A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; 196: 1046 - 1054.

A Standard Framework for Evaluating Large Health Care Data and Related Resources

Since 2000, the availability and use of large health care data and related resources for conducting surveillance, research, and evaluations to guide clinical and public health decision-making has increased rapidly. These trends have been related to transformations in health care information technology and public as well as private-sector efforts for collecting, compiling, and supplying large volumes of data. This growing collection of robust and often timely data has enhanced the capability to increase the knowledge base guiding clinical and public health activities and also has increased the need for effective tools to assess the attributes of these resources and identify the types of scientific questions they are best suited to address. This

MMWR supplement presents a standard framework for evaluating large health care data and related resources, including constructs, criteria, and tools that investigators and evaluators can apply and adapt.

Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology

Data-driven machine learning in medical research and diagnostics needs large-scale datasets curated by clinical experts. The generation of large datasets can be challenging in terms of resource consumption and time effort, while generalizability and validation of the developed models significantly benefit from variety in data sources. Training algorithms on smaller decentralized datasets through federated learning can reduce effort, but require the implementation of a specific and ambitious infrastructure to share data, algorithms and computing time. Additionally, it offers the opportunity of maintaining and keeping the data locally. Thus, data safety issues can be avoided because patient data must not be shared. Machine learning models are trained on local data by sharing the model and through an established network. In addition to commercial applications, there are also numerous academic and customized implementations of network infrastructures available. The configuration of these networks primarily differs, yet adheres to a standard framework composed of fundamental components. In this technical note, we propose basic infrastructure requirements for data governance, data science workflows, and local node set-up, and report on the advantages and experienced pitfalls in implementing the local infrastructure with the German Radiological Cooperative Network initiative as the use case example. We show how the infrastructure can be built upon some base components to reflect the needs of a federated learning network and how they can be implemented considering both local and global network requirements. After analyzing the deployment process in different settings and scenarios, we recommend integrating the local node into an existing clinical IT infrastructure. This approach offers benefits in terms of maintenance and deployment effort compared to external integration in a separate environment (e.g., the radiology department). This proposed groundwork can be taken as an exemplary development guideline for future applications of federated learning networks in clinical and scientific environments.

[The German Network of University Medicine: technical and organizational approaches for research data platforms]

The Network University Medicine (NUM) consists of 36 university clinics in Germany. It was set up to coordinate COVID-19 university medicine research activities on a national level. This required, among other things, common infrastructures for the collection, storage, and use of medical research data. These infrastructures were not available in the required form when the NUM started in April 2020. Medical research data are extremely heterogeneous and reach far beyond "real world data" from patient care. There was no "one size fits all" solution, so NUM built five infrastructures for different types of data, different ways of obtaining data, and different data origination settings. To prevent the creation of new data silos, all five infrastructures operate based on FAIR principles (findable, accessible, interoperable, reusable). In addition, NUM is implementing an overarching governance framework to manage the evolution of these five infrastructures. The article describes the current state of development and possible perspectives with a strong focus on technical and organizational aspects.

Pulmonary thromboembolic events in COVID-19-A systematic literature review

Pulmonary thromboembolic events have been linked to coronavirus disease 2019 (COVID-19), but their incidence and long-term sequelae remain unclear. We performed a systematic literature review to investigate the incidence of pulmonary embolism (PE), microthrombi, thrombosis in situ (thromboinflammatory disease), and chronic thromboembolic pulmonary hypertension (CTEPH) during and after COVID-19. PubMed and the World Health Organization Global Research Database were searched on May 7, 2021. Hospital cohort and database studies reporting data for ≥1000 patients and autopsy studies reporting data for ≥20 patients were included. Results were summarized descriptively. We screened 1438 records and included 41 references (32 hospital/database studies and 9 autopsy studies). The hospital/database studies reported the incidence of PE but not CTEPH, microthrombi, or thromboinflammatory disease. PE incidence varied widely (0%-1.1% of outpatients, 0.9%-8.2% of hospitalized patients, and 1.8%-18.9% of patients in intensive care). One study reported PE events occurring within 45 days after hospital discharge (incidence in discharged patients: 0.2%). Segmental arteries were generally the most common location for PE. In autopsy studies, PE, thromboinflammatory disease, and microthrombi were reported in 6%-23%, 43%-100%, and 45%-84% of deceased patients, respectively. Overall, the included studies mostly focused on PE during the acute phase of COVID-19. The results demonstrate the challenges of identifying and characterizing vascular abnormalities using current protocols (e.g., visual computed tomography reads). Further research is needed to detect subtle pulmonary vascular abnormalities, distinguish thromboinflammatory disease from PE, optimize treatment, and assess the incidence of long-term sequelae after COVID-19.