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Background: Atrial fibrillation (AF) is an important side effect of thoracic Radiotherapy (RT), which may impair quality of life and survival. This study aimed to develop a prediction model for new-onset AF in patients with Non-Small Cell Lung Cancer (NSCLC) receiving RT alone or as a part of their multi-modal treatment., Patients and Methods: Patients with stage I-IV NSCLC treated with curative-intent conventional photon RT were included. The baseline electrocardiogram (ECG) was compared with follow-up ECGs to identify the occurrence of new-onset AF. A wide range of potential clinical predictors and dose-volume measures on the whole heart and six automatically contoured cardiac substructures, including chambers and conduction nodes, were considered for statistical modeling. Internal validation with optimism-correction was performed. A nomogram was made., Results: 374 patients (mean age 69 ± 10 years, 57 % male) were included. At baseline, 9.1 % of patients had AF, and 42 (11.2 %) patients developed new-onset AF. The following parameters were predictive: older age (OR=1.04, 95 % CI: 1.013-1.068), being overweight or obese (OR=1.791, 95 % CI: 1.139-2.816), alcohol use (OR=4.052, 95 % CI: 2.445-6.715), history of cardiac procedures (OR=2.329, 95 % CI: 1.287-4.215), tumor located in the upper lobe (OR=2.571, 95 % CI: 1.518-4.355), higher forced expiratory volume in 1 s (OR=0.989, 95 % CI: 0.979-0.999), higher creatinine (OR=1.008, 95 % CI: 1.002-1.014), concurrent chemotherapy (OR=3.266, 95 % CI: 1.757 to 6.07) and left atrium D max (OR=1.022, 95 % CI: 1.012-1.032). The model showed good discrimination (area under the curve = 0.80, 95 % CI: 0.76-0.84), calibration and positive net benefits., Conclusion: This prediction model employs readily available predictors to identify patients at high risk of new-onset AF who could potentially benefit from active screening and timely management of post-RT AF., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objective: To evaluate two years of clinical experience with markerless breath-hold liver stereotactic radiotherapy (SBRT) using non-invasive nasal high flow therapy (NHFT) for breath-hold prolonging and surface guidance (SGRT) for monitoring., Methods: Heated and humidified air was administered via a nasal cannula (40 L/min, 80% oxygen, 34 °C). Patients performed voluntary inspiration breath-holds with visual feedback. After a training session, 4-5 breath-hold CT scans were acquired to delineate an internal target volume (ITV) accounting for inter- and intra-breath-hold variations. Patients were treated in 3-8 fractions (7.5-20 Gy/fraction) using SGRT-controlled beam-hold. Patient setup was performed using SGRT and CBCT imaging. A post-treatment CBCT was acquired for evaluation purposes., Results: Fifteen patients started the training session and received treatment, of whom 10 completed treatment in breath-hold. Half of all 60-second CBCT scans were acquired during a single breath-hold. The average maximum breath-hold duration during treatment ranged from 47-108 s. Breath-hold ITV was on average 6.5 cm³/30% larger (range: 1.1-23.9 cm³/5-95%) than the largest GTV. Free-breathing ITV based on 4DCT scans was on average 16.9 cm³/47% larger (range: -2.3-58.7 cm3/-16-157%) than the breath-hold ITV. The average 3D displacement vector of the area around PTV for the post-treatment CBCT scans was 5.0 mm (range: 0.7-12.9 mm)., Conclusions: Liver SBRT in breath-hold using NHFT and SGRT is feasible for the majority of patients. An ITV reduction was observed compared to free-breathing treatments. To further decrease the PTV, internal anatomy-based breath-hold monitoring is desired., Advances in Knowledge: Non-invasive NHFT allows for prolonged breath-holding during surface-guided liver SBRT., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Background: Deterioration of neurocognitive function in adult patients with a primary brain tumor is the most concerning side effect of radiotherapy. This study aimed to develop and evaluate normal-tissue complication probability (NTCP) models using clinical and dose-volume measures for 6-month, 1-year, and 2-year Neurocognitive Decline (ND) postradiotherapy., Methods: A total of 219 patients with a primary brain tumor treated with radical photon and/or proton radiotherapy (RT) between 2019 and 2022 were included. Controlled oral word association test, Hopkins verbal learning test-revised, and trail making test were used to objectively measure ND. A comprehensive set of potential clinical and dose-volume measures on several brain structures were considered for statistical modeling. Clinical, dose-volume and combined models were constructed and internally tested in terms of discrimination (area under the curve, AUC), calibration (mean absolute error, MAE), and net benefit., Results: Fifty percent, 44.5%, and 42.7% of the patients developed ND at 6-month, 1-year, and 2-year time points, respectively. The following predictors were included in the combined model for 6-month ND: age at radiotherapy > 56 years (OR = 5.71), overweight (OR = 0.49), obesity (OR = 0.35), chemotherapy (OR = 2.23), brain V20 Gy ≥ 20% (OR = 3.53), brainstem volume ≥ 26 cc (OR = 0.39), and hypothalamus volume ≥ 0.5 cc (OR = 0.4). Decision curve analysis showed that the combined models had the highest net benefits at 6-month (AUC = 0.79, MAE = 0.021), 1-year (AUC = 0.72, MAE = 0.027), and 2-year (AUC = 0.69, MAE = 0.038) time points., Conclusions: The proposed NTCP models use easy-to-obtain predictors to identify patients at high risk of ND after brain RT. These models can potentially provide a base for RT-related decisions and post-therapy neurocognitive rehabilitation interventions., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This publication is part of the project “Making radiotherapy sustainable” with project number 10070012010002 of the Highly Specialised Care & Research programme (TZO programme) which is (partly) financed by the Netherlands Organisation for Health Research and Development (ZonMw)., (© 2024 The Authors.)

Background and Purpose: Brain tumors are in general treated with a maximal safe resection followed by radiotherapy of remaining tumor including the resection cavity (RC) and chemotherapy. Anatomical changes of the RC during radiotherapy can have impact on the coverage of the target volume. The aim of the current study was to quantify the potential changes of the RC and to identify risk factors for RC changes., Materials and Methods: Sixteen patients treated with pencil beam scanning proton therapy between October 2019 and April 2020 were retrospectively analyzed. The RC was delineated on pre-treatment computed tomography (CT) and magnetic resonance imaging, and weekly CT-scans during treatment. Isotropic expansions were applied to the pre-treatment RC (1-5 mm). The percentage of volume of the RC during treatment within the expanded pre-treatment volumes was quantified. Potential risk factors (volume of RC, time interval surgery-radiotherapy and relationship of RC to the ventricles) were evaluated using Spearman's rank correlation coefficient., Results: The average variation in relative RC volume during treatment was 26.1% (SD 34.6%). An expansion of 4 mm was required to cover > 95% of the RC volume in > 90% of patients. There was a significant relationship between the absolute volume of the pre-treatment RC and the volume changes during treatment (Spearman's ρ = - 0.644; p = 0.007)., Conclusion: RCs are dynamic after surgery. Potentially, an additional margin in brain cancer patients with an RC should be considered, to avoid insufficient target coverage. Future research on local recurrence patterns is recommended., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas., Methods: Image and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests., Results: CT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88-0.95) and 0.84 (0.77-0.89) respectively, as well as lowest MSHD: 0.27 (0.22-0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔD max,0.03cc -parameters when splitting the data in ≤ 4 cm and > 4 cm OAR-distance (p < 0.001)., Conclusion: MR-based DLC and CT-based atlas-contouring enable high-quality segmentation. It was shown that a combination of both CT- and MR-autocontouring models results in the best quality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

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 (LET d ), and the product between dose and LET d (DLET d ) 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 LET d , DLET d , 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 LET d visualization. The relationship between physical dose and LET d and the frequency of LET d hotspots were investigated., Results: The phenomenological relative biological effectiveness model presented consistently higher dose values. For lower dose thresholds, the LET d distribution was steered toward higher values related to low treatment doses. Differences up to 26.0% were found according to the threshold. Maximum LET d values were identified in the brain, periventricular space, and ventricles. An inverse relationship between LET d and dose was observed. Frequency information to the domain of dose and LET d allowed for the identification of clusters, which steer the mean LET d values, and the identification of higher, but sparse, LET d 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 DLET d or dose × LET d during treatment planning could allow for clinicians to make informed decisions., (© 2022 The Authors.)

Introduction: The various rectal endoluminal radiation techniques all have steep, but different, dose gradients. In rectal contact brachytherapy (CXB) doses are typically prescribed and reported to the applicator surface and not to the gross tumor volume (GTV), clinical target volume (CTV) or organs at risk (OAR), which is crucial to understand tumor response and toxicity rates. To quantify the above-described problem, we performed a dose modeling study using a fixed prescription dose at the surface of the applicator and varied tumor response scenarios., Methods: Endorectal ultrasound-based 3D-volume-models of rectal tumors and the rectal wall were used to simulate the delivered dose to GTV, CTV and the rectal wall layers, assuming treatment with Maastro HDR contact applicator for rectal cancer with a fixed prescription dose to the applicator surface (equivalent to 3 × 30 Gy CXB) and various response scenarios., Results: An identical prescribed dose to the surface of the applicator resulted in a broad range of doses delivered to the GTV, CTV and the uninvolved intestinal wall. For example, the equieffective dose in 2 Gy per fraction (EQD2) D90% of the GTV varied between 63 and 231 Gy, whereas the EQD2 D2cc of the rectal wall varied between 97 and 165 Gy., Conclusion: Doses prescribed at the surface are not representative of the dose received by the tumor and the bowel wall. This stresses the relevance of dose reporting and prescription to GTV and CTV volumes and OAR in order to gain insight between delivered dose, local control and toxicity and to optimize treatment protocols., (Copyright © 2022 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.)

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 (CTV manual ) and deformably mapped target contours (CTV auto ). It was compared if CTV manual and CTV auto 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 CTV manual , but all constraints were satisfied for CTV auto ), 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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.)

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., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Maastro has licensing and research agreements with Mirada Medical Ltd., (© 2022 The Authors.)

Background: The definition of the clinical target volume (CTV) for post-operative radiotherapy (PORT) for thymoma is largely unexplored. The aim of this study was to analyze the difference in CTV delineation between radiation oncologists (RTO) and surgeons., Methods: This retrospective multi-center study enrolled 31 patients who underwent PORT for a thymoma from five hospitals. Three CTVs were delineated per patient: one CTV by the RTO, one CTV by the surgeon (blinded to the results of the RTO) and a joint CTV after collaboration. Volumes (cm 3 ), Hausdorff distances (HD) and Dice similarity coefficients (DSC) were analyzed., Results: RTO delineated significantly bigger CTVs than surgeons (mean: 93.9 ± 63.1, versus 57.9 ± 61.3 cm 3 , p = 0.003). Agreement was poor between RO and surgeons, with a low mean DSC (0.34 ± 0.21) and high mean HD of 4.5 (±2.2) cm. Collaborative delineation resulted in significantly smaller volumes compared to RTO (mean 57.1 ± 58.6 cm 3 , p < 0.001). A mean volume of 18.9 (±38.1) cm 3 was included in joint contours, but missed by RTO. Conversely, a mean volume of 55.7 (±39.9) cm 3 was included in RTO's delineations, but not in the joint delineations., Conclusions: To the best of our knowledge, this is the first study investigating CTV definition in thymoma. We demonstrated a significant variability between RTO and surgeons. Joint delineation prompted revisions in smaller CTV as well as favoring the surgeons' judgement, suggesting that surgeons provided relevant insight into other risk areas than RTO. We recommend a multidisciplinary approach to PORT for thymomas in clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

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 D max (3.4 ± 6.8 Gy) and lowest for Lungs-GTV D mean (0.3 ± 0.4 Gy). The effect of contour variation on treatment plan evaluation was highest for Heart D max (6.0 ± 13.4 Gy) and Esophagus D max (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 D mean , 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 D max -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 D mean , we recommend delineation errors to be corrected when the heart overlaps with the PTV. For D max -parameters, we recommend checking contours if the distance is close to PTV (<5 cm). For the lungs, only obvious large errors need to be adjusted., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Intensity-modulated proton therapy (IMPT) for lung tumors with a large tumor movement is challenging due to loss of robustness in the target coverage. Often an upper cut-off at 5-mm tumor movement is used for proton patient selection. In this study, we propose (1) a robust and easily implementable treatment planning strategy for lung tumors with a movement larger than 5 mm, and (2) a four-dimensional computed tomography (4DCT) robust evaluation strategy for evaluating the dose distribution on the breathing phases., Materials and Methods: We created a treatment planning strategy based on the internal target volume (ITV) concept (aim 1). The ITV was created as a union of the clinical target volumes (CTVs) on the eight 4DCT phases. The ITV expanded by 2 mm was the target during robust optimization on the average CT (avgCT). The clinical plan acceptability was judged based on a robust evaluation, computing the voxel-wise min and max (VWmin/max) doses over 28 error scenarios (range and setup errors) on the avgCT. The plans were created in RayStation (RaySearch Laboratories, Stockholm, Sweden) using a Monte Carlo dose engine, commissioned for our Mevion S250i Hyperscan system (Mevion Medical Systems, Littleton, MA, USA). We developed a new 4D robust evaluation approach (4DRobAvg; aim 2). The 28 scenario doses were computed on each individual 4DCT phase. For each scenario, the dose distributions on the individual phases were deformed to the reference phase and combined to a weighted sum, resulting in 28 weighted sum scenario dose distributions. From these 28 scenario doses, VWmin/max doses were computed. This new 4D robust evaluation was compared to two simpler 4D evaluation strategies: re-computing the nominal plan on each individual 4DCT phase (4DNom) and computing the robust VWmin/max doses on each individual phase (4DRobInd). The treatment planning and dose evaluation strategies were evaluated for 16 lung cancer patients with tumor movement of 4-26 mm., Results: The ratio of the ITV and CTV volumes increased linearly with the tumor amplitude, with an average ratio of 1.4. Despite large ITV volumes, a clinically acceptable plan fulfilling all target and organ at risk (OAR) constraints was feasible for all patients. The 4DNom and 4DRobInd evaluation strategies were found to under- or overestimate the dosimetric effect of the tumor movement, respectively. 4DRobInd showed target underdosage for five patients, not observed in the robust evaluation on the avgCT or in 4DRobAvg. The accuracy of dose deformation used in 4DRobAvg was quantified and found acceptable, with differences for the dose-volume parameters below 1 Gy in most cases., Conclusion: The proposed ITV-based planning strategy on the avgCT was found to be a clinically feasible approach with adequate tumor coverage and no OAR overdosage even for large tumor movement. The new proposed 4D robust evaluation, 4DRobAvg, was shown to give an easily interpretable understanding of the effect of respiratory motion dose distribution, and to give an accurate estimate of the dose delivered in the different breathing phases., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background and Purpose: Reducing breathing motion in radiotherapy (RT) is an attractive strategy to reduce margins and better spare normal tissues. The objective of this prospective study (NCT03729661) was to investigate the feasibility of irradiation of non-small cell lung cancer (NSCLC) with visually guided moderate deep inspiration breath-hold (IBH) using nasal high-flow therapy (NHFT)., Material and Methods: Locally advanced NSCLC patients undergoing photon RT were given NHFT with heated humidified air (flow: 40 L/min with 80% oxygen) through a nasal cannula. IBH was monitored by optical surface tracking (OST) with visual feedback. At a training session, patients had to hold their breath as long as possible, without and with NHFT. For the daily cone beam CT (CBCT) and RT treatment in IBH, patients were instructed to keep their BH as long as it felt comfortable. OST was used to analyze stability and reproducibility of the BH, and CBCT to analyze daily tumor position. Subjective tolerance was measured with a questionnaire at 3 time points., Results: Of 10 included patients, 9 were treated with RT. Seven (78%) completed the treatment with NHFT as planned. At the training session, the mean BH length without NHFT was 39 s (range 15-86 s), and with NHFT 78 s (range 29-223 s) ( p  = .005). NHFT prolonged the BH duration by a mean factor of 2.1 (range 1.1-3.9s). The mean overall stability and reproducibility were within 1 mm. Subjective tolerance was very good with the majority of patients having no or minor discomfort caused by the devices. The mean inter-fraction tumor position variability was 1.8 mm (-1.1-8.1 mm;SD 2.4 mm)., Conclusion: NHFT for RT treatment of NSCLC in BH is feasible, well tolerated and significantly increases the breath-hold duration. Visually guided BH with OST is stable and reproducible. We therefore consider this an attractive patient-friendly approach to treat lung cancer patients with RT in BH.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2019 The Authors.)

We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge,14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness.Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only.