Search

The detection and characterization of coronary artery stenosis and atherosclerosis using imaging tools are key for clinical decision-making in patients with known or suspected coronary artery disease. In this regard, imaging-based quantification can be improved by choosing the most appropriate imaging modality for diagnosis, treatment and procedural planning. In this Consensus Statement, we provide clinical consensus recommendations on the optimal use of different imaging techniques in various patient populations and describe the advances in imaging technology. Clinical consensus recommendations on the appropriateness of each imaging technique for direct coronary artery visualization were derived through a three-step, real-time Delphi process that took place before, during and after the Second International Quantitative Cardiovascular Imaging Meeting in September 2022. According to the Delphi survey answers, CT is the method of choice to rule out obstructive stenosis in patients with an intermediate pre-test probability of coronary artery disease and enables quantitative assessment of coronary plaque with respect to dimensions, composition, location and related risk of future cardiovascular events, whereas MRI facilitates the visualization of coronary plaque and can be used in experienced centres as a radiation-free, second-line option for non-invasive coronary angiography. PET has the greatest potential for quantifying inflammation in coronary plaque but SPECT currently has a limited role in clinical coronary artery stenosis and atherosclerosis imaging. Invasive coronary angiography is the reference standard for stenosis assessment but cannot characterize coronary plaques. Finally, intravascular ultrasonography and optical coherence tomography are the most important invasive imaging modalities for the identification of plaques at high risk of rupture. The recommendations made in this Consensus Statement will help clinicians to choose the most appropriate imaging modality on the basis of the specific clinical scenario, individual patient characteristics and the availability of each imaging modality.

The aCORN experiment measures the electron-antineutrino $a$-coefficient in free neutron decay. We update the previous aCORN results to include radiative and recoil corrections to first order. The corrected combined result is $\langle a \rangle = -0.10859 \pm .00125\, ({\rm stat}) \pm 0.00133\, ({\rm sys})$, an increase in magnitude of 0.7 % compared to the overall relative standard uncertainty of 1.7 %, which is unchanged. The corresponding corrected result for the ratio of weak coupling constants $\lambda = G_A/G_V$ is $\lambda = -1.2737 \pm 0.0061$. This improves agreement with previous $a$-coefficient experiments, in particular the 2020 aSPECT result.

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench – the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection-Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge., Journal of Advances in Modeling Earth Systems, 14 (10), ISSN:1942-2466

A Correction to this paper has been published

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. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory. ispartof: EUROPEAN RADIOLOGY vol:31 issue:8 pages:6001-6012 ispartof: location:Germany status: published

Objectives: The concept of recovery has exerted considerable traction in mental health services for adults of working age, but less so in older people’s mental health services. The aim of this study was to evaluate the feasibility of a staff-level recovery intervention in older people’s mental health services.Method: The study used a mixed-method pre-post design. The study took place in NHS older people’s mental health services, UK. Staff participants were multi-disciplinary mental health team members from the same service. The intervention was a manualised staff-level recovery intervention called the Older Adults Recovery Intervention (OARI). Measurement included the Recovery Knowledge Inventory and the Recovery Attitudes Questionnaire (RAQ-7) as well as fidelity data and in-depth qualitative interviews.Results: OARI was delivered to 204 staff in 15 clinical teams. There was a statistically significant change towards improvement in four of the six recovery attitude and knowledge sub-scales. There were positive findings in change in practice at individual level, but not at team level. A number of context barriers were identified leading to the intervention not being delivered as intended.Conclusions: Further development of OARI will involve a clearer distinction about the practice implications for service users with dementia versus functional illnesses, a stronger focus on implementation support, more use of evidence in training materials and a tailoring of context to meet professional group training needs. Overall, this study contributes novel data to the evidence base for recovery within older people’s mental health services.

The original version of this article, published on 16 December 2019, unfortunately contained two mistakes.

To implement detailed EU cardiac computed tomography angiography (CCTA) quality criteria in the multicentre DISCHARGE trial (FP72007-2013, EC-GA 603266), we reviewed image quality and adherence to CCTA protocol and to the recommendations of invasive coronary angiography (ICA) in a pilot study.From every clinical centre, imaging datasets of three patients per arm were assessed for adherence to the inclusion/exclusion criteria of the pilot study, predefined standards for the CCTA protocol and ICA recommendations, image quality and non-diagnostic (NDX) rate. These parameters were compared via multinomial regression and ANOVA. If a site did not reach the minimum quality level, additional datasets had to be sent before entering into the final accepted database (FADB).We analysed 226 cases (150 CCTA/76 ICA). The inclusion/exclusion criteria were not met by 6 of the 226 (2.7%) datasets. The predefined standard was not met by 13 of 76 ICA datasets (17.1%). This percentage decreased between the initial CCTA database and the FADB (multinomial regression, 53 of 70 vs 17 of 75 [76%] vs [23%]). The signal-to-noise ratio and contrast-to-noise ratio of the FADB did not improve significantly (ANOVA, p = 0.20; p = 0.09). The CTA NDX rate was reduced, but not significantly (initial CCTA database 15 of 70 [21.4%]) and FADB 9 of 75 [12%]; p = 0.13).We were able to increase conformity to the inclusion/exclusion criteria and CCTA protocol, improve image quality and decrease the CCTA NDX rate by implementing EU CCTA quality criteria and ICA recommendations.• Failure to meet protocol adherence in cardiac CTA was high in the pilot study (77.6%). • Image quality varies between sites and can be improved by feedback given by the core lab. • Conformance with new EU cardiac CT quality criteria might render cardiac CTA findings more consistent and comparable.