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PURPOSEMost research on artificial intelligence–based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).MATERIALS AND METHODSNinety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.RESULTSAI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (–1.41 [–2.44; –0.25]), mandible (–6.60 [–8.09; –3.35]), optic nerve (–0.19 [–0.47; –0.02]), parotid (–1.80 [–2.66; –0.32]), and thyroid (–1.03 [–2.18; –0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [–0.51; 5.14]), oral cavity (1.42 [–0.08; 4.14]), and thyroid (1.60 [–0.04; 2.22]).CONCLUSIONThe study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.

Summary: This retrospective study examined bone flap displacement during radiotherapy in 25 post-operative brain tumour patients. Though never exceeding 2.5 mm, the sheer frequency of displacement highlights the need for future research on larger populations to validate its presence and assess the potential clinical impact on planning tumour volume margins.

Objective: NCT01780675, a multicenter randomized phase III trial of prophylactic cranial irradiation (PCI) versus PCI with hippocampal sparing in small cell lung cancer (SCLC) investigated neurocognitive decline and safety. As part of quality assurance, we evaluated if hippocampal avoidance (HA)-PCI was performed according to the NCT01780675 trial protocol instructions, and performed a safety analysis to study the incidence and location of brain metastases for patients treated with HA-PCI. Methods: This retrospective analysis evaluated the quality of the irradiation given in the randomized controlled trial (RCT) comparing SCLC patients receiving PCI with or without hippocampal avoidance, using intensity modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT). The dose distribution for each patient receiving HA-PCI was retrieved and analyzed to evaluate if the treatment dose constraints were met. A questionnaire was sent out to all participating sites, and data on radiotherapy technique, pre-treatment dummy runs, phantom measurements and treatment electronic portal imaging device (EPID) dosimetry were collected and analyzed. As part of the safety analysis, the follow-up magnetic resonance imaging (MRI) or computerized tomography (CT) scans on which cranial disease progression was first diagnosed were collected and matched to the radiotherapy planning dose distribution. The matched scans were reviewed to analyze the location of the brain metastases in relation to the prescribed dose. Results: A total of 168 patients were randomized in the NCT01780675 trial in 10 centers in the Netherlands and Belgium from April 2013 until March 2018. Eighty two patients receiving HA-PCI without evidence of brain metastases were analyzed. All patients were treated with 25 Gy in 10 fractions. Dummy runs and phantom measurements were performed in all institutions prior to enrolling patients into the study. The radiotherapy (RT) plans showed a median mean bilateral hippocampal dose of 8.0 Gy, range 5.4–11.4 (constraint ≤ 8.5 Gy). In six patients (7.3%) there was a protocol violation of the mean dose in one or both hippocampi. In four of these six patients (4.9%) the mean dose to both hippocampi exceeded the constraint, in 1 patient (1.2%) only the left and in 1 patient (1.2%) only the right hippocampal mean dose was violated (average median dose left and right 8.9 Gy). All patients met the trial dose constraint of V115% PTV ≤ 1%; however the Dmax PTV constraint of ≤ 28.75 Gy was violated in 22.0% of the patients. The safety analysis showed that 14 patients (17.1%) developed cranial progression. No solitary brain metastases in the underdosed region were found. Two out of 11 patients with multiple brain metastasis developed metastasis in the underdosed region(s). Conclusions: The radiotherapy quality within the HA-PCI trial is performed according to the protocol guidelines. The dose constraints to the hippocampi are met in the vast majority of cases. In all patients, the volume of the brain for which a higher dose was accepted, is according to the trial. However, within this volume there are small areas with higher doses than advised.

Background and purpose: Dose calculation on cone-beam computed tomography (CBCT) images has been less accurate than on computed tomography (CT) images due to lower image quality and discrepancies in CT numbers for CBCT. As increasing interest arises in offline and online re-planning, dose calculation accuracy was evaluated for a novel CBCT imager integrated into a ring gantry treatment machine. Materials and methods: The new CBCT system allowed fast image acquisition (5.9 s) by using new hardware, including a large-size flat panel detector, and incorporated image-processing algorithms with iterative reconstruction techniques, leading to accurate CT numbers allowing dose calculation. In this study, CBCT- and CT-based dose calculations were compared based on three anthropomorphic phantoms, after CBCT-to-mass-density calibration was performed. Six plans were created on the CT scans covering various target locations and complexities, followed by CBCT to CT registrations, copying of contours, and re-calculation of the plans on the CBCT scans. Dose-volume histogram metrics for target volumes and organs-at-risk (OARs) were evaluated, and global gamma analyses were performed. Results: Target coverage differences were consistently below 1.2 %, demonstrating the agreement between CT and re-calculated CBCT dose distributions. Differences in Dmean for OARs were below 0.5 Gy for all plans, except for three OARs, which were below 0.8 Gy ( 97 %. Conclusions: This study demonstrated comparable results between dose calculations performed on CBCT and CT acquisitions. The new CBCT system with enhanced image quality and CT number accuracy opens possibilities for off-line and on-line re-planning.

Background and purpose: Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT evaluation, and assessed if automatic target contour propagation would lead to the same clinical decision for plan adaptation as the manual workflow. Materials and methods: This study included 79 consecutive patients with a total of 250 reCTs which had been manually evaluated. To assess the feasibility of automated reCT evaluation, we propagated the clinical target volumes (CTVs) deformably from the planning-CT to the reCTs in a commercial treatment planning system. The dose-volume-histogram parameters were extracted for manually re-delineated (CTVmanual) and deformably mapped target contours (CTVauto). It was compared if CTVmanual and CTVauto both satisfied/failed the clinical constraints. Duration of the reCT workflows was also recorded. Results: In 92% (N = 229) of the reCTs correct flagging was obtained. Only 4% (N = 9) of the reCTs presented with false negatives (i.e., at least one clinical constraint failed for CTVmanual, but all constraints were satisfied for CTVauto), while 5% (N = 12) of the reCTs led to a false positive. Only for one false negative reCT a plan adaption was made in clinical practice, i.e., only one adaptation would have been missed, suggesting that automated reCT evaluation was possible. Clinical introduction hereof led to a time reduction of 49 h (from 65 to 16 h). Conclusion: Deformable target contour propagation was clinically acceptable. A script-based automatic reCT evaluation workflow has been introduced in routine clinical practice.

BackgroundRadiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI.PurposeThe purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images.Materials and methodsFDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew’s correlation coefficient (MCC), and the F1 score calculated for both classes separately.ResultsCNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours.ConclusionHigh performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

Purpose: The current knowledge on biological effects associated with proton therapy is limited. Therefore, we investigated the distributions of dose, dose-averaged linear energy transfer (LETd), and the product between dose and LETd (DLETd) for a patient cohort treated with proton therapy. Different treatment planning system features and visualization tools were explored. Methods and Materials: For a cohort of 24 patients with brain tumors, the LETd, DLETd, and dose was calculated for a fixed relative biological effectiveness value and 2 variable models: plan-based and phenomenological. Dose threshold levels of 0, 5, and 20 Gy were imposed for LETd visualization. The relationship between physical dose and LETd and the frequency of LETd hotspots were investigated. Results: The phenomenological relative biological effectiveness model presented consistently higher dose values. For lower dose thresholds, the LETd distribution was steered toward higher values related to low treatment doses. Differences up to 26.0% were found according to the threshold. Maximum LETd values were identified in the brain, periventricular space, and ventricles. An inverse relationship between LETd and dose was observed. Frequency information to the domain of dose and LETd allowed for the identification of clusters, which steer the mean LETd values, and the identification of higher, but sparse, LETd values. Conclusions: Identifying, quantifying, and recording LET distributions in a standardized fashion is necessary, because concern exists over a link between toxicity and LET hotspots. Visualizing DLETd or dose × LETd during treatment planning could allow for clinicians to make informed decisions.

Background and purpose: User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region. Materials and methods: A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed. Subsampling was performed for some OARs, using an inter-slice gap of 1–3 slices. Commonly-used whole-organ contouring assessment measures were calculated, and all cases were registered to a common reference shape per OAR to identify regions of manual adjustment. Results were expressed as the median, 10th-90th percentile of adjustment and visualized using 3D renderings. Results: Per OAR, the median amount of editing was below 1 mm. However, large adjustments were found in some locations for most OARs. In general, enlarging of the auto-contours was needed. Subsampling DL-contours showed less adjustments were made in the interpolated slices compared to simulated no-subsampling for these OARs. Conclusion: The real-world performance of automatic DL-contouring software was evaluated and proven useful in clinical practice. Specific regions-of-adjustment were identified per OAR in the thorax region, and separate models were found to be necessary for specific clinical indications different from training data. This analysis showed the need to perform routine clinical analysis especially when procedures or acquisition protocols change to have the best configuration of the workflow.

Background and purpose: Dose painting by numbers (DPBN) require a high degree of dose modulation to fulfill the image-based voxel wise dose prescription. The aim of this study was to assess the dosimetric accuracy of 18F-fluoro-2-deoxy-glucose positron emission tomography(18F-FDG-PET)-based DPBN in an anthropomorphic lung phantom using alanine dosimetry. Materials and methods: A linear dose prescription based on 18F-FDG-PET image intensities within the gross tumor volume (GTV) of a lung cancer patient was employed. One DPBN scheme with low dose modulation (Scheme A; minimum/maximum fraction dose to the GTV 2.92/4.26 Gy) and one with a high modulation (Scheme B; 2.81/4.52 Gy) were generated. The plans were transferred to a computed tomograpy (CT) scan of a thorax phantom based on CT images of the patient. Using volumetric modulated arc therapy (VMAT), DPBN was delivered to the phantom with embedded alanine dosimeters. A plan was also delivered to an intentionally misaligned phantom. Absorbed doses at various points in the phantom were measured by alanine dosimetry. Results: A pointwise comparison between GTV doses from prescription, treatment plan calculation and VMAT delivery showed high correspondence, with a mean and maximum dose difference of

PurposeArtificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans.MethodsClinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC).ResultsThe best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence.ConclusionWe have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Ten new organs at risk (OARs) were recently introduced in the updated European Particle Therapy Network neurological contouring atlas. Despite the use of the illustrated atlas and descriptive text, interindividual contouring variations may persist. To further facilitate the contouring of these OARs, educational films were developed and published on www.cancerdata.org.

Background and purpose: The COVID-19 pandemic has imposed changes in radiotherapy (RT) departments worldwide. Medical physicists (MPs) are key healthcare professionals in maintaining safe and effective RT. This study reports on MPs experience during the first pandemic peak and explores the consequences on their work. Methods: A 39-question survey on changes in departmental and clinical practice and on the impact for the future was sent to the global MP community. A total of 433 responses were analysed by professional role and by country clustered on the daily infection numbers. Results: The impact of COVID-19 was bigger in countries with high daily infection rate. The majority of MPs worked in alternation at home/on-site. Among practice changes, implementation and/or increased use of hypofractionation was the most common (47% of the respondents). Sixteen percent of respondents modified patient-specific quality assurance (QA), 21% reduced machine QA, and 25% moved machine QA to weekends/evenings. The perception of trust in leadership and team unity was reversed between management MPs (towards increased trust and unity) and clinical MPs (towards a decrease). Changes such as home-working and increased use of hypofractionation were welcomed. However, some MPs were concerned about pressure to keep negative changes (e.g. weekend work). Conclusion: COVID-19 affected MPs through changes in practice and QA procedures but also in terms of trust in leadership and team unity. Some changes were welcomed but others caused worries for the future. This report forms the basis, from a medical physics perspective, to evaluate long-lasting changes within a multi-disciplinary setting.

PurposeAlthough an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.MethodsMEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy,” “brain,” “prediction model,” and “neurocognitive impairments.” Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.ResultsOf 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n = 7) and the trail making tests (n = 4) were the most frequent outcome assessment tools. All studies used regression (n = 14 linear, n = 8 logistic, and n = 4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.ConclusionExisting models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (147) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

Background and purpose: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. Material and methods: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). Results: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and −4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: −0.1 ± 1.1 Gy and −0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. Conclusion: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.

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

Background and purpose: Head and neck (HN) radiotherapy can benefit from automatic delineation of tumor and surrounding organs because of the complex anatomy and the regular need for adaptation. The aim of this study was to assess the performance of a commercially available deep learning contouring (DLC) model on an external validation set. Materials and methods: The CT-based DLC model, trained at the University Medical Center Groningen (UMCG), was applied to an independent set of 58 patients from the Radboud University Medical Center (RUMC). DLC results were compared to the RUMC manual reference using the Dice similarity coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Craniocaudal spatial information was added by calculating binned measures. In addition, a qualitative evaluation compared the acceptance of manual and DLC contours in both groups of observers. Results: Good correspondence was shown for the mandible (DSC 0.90; HD95 3.6 mm). Performance was reasonable for the glandular OARs, brainstem and oral cavity (DSC 0.78–0.85, HD95 3.7–7.3 mm). The other aerodigestive tract OARs showed only moderate agreement (DSC 0.53–0.65, HD95 around 9 mm). The binned measures displayed the largest deviations caudally and/or cranially. Conclusions: This study demonstrates that the DLC model can provide a reasonable starting point for delineation when applied to an independent patient cohort. The qualitative evaluation did not reveal large differences in the interpretation of contouring guidelines between RUMC and UMCG observers.

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

Radiotherapy software messages (sometimes called alerts, pop-up windows, alarms, or error messages) to the user appear continuously on computer screens. These software messages sometimes require decisions to be made as to the next appropriate action. However, mainly these messages are for information only. Dealing with software messages is a well-recognized problem in healthcare and has contributed to catastrophic events both outside and within radiotherapy. The purpose of this work is to highlight the prevalence and raise awareness within the radiotherapy community of such software messages related to external beam radiation therapy procedures at the linear accelerator. Radiation Therapists (RTTs) were asked to record the type and frequency of software message over 50 fractions and for 50 different patients. The data was collected at 6 institutions in the Netherlands using linear accelerators from Elekta, Ltd. and Varian Medical Systems, Inc. Results show that linear accelerator software messages (including record and verify) occur at a rate of about 8.9 messages per patient fraction. This number of software messages is potentially impacting on patient safety as these messages range in level of importance. The impact and potential reduction of these software messages should be the focus of future research and improved implementation.

Background: Nitroglycerin is proposed as an agent to reduce tumour hypoxia by improving tumour perfusion. We investigated the potential of nitroglycerin as a radio-sensitizer in non-small cell lung cancer (NSCLC) and the potential of functional imaging for patient selection. Material and methods: Trial NCT01210378 is a single arm phase II trial, designed to detect 15% improvement in 2-year overall survival (primary endpoint) in stage IB-IV NSCLC patients treated with radical (chemo-) radiotherapy and a Transiderm-Nitro 5 patch during radiotherapy. Patients underwent dynamic contrast-enhanced CTs (DCE-CT) and HX4 (hypoxia) PET/CTs before and after nitroglycerin. Secondary endpoints were progression-free survival, toxicity and the prognostic value of tumour perfusion/hypoxia at baseline and after nitroglycerin. Results: The trial stopped after a futility analysis after 42 patients. At median follow-up of 41 months, two-year and median OS were 58% (95% CI: 44–78%) and 38 months (95% CI: 22–54 months), respectively. Nitroglycerin could not reduce tumour hypoxia. DCE-CT parameters did not correlate with OS, whereas hypoxic tumours had a worse OS (p = 0.029). Changes in high-uptake fraction of HX4 and tumour blood flow were negatively correlated (r = -0.650, p = 0.022). The heterogeneity in treatment modalities and patient characteristics combined with a small sample size made further subgroup analysis of survival results impossible. Toxicity related to nitroglyerin was limited to headache (17%) and hypotension (2.4%). Conclusion: Nitroglycerin did not improve OS of NSCLC patients treated with (chemo-)radiotherapy. A general ability of nitroglycerin to reduce hypoxia was not shown. Keywords: Nitroglycerin, HX4, NSCLC, Hypoxia, Perfusion, Mitochondria

Background and purpose: In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring. Materials and methods: Twenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R. Results: The highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = −0.38), for surface DSC with absolute adaption time (R = −0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = −0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and −0.64, respectively). Conclusion: Surface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures. Keywords: Radiotherapy, Automatic delineation, Contouring time, Time-saving, Hausdorff distance, Dice similarity coefficient, Surface DSC, Added path length

Introduction: Radiotherapy education can be very different across Europe, despite the publication of the ESTRO core curricula in 2011. The purpose of the current study is to map the different RO European education systems, to report their perceived quality and to understand what could be improved to better teach RO. Methods: An online survey consisting of 30 questions was sent to RO professionals under 40 years of age via email and social media. Clinicians, radiobiologists, physicists and radiation therapists (RTTs) were invited to answer questions regarding (1) demographics data, (2) duration, (3) organization, (4) content, (5) quality and potential improvements of national education programs. Results: Four hundred and sixty three questionnaires were received from 34 European countries. All disciplines were represented: 45% clinicians (n = 210), 29% physicists (n = 135), 24% RTTs (n = 108) and 2% radiobiologists (n = 10). Male and female participants were well-balanced in each speciality, except for radiobiologists (80% males). Median age was 31.5 years old (range 21–40). A large range of the duration of the National RO education programs was observed: median = 9 years (range: 3–15). In half of the surveyed countries the European Credit Transfer System (ECTS), that facilitates mobility for trainees, has been implemented. Participants declared only a minority of countries have implemented the ESTRO Core Curriculum (n = 5). A quarter of participants indicated that their national education program is insufficient. Conclusion: This is the first study to examine the different RO education systems in Europe. Large differences in organization and duration of national education programs have been found, along with perceived quality across Europe within each speciality. These results show the necessity of a discussion on how to move forward in this diversity of education programs and the potential contribution that the ESTRO may fulfil.

Background and purpose: Computed tomography (CT) imaging is frequently used in radiation oncology to calculate radiation dose distributions. In order to calculate doses, the CT numbers must be converted into densities by an energy dependent conversion curve. A recently developed algorithm directly reconstructs CT projection data into relative electron densities which eliminates the use of separate conversion curves for different X-ray tube potentials. Our work evaluates this algorithm for various cancer sites and shows its applicability in a clinical workflow. Materials and methods: The Gammex phantom with tissue mimicking inserts was scanned to characterize the CT number to density conversion curves. In total, 33 patients with various cancer sites were scanned using multiple tube potentials. All CT acquisitions were reconstructed with the standard filtered back-projection (FBP) and the new developed DirectDensity⢠(DD) algorithm. The mean tumor doses and the volume percentage that receives more than 95% of the prescribed dose were calculated for the planning target volume. Relevant parameters for the organs at risk for each tumor site were also calculated. Results: The relative mean dose differences between the standard 120 kVp FBP CT scan workflow and the DD CT scans (80, 100, 120 and 140 kVp) were in general less than 1% for the planned target volume and organs at risk. Conclusion: The energy independent DD algorithm allows for accurate dose calculations over a variety of body sites. This novel algorithm eliminates the tube potential specific calibration procedure and thereby simplifies the clinical radiotherapy workflow. Keywords: CT imaging, Image reconstruction, Dose calculations, Electron density reconstruction

Background and purpose: Anatomical changes in non-small cell lung cancer (NSCLC) patients may lead to unacceptable treatment results, requiring adaptive radiotherapy (ART). The method proposed in this study describes the proof-of-principle to automatically select patients eligible for ART. Materials and methods: A method was developed flagging patients potentially requiring replanning using changes in density information between the planning CT and cone-beam CT (CBCT) scan. Potential candidates were defined based on anatomical changes in the primary tumour, mediastinum and a region including the lungs. In total, CBCTs of 70 NSCLC patients were retrospectively scored by two independent experts for the need of replanning using the clinical guidelines. Expert evaluation was used as benchmark for the automatic method and accuracy was quantified using a leave-one-out cross-validation strategy. Results: The automatic method correctly selected 79%, 71%, and 89% of the pre-defined candidates based on anatomical change in the tumour, mediastinum and lungs, respectively. The false positive rate was 27%, 9%, and 19% for these regions, respectively. If a single criterion (‘flag’) per patient is sufficient for replanning, the overall sensitivity was 89% with a false positive rate of 34%. Most common reasons for false positives were changes in breathing pattern or anatomical shifts outside the evaluated region. Excluding these reasons, the false positive rate dropped to 21% and the accuracy was almost similar to expert evaluation: overall sensitivity of approx. 95% with a false positive rate below 19%. Conclusion: Our method based on daily CBCT allows automatic selection of patients that should be investigated for treatment adaptation. Keywords: Non-small cell lung cancer, Adaptive radiotherapy, Replanning, Automatic selection method, Cone-beam CT

BackgroundPrognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment.MethodsThe dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called 'cohort differences model' approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets.ResultsThe individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets.ConclusionThe results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.

Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor).Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH.Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.