National societies' needs as assessed by the ESTRO National Societies Committee survey: A European perspective

Purpose: To determine how ESTRO can collaborate with Radiation Oncology National Societies (NS) according to its mission and values, and to define the new roadmap to strengthen the NS network role in the forthcoming years. Materials and methods: The ESTRO NS committee launched a survey addressed to all European National Societies, available online from June 5th to October 30th 2018. Questions were divided into three main sections: (1) general information about NS; (2) relevant activities (to understand the landscape of each NS context of action); (3) relevant needs (to understand how ESTRO can support the NS). Eighty-nine European NS were invited to participate. Respondents were asked to rank ESTRO milestones in order of importance, indicating the level of priority to their society. Results: A total of 58 out of 89 NS (65.2%) from 31 European countries completed the questionnaire. The majority of NS ranked “Optimal patient care to cure cancer and to reduce treatment-related toxicity” as the highest level of priority. This aligns well with the ESTRO vision 2030 “Optimal health for all together.” NS also indicated a high need for more consensus guidelines and exchange of best practices, access to high quality accredited education, implementation of the ESTRO School Core Curriculum at the national level, and defining quality indicators and standard in Radiation Oncology, improved communication and increased channelling of information. Conclusion: The results of this survey will be used to strengthen the relations between ESTRO and European NS to promote and develop initiatives to improve cancer care

Learning radiation oncology in Europe: Results of the ESTRO multidisciplinary survey

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

The role of alexithymia and empathy on radiation therapists’ professional quality of life

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 < 0 0.001), and the individual's perception of being valued by supervisor (β = −0.29, p < 0.001). With respect to CS, the final model included TAS-20 total score (β = −0.33, p < 0.001), the Empatic Concern domain (β = 0.23, p < 0.001) of the IRI questionnaire and the individual's perception of being valued by colleagues (β = 0.22, p < 0.001). Conclusions: Alexithymia increased the likelyhood to experience burnout and negatively affected the professional quality of life amongst RTTs working in oncology. Empathy resulted in higher professional fulfillment together with collegaues’ appreciation. These results may be used to benchmark preventing strategies and implement organization-direct and/or individual-directed interventions

Professional quality of life and burnout among medical physicists working in radiation oncology: The role of alexithymia and empathy

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 (TAS20); 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 < 0 0.001), increased likelihood for STS (β = 0.26, p < 0 0.001) and burnout (β = 0.47, p < 0 0.001). With respect to empathy, the ‘Empatic Concern’ subscale of the IRI was found to be a significant predictor for increased CS (β = 0.19, p = 0 0.001) and increased STS (β = 0.19, p < 0 0.001), without significant correlation with burnout. The individual’s perception of being valued by own’s supervisor was correlated to increased CS (β = 0.23, p < 0.001), and decreased burnout (β = −0.29, p < 0.001). Conclusions: Alexithymic personality trait increased the likelihood to develop burnout

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

Evaluation of patients’ engagement in radiation therapy safety

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