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Background & purpose: Magnetic resonance guided adaptive radiotherapy (MRgART) enables hypofractionated prostate radiotherapy by daily contour adaptation. The MRgART workflow, however, is labour intensive and in many institutes still requires the presence of the radiation oncologist (RO). Transferring the online contour adaptation task to the radiation therapist (RTT) will release the clinician from attending each treatment fraction making MRgRT more efficient and cost effective. In this study we investigate the viability of RTT-led prostate MRgART on a low-field MR-linac, by assessing the interobserver variations of RTT- and RO-generated CTV contour adaptations as well as the resulting dosimetric effects. Materials & methods: Four RTTs and four ROs performed CTV contour adaptations on first fraction data in ten patients. Delineations were compared against a gold standard contour using target volume, Dice similarity coefficient (DSC), and 95th percentile Hausdorff distance. In addition, a dosimetric evaluation was performed on all first fractions by performing plan adaptations based on all RTT contour adaptation and comparing these to the clinically delivered plan. Finally, a full-treatment simulation was performed in four patients to investigate the dosimetric effects of the RTTs’ contour adaptations throughout an entire treatment. Results: RTTs with no experience in prostate delineation prior to this study spent more time on CTV contour adaptations. The geometric and dosimetric analyses, however, showed no statistically significant differences between both groups. Conclusions: This study confirmed that RTTs perform similarly to ROs in carrying out online contour adaptations. These results indicate the feasibility of initiating a transition in contour adaptation tasks from ROs to RTTs.

Anti-bias interventions do not always have the intended results and can even backfire. In light of research on the psychology of morality, we examined whether confronting people with evidence of their own (group's) bias causes a (psychophysiological) threat response, and how to overcome this. We focused on an intervention addressing gender bias in teacher evaluations. After assessing their own teaching evaluations, we presented student research participants (N = 101; 71.3% female), in Part 1 of the intervention, with evidence of bias displayed in such teaching evaluations. This evidence either did (self-implied condition) or did not (self not-implied condition) include participants' own ostensibly biased evaluations. In Part 2 of the intervention, we asked participants to reflect on the issue of gender bias, and compared the impact of two experimental instructions. In the promotion condition, instructions referred to emphasizing how the university could try to achieve the ideal of promoting fair and just evaluations of teachers. In the prevention condition, instructions referred to highlighting the university's obligation to prevent unfair and unjust teacher evaluations. While participants verbally reflected on the intervention, during both phases (in Part 1 and Part 2) we measured their psychophysiological responses using indices of cardiovascular 'threat vs. challenge'. Then, we used self-report measures to examine participants' explicit responses to the different parts of the intervention. Results revealed that implicating the self in the occurrence of bias (Part 1) raises a psychophysiological threat response. However, emphasizing the future ideal of promoting fair evaluations of teachers (rather than the obligation of preventing biased evaluations; Part 2) resulted in a psychophysiological challenge response and increased perceived coping abilities to combat such bias. The implications of these findings are discussed.

This study examined how employees experience humanness in organizations related to leadership behavior. More specifically, it was aimed to define what experienced humanness is from the perspective of employees, and which leadership behaviors employees perceive to contribute to these experiences of organizational humanness. To fulfill this aim, an exploratory grounded theory study was conducted, in which 13 employees were interviewed until saturation was attained, investigating their experiences in organizations that relate to humanness and examples of leadership behavior that influence it. Three overarching categories of experienced humanness were identified in organizations i.e., bounded space, attentive care, and human connection. Furthermore, eight specific sets of leadership behaviors appear to contribute to the experience of humanness. The findings were compared to the scientific literature and led to the conclusion that the findings yield unique elements, (e.g., fostering human connection within a team) that have not been covered in previous conceptualizations.

ObjectiveThis study explores the feasibility of ex-vivo high-field magnetic resonance (MR) imaging to create digital a three-dimensional (3D) representations of tongue cancer specimens, referred to as the “MR-based digital specimen” (MR-DS). The aim was to create a method to assist surgeons in identifying and localizing inadequate resection margins during surgery, a critical factor in achieving locoregional control.MethodsFresh resection specimens of nine tongue cancer patients were imaged in a 7 Tesla small-bore MR, using a high-resolution multislice and 3D T2-weighted Turbo Spin Echo. Two independent radiologists (R1 and R2) outlined the tumor and mucosa on the MR-images whereafter the outlines were configured to an MR-DS. A color map was projected on the MR-DS, mapping the inadequate margins according to R1 and R2. We compared the hematoxylin-eosin-based digital specimen (HE-DS), which is a histopathological 3D representation derived from HE stained sections, with its corresponding MR-images. In line with conventional histopathological assessment, all digital specimens were divided into five anatomical regions (anterior, posterior, craniomedial, caudolateral and deep central). Over- and underestimation 95th-percentile Hausdorff-distances were calculated between the radiologist- and histopathologist-determined tumor outlines. The MR-DS’ diagnostic accuracy for inadequate margin detection (i.e. sensitivity and specificity) was determined in two ways: with conventional histopathology and HE-DS as reference.ResultsUsing conventional histopathology as a reference, R1 achieved 77% sensitivity and 50% specificity, while R2 achieved 65% sensitivity and 57% specificity. When referencing to the HE-DS, R1 achieved 94% sensitivity and 61% specificity, while R2 achieved 88% sensitivity and 71% specificity. Range of over- and underestimation 95HD was 0.9 mm - 11.8 mm and 0.0 mm - 5.3 mm, respectively.ConclusionThis proof of concept for volumetric assessment of resection margins using MR-DSs, demonstrates promising potential for further development. Overall, sensitivity is higher than specificity for inadequate margin detection, because of the radiologist’s tendency to overestimate tumor size.

General anesthesia is routinely used in endovascular thrombectomy procedures, for which volatile gas and/or intravenous propofol are recommended. Emerging evidence suggests propofol may have superior effects on disability and/or mortality rates, but a mode-of-action underlying these class-specific effects remains unknown. Here, a moderate isoflurane or propofol dosage on experimental stroke outcomes was retrospectively compared using serial multiparametric MRI and behavioral testing. Adult male rats (N = 26) were subjected to 90-min filament-induced transient middle cerebral artery occlusion. Diffusion-, T2- and perfusion-weighted MRI was performed during occlusion, 0.5 h after recanalization, and four days into the subacute phase. Sequels of ischemic damage—blood–brain barrier integrity, cerebrovascular reactivity and sensorimotor functioning—were assessed after four days. While size and severity of ischemia was comparable between groups during occlusion, isoflurane anesthesia was associated with larger lesion sizes and worsened sensorimotor functioning at follow-up. MRI markers indicated that cytotoxic edema persisted locally in the isoflurane group early after recanalization, coinciding with burgeoning vasogenic edema. At follow-up, sequels of ischemia were further aggravated in the post-ischemic lesion, manifesting as increased blood–brain barrier leakage, cerebrovascular paralysis and cerebral hyperperfusion. These findings shed new light on how isoflurane, and possibly similar volatile agents, associate with persisting injurious processes after recanalization that contribute to suboptimal treatment outcome.

Anti-bias interventions do not always have the intended results and can even backfire. In light of research on the psychology of morality, we examined whether confronting people with evidence of their own (group's) bias causes a (psychophysiological) threat response, and how to overcome this. We focused on an intervention addressing gender bias in teacher evaluations. After assessing their own teaching evaluations, we presented student research participants (N = 101; 71.3% female), in Part 1 of the intervention, with evidence of bias displayed in such teaching evaluations. This evidence either did (self-implied condition) or did not (self not-implied condition) include participants' own ostensibly biased evaluations. In Part 2 of the intervention, we asked participants to reflect on the issue of gender bias, and compared the impact of two experimental instructions. In the promotion condition, instructions referred to emphasizing how the university could try to achieve the ideal of promoting fair and just evaluations of teachers. In the prevention condition, instructions referred to highlighting the university's obligation to prevent unfair and unjust teacher evaluations. While participants verbally reflected on the intervention, during both phases (in Part 1 and Part 2) we measured their psychophysiological responses using indices of cardiovascular 'threat vs. challenge'. Then, we used self-report measures to examine participants' explicit responses to the different parts of the intervention. Results revealed that implicating the self in the occurrence of bias (Part 1) raises a psychophysiological threat response. However, emphasizing the future ideal of promoting fair evaluations of teachers (rather than the obligation of preventing biased evaluations; Part 2) resulted in a psychophysiological challenge response and increased perceived coping abilities to combat such bias. The implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively. Methods: For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1–4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale. Results: Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections. Conclusions: A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer.

Abstract Background Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. Methods For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. Results The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. Conclusions Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.

Exposure to conflict‐related violence is one of the most harmful consequences of living amidst a violent intergroup conflict. While its potential significance is profound, the impact of exposure to conflict‐related violence on intergroup empathy remains largely unexplored. The current paper aims to address this gap by proposing political ideology as a potential moderator of the exposure‐empathy link. Three studies conducted among Jewish‐Israeli participants reveal an interactive effect of exposure to conflict‐related violence and political ideology on intergroup empathy. In Study 1, high (vs. low) exposure to conflict‐related violence was associated with decreased empathy among rightists—but increased empathy among leftists—towards individual outgroup members. In Study 2, political ideology also moderated the exposure‐empathy relationship: Bereaved (vs. non‐bereaved) rightists exhibited significantly less intergroup empathy towards both an individual bereaved outgroup member and the outgroup as a whole, an effect not found among leftists. Study 3 revealed a similar, albeit marginally significant, interactive effect of exposure to conflict‐related violence and political ideology on empathy towards individuals living in a different violent conflict. Finally, an internal meta‐analysis provided further, more robust evidence for these effects. We discuss theoretical and practical implications of the findings and suggest future directions for research on this important issue. [ABSTRACT FROM AUTHOR]