Multicenter Research Studies in Radiology

Assessing the Relationship between Radiology Department Research Funding and Institutional Community Inclusion and Investment

Background Health care access disparities and lack of inclusion in clinical research have been well documented for marginalized populations. However, few studies exist examining the research funding of institutions that serve historically underserved groups. Purpose To assess the relationship between research funding awarded to radiology departments by the National Institutes of Health (NIH) and Lown Institute Hospitals Index rankings for inclusivity and community benefit. Materials and Methods This retrospective study included radiology departments awarded funding from the NIH between 2017 and 2021. The 2021 Lown Institute Hospitals Index rankings for inclusivity and community benefit were examined. The inclusivity metric measures how similar a hospital's patient population is to the surrounding community in terms of income, race and ethnicity, and education level. The community benefit metric measures charity care spending, Medicaid as a proportion of patient revenue, and other community benefit spending. Linear regression and Pearson correlation coefficients (r values) were used to evaluate the relationship between aggregate NIH radiology department research funding and measures of inclusivity and community benefit. Results Seventy-five radiology departments that received NIH funding ranging from $195 000 to $216 879 079 were included. A negative correlation was observed between the amount of radiology department research funding received and institutional rankings for serving patients from racial and/or ethnic minorities (r = -0.34; P < .001), patients with low income (r = -0.44; P < .001), and patients with lower levels of education (r = -0.46; P < .001). No correlation was observed between the amount of radiology department research funding and institutional rankings for charity care spending (r = -0.19; P = .06), community investment (r = -0.04; P = .68), and Medicaid as a proportion of patient revenue (r = -0.10; P = .22). Conclusion Radiology departments that received more NIH research funding were less likely to serve patients from racial and/or ethnic minorities and patients who had low income or lower levels of education. © RSNA, 2024 See also the editorial by Mehta and Rosen in this issue.

BMAT: An open-source BIDS managing and analysis tool

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

Medical-Blocks: A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research

BACKGROUND

Biomedical research requires healthcare institutions to provide sensitive clinical data to leverage data science and artificial intelligence technologies. However, providing healthcare data to researchers simple and secure, proves to be challenging for healthcare institutions.

OBJECTIVE

We describe and introduce Medical-Blocks, a platform for data exploration, data management, data analysis, and data sharing in biomedical research.

METHODS

The specification requirements for Medical-Blocks included: i) Connection to data sources of healthcare institutions with an interface for data exploration, ii) management of data in an internal file storage system, iii) data analysis through visualization and classification of data, and iv) data sharing via a file hosting service for collaboration. Medical-Blocks should be simple to use via a web-based user interface and extensible with new functionalities by a modular design via microservices ("blocks"). The scalability of the platform should be ensured by containerization. Security and legal regulations were considered during the development.

RESULTS

Medical-Blocks is a web application that runs in the cloud or as a local instance at a healthcare institution. Local instances of Medical-Blocks access data sources such as electronic health records and picture archiving and communications system (PACS) at healthcare institutions. Researchers and clinicians can explore, manage, and analyze the available data through Medical-Blocks. The data analysis involves classification of data for metadata extraction and the formation of cohorts. In collaborations, metadata (e.g., number of patients per cohort) and/or the data itself can be shared through Medical-Blocks locally or via a cloud instance to other researchers and clinicians.

CONCLUSIONS

Medical-Blocks facilitates biomedical research by providing a centralized platform to interact with medical data in collaborative research projects. The access to and management of medical data is simplified. Data can be swiftly analyzed to form cohorts for research and be shared among researchers. The modularity of Medical-Blocks makes the platform feasible for biomedical research where heterogenous medical data is needed.

CLINICALTRIAL

AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence

The Radiology Research Alliance (RRA) of the Association of University Radiologists (AUR) convenes Task Forces to address current topics in radiology. In this article, the AUR-RRA Task Force on Academic-Industry Partnerships for Artificial Intelligence, considered issues of importance to academic radiology departments contemplating industry partnerships in artificial intelligence (AI) development, testing and evaluation. Our goal was to create a framework encompassing the domains of clinical, technical, regulatory, legal and financial considerations that impact the arrangement and success of such partnerships.

Transfer, Collection and Organisation of Electrophysiological and Imaging Data for Multicentre Studies

Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS Uploader. The former has been designed to collect, organise and manage the data and the latter has been conceived to handle their transfer and anonymisation from the partner centres. These two programs aim at facilitating the implementation of multicentre study by providing a standardised framework.

Considerations in preparing a multicenter study: Lessons learned from the Epilepsy Monitoring Unit Comfort Questionnaire (EMUCQ) validation feasibility study

PURPOSE

It is recommended to perform a pilot testing before conducting a validation study of a novel questionnaire. Pilot studies may serve different purposes. The aim of this study was to assess the feasibility of a multicenter validation study, to recruit additional study centers, and to undertake orientating descriptive item analysis of the 44-item Epilepsy Monitoring Unit (EMU) Comfort Questionnaire (EMUCQ).

METHODS

During a six-month sampling period, the EMUCQ was administered to eligible EMU patients. The patients filled out the questionnaire at two time points. Additional centers were recruited in Germany and Austria, and ethics votes obtained. In descriptive item analysis central tendency, variability, item distribution and item difficulty were calculated.

RESULTS

A total of 44 EMU patients participated in the study. Eight additional EMUs agreed to join the planned validation study. Recruitment of the centers took four months. Another six months passed to obtain all the ethics votes. Floor and ceiling effects could be detected in 32 items. One item with the lowest median showed the low item difficulty. Another five items showed medians with the height of 6. In four items, high difficulty indices could be observed.

CONCLUSION

A good network has turned out to be very helpful while planning a multicenter study. Enough time must be scheduled, because obtaining an ethics vote may take quite a long time. No conclusive statements regarding item properties could be made as this was a feasibility study.