Anxiety and depressive symptoms were higher than normative values in Europe. • Anxiety symptoms were negatively correlated with the number of years in research. • Mental health symptoms were lower in participants with institutional health support., Introduction With the COVID-19 pandemic, individuals have been forced to follow strict social isolation guidelines. While crucial to control the pandemic, isolation might have a significant impact on productivity and mental health. Especially for researchers working in healthcare, the current situation is complex. We therefore carried out a survey amongst researchers in the field of radiation oncology to gain insights on the impact of social isolation and working from home and to guide future work. Materials and methods An online survey was conducted between March 27th and April 5th, 2020. The first part contained 14 questions intended to capture an overview of the specific aspects related to research while in isolation. The second (optional) part of the questionnaire was the validated Hospital Anxiety and Depression Scale (HADS), a self-reported measure used to assess levels of anxiety and depressive symptoms. Results From 543 survey participants, 48.8% reported to work full-time from home. The impact on perceived productivity, with 71.2% of participants feeling less productive, caused 58% of participants to feel some level of guilt. Compared to normative data, relatively high levels of anxiety and depressive symptoms were recorded for the 335 participants who filled out the HADS questionnaire. Group comparisons found the presence of a supportive institutional program as the sole factor of statistical significance in both anxiety and depressive symptom levels. People having to work full-time on location showed higher depressive symptom levels than those working from home. Anxiety scores were negatively correlated with the number of research years. Conclusion Results of the survey showed there is a non-negligible impact on both productivity and mental health. As the radiation oncology research community was forced to work from home during the COVID-19 pandemic, lessons can be learned to face future adverse situations but also to improve work-life balance in general.

Highlights • Burnout is critical for oncology professionals, affecting quality of life and performance at work. • Alexithymia and empathy are personality traits influencing the capacity to cope with stress. • Alexithymia increases burnout among medical physicists with a negative impact at work. • Higher level of empathy are correlated to a better professional quality of life. • This data may help identifying subjects at risk to implement preventive strategies., Background and purpose The professional quality of life of radiation oncology professionals can be influenced by different contributing factors, including personality traits. Alexithymia involves deficits in emotion processing and awareness. Empathy is the ability to understand another’s ‘state of mind/emotion’. We investigated professional quality of life, including burnout, in radiation oncology, exploring the role of alexithymia and empathy and targeting the population of medical physicists (MPs), since this professional category is usually underrepresented in surveys exploring professional well-being in radiation oncology and MPs may experience professional distress given the increasing complexity of multimodal cancer care. Material and methods An online survey was addressed to ESTRO members. Participants filled out three questionnaires to evaluate alexithymia, empathy and professional quality of life: a) Toronto Alexithymia Scale (TAS-20); b) Interpersonal Reactivity Index (IRI); c) Professional Quality of Life Scale (ProQoL). Professional quality of life as per ProQoL was considered as dependent variable. The three domains of the ProQoL, namely compassion satisfaction (CS), secondary traumatic stress (STS) and burnout were correlated with alexithymia (as per TAS-20) and empathy (as per IRI with three subcategories: empathic concern, perspective taking and personal distress) and demographic/professional characteristics as independent variables. Generalized linear modeling was used. Significant covariates on univariate linear regression analysis were included in the multivariate linear regression model. Results A total of 308 medical physicists completed all questionnaires. Alexithymia as per TAS-20 was correlated to decreased CS (β = −0.25, p

Purpose To demonstrate the feasibility of predicting the patient-specific treatment planning Pareto front (PF) for prostate cancer patients based only on delineations of PTV, rectum and body. Material/methods Our methodology consists of four steps. First, using Erasmus-iCycle, the Pareto fronts of 112 prostate cancer patients were constructed by generating per patient 42 Pareto optimal treatment plans with different priorities. Dose parameters associated to homogeneity, conformity and dose to rectum were extracted. Second, a 3D convex function representing the PF spanned by the 42 plans was fitted for each patient using three patient-specific parameters. Third, ten features were extracted from the, aforementioned, structures to train a linear-regressor prediction algorithm to predict these three patient-specific parameters. Fourth, the quality of the predictions was assessed by calculating the average and maximum distances of the predicted PF to the 42 plans for patients in the validation cohort. Results The prediction model was able to predict the clinically relevant PF within 2 Gy for 90% of the patients with a median average distance of 0.6 Gy. Conclusions We demonstrate the feasibility of fast, accurate predictions of the patient-specific PF for prostate cancer patients based only on delineations of PTV, rectum and body.

Purpose: The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)–based delineation of OARs in the head and neck (H&N) region. Methods and Materials: After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results: A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions: In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability.

Background/purpose: To determine the efficacy and toxicity profile of a stereotactic body radiotherapy (SBRT) boost as a first line treatment in patients with oropharyngeal squamous cell carcinoma ...

Purpose: To optimize the diffusion-weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region. Methods: Optimized diffusion-weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér-Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose. The effects of (1) b value optimization and number of b values, (2) registration type (none vs. intervolume vs. intra- and intervolume registration), and (3) manual swallowing artifact rejection on the parameter precision were assessed. Results: The SD was higher in the reference set for perfusion fraction, diffusion coefficient, and kurtosis by a factor of 1.7, 1.5, and 2.3 compared to the optimized set, respectively. A smaller SD (factor 0.7) was seen in pseudodiffusion coefficient. The sets containing 15, 20, and 30 b values had comparable repeatability in all parameters, except pseudodiffusion coefficient, for which set size 30 was worse. Equal repeatability for the registration approaches was seen in all parameters of interest. Swallowing artifact rejection removed the bias when present. Conclusion: To achieve optimal hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region, b value optimization and swallowing artifact image rejection are beneficial. The optimized set of 15 b values yielded the optimal protocol efficiency, with a precision comparable to larger b value sets and a 50% reduction in scan time.

Purpose or Objective A questionnaire among 543 radiation oncology (RO) professionals involved in research demonstrated that approximately half of them were forced to work full-time from home due to the COVID-19 pandemic (Dhont et al, ctRO, 2020). At the same time, those still working on location had to adjust to a new working environment. Both groups and especially early-career researchers experienced a substantial impact on mental health and work productivity. The aim of the current qualitative study was to assess in-depth the specific challenges faced by young researchers in RO and what constitutes helpful support in case of continuing or future adverse situations. Materials and Methods This study was conducted jointly by researchers in the field of RO and experts in qualitative research. Data was collected using online Focus Groups (oFGs) facilitated by qualitative research experts. Three oFGs were held with 7-11 young RO professionals (total N=25), in which data saturation was reached. Invitations to participate in the oFGs were sent out 30 days prior through the ESTRO newsletter, social media and personal networks. Inclusion criteria to participate were (1) working in RO, (2) in a European institution, and (3) with less than 10 years of research experience. Efforts were made to ensure a diverse panel. The design of the oFGs (Figure 1) comprised two main steps: (1) Deepening the understanding of experienced impacts during the COVID-19 pandemic, and (2) formulating solutions that could positively address the identified impacts. The oFGs were recorded and transcribed verbatim. Transcripts were analyzed via the qualitative analysis software Atlas (2019) through inductive coding. The coded segments were then translated into themes. $Φg Results In addition to years of research experience, seven other variables were identified as potentially impacting on the work or personal sphere in a positive and/or negative manner. In total ten main impacts were identified in the personal (2) and work sphere (8), which were either affected directly by the variables or indirectly through other impacts. It was for example found that job title could negatively impact the type of work the researcher had to perform during the pandemic, which in turn negatively impacted his/her mental health. All variables, impacts and how they relate are illustrated in Figure 2. Proposed solutions were mostly technical, illustrating that the most pressing issue was to ensure the possibility to perform one's work. Proposed solutions for social isolation (e.g. virtual social events) were often countered by the presence of “zoom fatigue”. $Φg Conclusion Different variables, their impacts and a complex interplay between them have been identified to determine how early-career RO professionals experienced working during the COVID-19 pandemic. Beyond technical solutions to enable remote working, the distinct personal situations must be considered when proposing solutions to support mental health in these situations.

Background and purpose: Different factors may influence the professional quality of life of oncology professionals. Among them, personality traits, as alexithymia and empathy, are underinvestigated. Alexithymia is about deficits in emotion processing and awareness. Empathy is the ability to understand another's 'state of mind'/emotion. The PROject on BurnOut in RadiatioN Oncology (PRO BONO) assesses professional quality of life, including burnout, in the field of radiation oncology and investigates alexithymia and empathy as contributing factors.Material and methods: An online survey was conducted amongst ESTRO members. Participants completed 3 validated questionnaires for alexithymia, empathy and professional quality of life: (a) Toronto Alexithymia Scale; (b) Interpersonal Reactivity Index; (c) Professional Quality of Life Scale. The present analysis, focusing on radiation/clinical oncologists, evaluates Compassion Satisfaction (CS), Secondary Traumatic Stress (STS) and Burnout and correlates them with alexithymia and empathy (empathic concern, perspective taking and personal distress) with generalized linear modeling. Significant covariates on univariate linear regression analysis were included in the multivariate linear regression model.Results: A total of 825 radiation oncologists completed all questionnaires. A higher level of alexithymia was associated to decreased CS (beta:-0.101; SE: 0.018; p Conclusions: Alexithymic personality trait increased burnout risk, with less professional satisfaction. Empathic concern was associated to increased stress, without leading to burnout, resulting in higher professional fulfillment. These results may be used to benchmark preventing strategies, such as work-hour restrictions, peer support, debriefing sessions, and leadership initiatives for professionals at risk. (c) 2020 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 147 (2020) 162-168

Purpose Treatment plans manually generated in clinical routine may suffer from variations and inconsistencies in quality. Using such plans for validating a DVH prediction algorithm might obscure its intrinsic prediction accuracy. In this study we used a recently published large database of Pareto-optimal prostate cancer plans to assess the prediction accuracy of a commercial knowledge-based DVH prediction algorithm, RapidPlan. The database plans were consistently generated with automated planning using an independent optimizer, and can be considered as aground truth of plan quality. Methods Prediction models were generated using training sets with 20, 30, 45, 55 and 114 Pareto-optimal plans. Model-20 and Model-30 were built using 5 groups of randomly selected training patients. For 60 independent Pareto-optimal validation plans, predicted and database DVHs were compared. Results For model-114, differences between predicted and database mean doses of more than ± 10% in rectum, anus and bladder, occurred for 23.3%, 55.0%, and 6.7% of the validation plans, respectively. For rectum V65Gy and V75Gy, differences outside the ±10% range were observed in 21.7% and 70.0% of validation plans, respectively. For 61.7% of validation plans, inaccuracies in predicted rectum DVHs resulted in a deviation in predicted NTCP for rectal bleeding outside ±10%. With smaller training sets the DVH prediction performance deteriorated, showing dependence on the selected training patients. Conclusion Even when analysed with Pareto-optimal plans with highly consistent quality, clinically relevant deviations in DVH predictions were observed. Such deviations could potentially result in suboptimal plans for new patients. Further research on DVH prediction models is warranted.

Purpose To prospectively investigate the use of an independent DVH prediction tool to detect outliers in the quality of fully automatically generated treatment plans for prostate cancer patients. Materials/methods A plan QA tool was developed to predict rectum, anus and bladder DVHs, based on overlap volume histograms and principal component analysis (PCA). The tool was trained with 22 automatically generated, clinical plans, and independently validated with 21 plans. Its use was prospectively investigated for 50 new plans by replanning in case of detected outliers. Results For rectum Dmean, V65Gy, V75Gy, anus Dmean, and bladder Dmean, the difference between predicted and achieved was within 0.4 Gy or 0.3% (SD within 1.8 Gy or 1.3%). Thirteen detected outliers were re-planned, leading to moderate but statistically significant improvements (mean, max): rectum Dmean (1.3 Gy, 3.4 Gy), V65Gy (2.7%, 4.2%), anus Dmean (1.6 Gy, 6.9 Gy), and bladder Dmean (1.5 Gy, 5.1 Gy). The rectum V75Gy of the new plans slightly increased (0.2%, p = 0.087). Conclusion A high accuracy DVH prediction tool was developed and used for independent QA of automatically generated plans. In 28% of plans, minor dosimetric deviations were observed that could be improved by plan adjustments. Larger gains are expected for manually generated plans.

With the advent of automatic treatment planning options like Pinnacle's Autoplanning (PAP), the challenge arises how to assess the quality of a plan that no dosimetrist did work on. The aim of this study was to assess plan quality consistency of PAP prostate cancer patients in clinical practice.100 prostate cancer patients were included from NKI and 129 from RadboudUMC (RUMC). Per institute a previously developed [1] treatment planning QA model, based on overlap volume histograms, was trained on PAP plans to predict achievable dose metrics which were then compared to the clinical PAP plans. A threshold of 3 Gy (DVH dose parameters)/3% (DVH volume parameters) was used to detect outliers. For the outlier plans, the PAP technique was adjusted with the aim of meeting the threshold.The average difference between the prediction and the clinically achieved value was0.5 Gy (mean dose parameters) and1.2% (volume parameters), with standard deviation of 1.9 Gy/1.5% respectively. We found 8% (NKI)/25% (RUMC) of patients to exceed the 3 Gy/3% threshold, with deviations up to 6.7 Gy (mean dose rectum) and 6% (rectal wall V64Gy). In all cases the plans could be improved to fall within the thresholds, without compromising the other dose metrics.Independent treatment planning QA was used successfully to assess the quality of clinical PAP in a multi-institutional setting. Respectively 8% and 25% suboptimal clinical PAP plans were detected that all could be improved with replanning. Therefore we recommend the use of independent treatment plan QA in combination with PAP for prostate cancer patients.

IMRT planning with commercial Treatment Planning Systems (TPSs) is a trial-and-error process. Consequently, the quality of treatment plans may not be consistent among patients, planners and institutions. Recently, different plan quality assurance (QA) models have been proposed, that could flag and guide improvement of suboptimal treatment plans. However, the performance of these models was validated using plans that were created using the conventional trail-and-error treatment planning process. Consequently, it is challenging to assess and compare quantitatively the accuracy of different treatment planning QA models. Therefore, we created a golden standard dataset of consistently planned Pareto-optimal IMRT plans for 115 prostate patients. Next, the dataset was used to assess the performance of a treatment planning QA model that uses the overlap volume histogram (OVH). 115 prostate IMRT plans were fully automatically planned using our in-house developed TPS Erasmus-iCycle. An existing OVH model was trained on the plans of 58 of the patients. Next it was applied to predict DVHs of the rectum, bladder and anus of the remaining 57 patients. The predictions were compared with the achieved values of the golden standard plans for the rectum D mean, V 65, and V 75, and D mean of the anus and the bladder. For the rectum, the prediction errors (predicted–achieved) were only −0.2 ± 0.9 Gy (mean ± 1 SD) for D mean,−1.0 ± 1.6% for V 65, and −0.4 ± 1.1% for V 75. For D mean of the anus and the bladder, the prediction error was 0.1 ± 1.6 Gy and 4.8 ± 4.1 Gy, respectively. Increasing the training cohort to 114 patients only led to minor improvements. A dataset of consistently planned Pareto-optimal prostate IMRT plans was generated. This dataset can be used to train new, and validate and compare existing treatment planning QA models, and has been made publicly available. The OVH model was highly accurate in predicting rectum and anus DVHs. For the bladder, larger prediction errors were observed.

Purpose/objective: To develop a method to predict feasible organ-at-risk (OAR) and tumour dose levels of non-small cell lung cancer (NSCLC) patients prior to the start of treatment planning. Materials/methods: Included were NSCLC patients treated with volumetric modulated arc therapy according to an institutional isotoxic dose-escalation protocol. A training cohort (N = 50) was used to calculate the average dose inside the OARs as a function of the distance to the planning target volume (PTV). These dose-distance relations were used in a validation cohort (N = 39) to predict dose-volume histograms (DVHs) of OARs and PTV as well as the maximum individualized PTV dose escalation. Results: The validation cohort showed that predicted and achieved MLD were in agreement with each other (difference: -0.1 +/- 1.9 Gy, p = 0.81). The spinal cord was dose limiting in only two patients, which was correctly predicted. The achieved mean PTV dose varied from 52 to 73 Gy and was predicted correctly with an accuracy better than 2 Gy (i.e. 1 fraction) for 79% of the patients. Conclusion: We have shown that the MLD and the prescribed PTV dose could be accurately predicted for NSCLC patients. This method can guide the treatment planner to achieve optimal OAR sparing and tumour dose escalation. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

Background: Proton therapy is becoming increasingly available, so it is important to apply objective and individualized patient selection to identify those who are expected to benefit most from proton therapy compared to conventional intensity modulated radiation therapy (IMRT). Comparative treatment planning using normal tissue complication probability (NTCP) evaluation has recently been proposed. This work investigates the impact of NTCP model and dose uncertainties on model-based patient selection. Material and Methods: We used IMRT and intensity modulated proton therapy (IMPT) treatment plans of 78 oropharyngeal cancer patients, which were generated based on automated treatment planning and evaluated based on three published NTCP models. A reduction in NTCP of more than a certain threshold (e.g. 10% lower NTCP) leads to patient selection for IMPT, referred to as ‘nominal’ selection. To simulate the effect of uncertainties in NTCP-model coefficients (based on reported confidence intervals) and planned doses on the accuracy of model-based patient selection, the Monte Carlo method was used to sample NTCP-model coefficients and doses from a probability distribution centered at their nominal values. Patient selection accuracy within a certain sample was defined as the fraction of patients which had similar selection in both the ‘nominal’ and ‘sampled’ scenario. Results: For all three NTCP models, the median patient selection accuracy was found to be above 70% when only NTCP-model uncertainty was considered. Selection accuracy decreased with increasing uncertainty resulting from differences between planned and delivered dose. In case of excessive dose uncertainty, selection accuracy decreased to 60%. Conclusion: Model and dose uncertainty highly influence the accuracy of model-based patient selection for proton therapy. A reduction of NTCP-model uncertainty is necessary to reach more accurate model-based patient selection.

Purpose: To assess the accumulated dose and the toxicity after reirradiation for tumors in the lung using non-rigid registration. Material and methods: Twenty patients with a tumor in the lung were reirradiated with or after stereotactic radiotherapy. The summed dose distribution was calculated using non-rigid registration. All doses were recalculated to an equivalent dose of 2 Gy per fraction (EQD2). The median follow-up time was 12 months (range 2-52). Results: The median D-max of the lung in the summed plans was 363 Gy(3) (range 123-590). The median accumulated V20 of the lungs was 15.2%. Seven patients had in the heart and the trachea an accumulated dose >= 70 Gy(3), with a median D-max of the heart of 115 Gy(3) and 89 Gy(3) for the trachea. Eight patients had in the esophagus an accumulated dose >= 70 Gy(3), with a median accumulated dose of 85 Gy(3). No grade 3-5 toxicity was observed. Conclusion: Reirradiation of the lung with or after stereotactic radiotherapy is feasible to a median D-max of 363 Gy(3) to the lung, as low toxicity was observed. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

Purpose To develop a model to assess the quality of an IMRT treatment plan using data of prior patients with pancreatic adenocarcinoma. Methods The dose to an organ at risk (OAR) depends in large part on its orientation and distance to the planning target volume (PTV). A database of 33 previously treated patients with pancreatic cancer was queried to find patients with less favorable PTV-OAR configuration than a new case. The minimal achieved dose among the selected patients should also be achievable for the OAR of the new case. This way the achievable doses to the OARs of 25 randomly selected pancreas cancer patients were predicted. The patients were replanned to verify if the predicted dose could be achieved. The new plans were compared to their original clinical plans. Results The predicted doses were achieved within 1 and 2 Gy for more than 82% and 94% of the patients, respectively, and were a good approximation of the minimal achievable doses. The improvement after replanning was 1.4 Gy (range 0–4.6 Gy) and 1.7 Gy (range 0–6.3 Gy) for the mean dose to the liver and the kidneys, respectively, without compromising target coverage or increasing radiation dose to the bowel, cord or stomach. Conclusions The model could accurately predict the achievable doses, leading to a considerable decrease in dose to the OARs and an increase in treatment planning efficiency.

Purpose Our hypothesis was that pretreatment inflammation in the lung makes pulmonary tissue more susceptible to radiation damage. The relationship between pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F]FDG) uptake in the lungs (as a surrogate for inflammation) and the delivered radiation dose and radiation-induced lung toxicity (RILT) was investigated. Methods and Materials We retrospectively studied a prospectively obtained cohort of 101 non-small-cell lung cancer patients treated with (chemo)radiation therapy (RT). [ 18 F]FDG-positron emission tomography-computed tomography (PET-CT) scans used for treatment planning were studied. Different parameters were used to describe [ 18 F]FDG uptake patterns in the lungs, excluding clinical target volumes, and the interaction with radiation dose. An increase in the dyspnea grade of 1 (Common Terminology Criteria for Adverse Events version 3.0) or more points compared to the pre-RT score was used as an endpoint for analysis of RILT. The effect of [ 18 F]FDG and CT-based variables, dose, and other patient or treatment characteristics that effected RILT was studied using logistic regression. Results Increased lung density and pretreatment [ 18 F]FDG uptake were related to RILT after RT with univariable logistic regression. The 95th percentile of the [ 18 F]FDG uptake in the lungs remained significant in multivariable logistic regression ( p = 0.016; odds ratio [OR] = 4.3), together with age ( p = 0.029; OR=1.06), and a pre-RT dyspnea score of ≥1 ( p = 0.005; OR=0.20). Significant interaction effects were demonstrated among the 80th, 90th, and 95th percentiles and the relative lung volume receiving more than 2 and 5 Gy. Conclusions The risk of RILT increased with the 95th percentile of the [ 18 F]FDG uptake in the lungs, excluding clinical tumor volume (OR = 4.3). The effect became more pronounced as the fraction of the 5%, 10%, and 20% highest standardized uptake value voxels that received more than 2 Gy to 5 Gy increased. Therefore, the risk of RILT may be decreased by applying sophisticated radiotherapy techniques to avoid areas in the lung with high [ 18 F]FDG uptake.

Introduction Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia⩾grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

Purpose: To correct megavoltage cone-beam CT (MVCBCT) images of the thorax and abdomen for cupping and truncation artefacts to reconstruct the 3D-delivered dose distribution for treatment evaluation. Materials and methods: MVCBCT scans of three phantoms, three lung and two rectal cancer patients were acquired. The cone-beam projection images were iteratively corrected for cupping and truncation artefacts and the resulting primary transmission was used for cone-beam reconstruction. The reconstructed scans were merged into the planning CT scan (MVCBCT+). Dose distributions of clinical IMRT, stereotactic and conformal treatment plans were recalculated on the uncorrected and corrected MVCBCT+ scans using the treatment planning system and compared to the planned dose distribution. Results: The dose distributions on the corrected MVCBCT+ of the phantoms were accurate for 99% of the voxels within 2% or 2 mm. Using this method the errors in mean GTV dose reduced from about 10% to 1% for the patients. Conclusions: The method corrects cupping and truncation artefacts in cone-beam scans of the thorax and abdomen in addition to head-and-neck (demonstrated previously). The corrected scans can be used to calculate the influence of anatomical changes on the 3D-delivered dose distribution.

The purpose of this study was to increase the potential of dose redistribution by incorporating estimates of oxygen heterogeneity within imaging voxels for optimal dose determination. Cellular oxygen tension (pO2) distributions were estimated for imaging-size-based voxels by solving oxygen diffusion-consumption equations around capillaries placed at random locations. The linear-quadratic model was used to determine cell survival in the voxels as a function of pO2 and dose. The dose distribution across the tumour was optimized to yield minimal survival after 30 × 2 Gy fractions by redistributing the dose based on differences in oxygen levels. Eppendorf data of a series of 69 tumours were used as a surrogate of what might be expected from oxygen imaging datasets. Dose optimizations were performed both taking into account cellular heterogeneity in oxygenation within voxels and assuming a homogeneous cellular distribution of oxygen. Our simulations show that dose redistribution based on derived cellular oxygen distributions within voxels result in dose distributions that require less total dose to obtain the same degree of cell kill as dose distributions that were optimized with a model that considered voxels as homogeneous with respect to oxygen. Moderately hypoxic tumours are expected to gain most from dose redistribution. Incorporating cellular-based distributions of radiosensitivity into dose-planning algorithms theoretically improves the potential gains from dose redistribution algorithms. © 2009 Institute of Physics and Engineering in Medicine.

Tumors of salivary glands arise mainly from the parotid gland. Magnetic Resonance Imaging (MRI) is mandatory not only to localize precisely the tumor within the gland but also to differentiate between benign and malignant neoplasms, in competition with cytology in fine-needle aspiration biopsy. Tumors without risk of transformation, such as adenolymphoma, are not systematically operated on. Indications of roentgenotherapy and irradiation volumes depend on histologic type, localisation and size of the tumor.

a chylurie, definie par une concentration elevee de triglycerides dans les urines, est toujours due a une fistule lympho-urinaire. Ce signe fonctionnel, tres rare en Europe mais bien connu en Asie et en Afrique repond a deux types de causes : parasitaires (dominees par la filariose lymphatique a Wuchereria Bancrofti) et non parasitaires. Les etiologies non parasitaires de chylurie peuvent etre d’origine posttraumatique, malformative (stenose congenitale du canal thoracique, anevrisme lymphatique ou thoracique) ou infectieuse non parasitaire (tuberculose, adenite mesenterique, abces). Nous rapportons un cas de fistule pyelo-lymphatique secondaire a une filariose lymphatique, facilement mise en evidence sur l’urographie intraveineuse et non directement visible sur l’uro-scanner.

The accuracy of intensity modulated proton therapy (IMPT) is sensitive to range uncertainties. Geometric margins, as dosimetric surrogates, are ineffective and robust optimization strategies are needed. These, however, lead to increased normal tissue dose. We explore here how this dose increase can be reduced by increasing the maximum tumor dose instead. We focus on range uncertainties, modeled by scaling the stopping powers 5% up (undershoot) or down (overshoot) compared to the nominal scenario. Robust optimization optimizes for target dose conformity in the most likely scenario, not the worst, while constraining target coverage for the worst-case scenario. Non-robust plans are also generated. Different maximum target doses are applied (105% versus 120% versus 140%) to investigate the effect on normal tissue dose reduction. The method is tested on a homogeneous and a lung phantom and on a liver patient. Target D99 of the robust plans equals the prescription dose of 60 GyE(4) for all scenarios, but decreases to 36 GyE for the non-robust plans. The mean normal tissue dose in a 2 cm ring around the target is 11% to 31% higher for the robust plans. This increase can be reduced to -8% and 3% (compared to the non-robust plan) by allowing a maximum tumor dose of 120% instead of 105%. Thus robustness leads to more normal tissue dose, but it can be compensated by allowing a higher maximum tumor dose.

A novel model of the titanium Rotterdam tandem and ovoid applicator is presented. As titanium produces artefacts in MR images, an MR sequence was sought and optimised for visualisation and accurate applicator reconstruction. The mean inter-observer (8 observers) variability for four patients was only 0.7 mm (maximum 1.7 mm).

Background and purpose: To predict the lowest achievable rectum 035 for quality assurance of IMRT plans of prostate cancer patients. Materials and methods: For each of 24 patients from a database of 47 previously treated patients, the anatomy was compared to the anatomies of the other 46 to predict the minimal achievable rectum D35. The 24 patients were then replanned to obtain maximally reduced rectum D35. Next, the newly derived plans were added to the database to replace the original clinical plans, and new predictions of the lowest achievable rectum D35 were made. Results: After replanning, the rectum D35 reduced by 9.3 Gy +/- 6.1 (average +/- 1 SD; p < 0.001) compared to the original plan. The first predictions of the rectum D35 were 4.8 Gy +/- 4.2 (average +/- 1 SD; p < 0.001) too high when evaluated with the new plans. After updating the database, the replanned and newly predicted rectum D35 agreed within 0.1 Gy +/- 2.8 (average +/- 1 SD; p = 0.89). The doses to the bladder, anus and femoral heads did not increase compared to the original plans. Conclusions: For individual prostate patients, the lowest achievable rectum D35 in IMRT planning can be accurately predicted from dose distributions of previously treated patients by quantitative comparison of patient anatomies. These predictions can be used to quantitatively assess the quality of IMRT plans. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.

Purpose To characterize the relationship between pre-radiotherapy 18Fluorodeoxyglucose (FDG) uptake in a tumour voxel, radiation dose and the probability to achieve metabolic control in the tumour voxel after radiotherapy. Materials and methods Thirty-nine patients with inoperable stage I-III non-small cell lung cancer, treated with radiotherapy (RT) alone or sequential chemo radiation were analysed retrospectively. Twenty-two showed metabolic active areas in the tumour 3 months post-radiotherapy, which is known to be a surrogate for persistent local tumour failure and worse survival. Pre- and post-RT FDG-PET-CT scans were registered and the metabolic active zones within the tumour after RT were projected on the pre-RT scan. Multi-level logistic regression was performed to determine the relation between the FDG uptake if a voxel pre-RT and its metabolic state after RT. Results The probability that a voxel is metabolically controlled (mVCP), decreased significantly with increasing FDG uptake in a voxel (SUV) (OR = 0.72), increasing tumour volume (20 cm3) (OR = 0.89) and increasing dose (Gy) (OR = 0.99). Inter-patient differences in mVCP were substantial. Conclusion A methodology was presented to derive relationships between FDG uptake, dose and metabolic control. Although no strong dose effect relation was demonstrated, mVCP decreased with increasing FDG uptake and tumour volume.

Background: Currently, treatment plans are generated by dosimetrists using a trial-and-error procedure. The process may take several hours and plan quality is dependent on the skills and experience of the dosimetrist, and on allotted time. We have developed and clinically introduced a system for fully automatic plan generation, using lexicographic multi-criterial optimization to replace the labour-intensive and operator-dependent trialand-error approach. Materials and Methods: For each patient, the treatment plan is fully automatically generated by the clinical treatment planning system (Monaco, Elekta AB), based on a patient-specific template that is automatically pre-generated with our in-house lexicographic multi-criterial optimizer (“Erasmus-iCycle”, Med Phys. 2012; 39(2): 951). Automatic plan generation in Erasmus-iCycle is based on a ‘wishlist’ with hard constraints and treatment objectives with assigned priorities. For each treatment site (e.g. HN 85(3): 866-72). For a group of 44 cervical cancer patients, dual-arc VMAT AUTO-plans were superior to MANUAL-plans generated by an expert cervical cancer planner, spending many hours; reduced small bowel V 15Gy ,V 45Gy ,a nd Dmean, bladder Dmean, and rectum Dmean ,p