BackgroundMagnetic resonance-guided high intensity focused ultrasound (MR-HIFU) has recently emerged as an effective treatment option for painful bone metastases. We describe here the first experience with volumetric MR-HIFU for palliative treatment of painful bone metastases and evaluate the technique on three levels: technical feasibility, safety, and initial effectiveness.MethodsIn this observational cohort study, 11 consecutive patients (7 male and 4 female; median age, 60 years; age range, 53–86 years) underwent 13 treatments for 12 bone metastases. All patients exhibited persistent metastatic bone pain refractory to the standard of care. Patients were asked to rate their worst pain on an 11-point pain scale before treatment, 3 days after treatment, and 1 month after treatment. Complications were monitored. All data were prospectively recorded in the context of routine clinical care. Response was defined as a ≥2-point decrease in pain at the treated site without increase in analgesic intake. Baseline pain scores were compared to pain scores at 3 days and 1 month using the Wilcoxon signed-rank test. For reporting, the STROBE guidelines were followed.ResultsNo treatment-related major adverse events were observed. At 3 days after volumetric MR-HIFU ablation, pain scores decreased significantly (p = 0.045) and response was observed in a 6/11 (55%) patients. At 1-month follow-up, which was available for nine patients, pain scores decreased significantly compared to baseline (p = 0.028) and 6/9 patients obtained pain response (overall response rate 67% (95% confidence interval (CI) 35%–88%)).ConclusionsThis is the first study reporting on the volumetric MR-HIFU ablation for painful bone metastases. No major treatment-related adverse events were observed during follow-up. The results of our study showed that volumetric MR-HIFU ablation for painful bone metastases is technically feasible and can induce pain relief in patients with metastatic bone pain refractory to the standard of care. Future research should be aimed at standardization of the treatment procedures and treatment of larger numbers of patients to assess treatment effectiveness and comparison to the standard of care.

Key Results 1. A model using baseline patient characteristics, laboratory markers, and chest radiography can predict short-term critical illness in hospitalized patients with COVID-19, with an internally validated AUC = 0.77. 2. At an example model risk threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. 3. A risk calculator has been made available for download: Dutch COVID-19 risk model (https://docs.google.com/spreadsheets/d/1eFrdHxnOA-M_P-ijxnC2u30qk7IhMVV6YvHvJhrZ8Ws/edit#gid=0) (see Appendix E2).

Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

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Background The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students’ attitudes to online learning to provide a broad scientific basis to guide future development of medical education. Methods Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education. Results The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching. Conclusion This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new “online era” in medical education.

Focused ultrasound surgery (FUS), in particular magnetic resonance guided FUS (MRgFUS), is an emerging non-invasive thermal treatment modality in oncology that has recently proven to be effective for the palliation of metastatic bone pain. A consensus panel of internationally recognised experts in focused ultrasound critically reviewed all available data and developed consensus statements to increase awareness, accelerate the development, acceptance and adoption of FUS as a treatment for painful bone metastases and provide guidance towards broader application in oncology. In this review, evidence-based consensus statements are provided for (1) current treatment goals, (2) current indications, (3) technical considerations, (4) future directions including research priorities, and (5) economic and logistical considerations.

This is a continuous paper on limitations of commonly used metrics in image analysis. The current version discusses segmentation metrics only, while future versions will also include metrics for classification and detection tasks. For missing references, use cases, other comments or questions, please contact

Objectives Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. Methods Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. Results The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). Conclusions Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. Key Points • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.