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Abstract Dose management systems (DMS) are an essential tool for quality assurance and optimising patient radiation exposure. For radiologists and medical physicists, they are important for managing many radiation protection tasks. In addition, they help fulfil the requirements of Directive 2013/59/EURATOM regarding the electronic transmission of dosimetric data and the detection of unintended patient exposures. The EuroSafe Imaging Clinical Dosimetry and Dose Management Working Group launched a questionnaire on the use of DMS in European member states and analysed the results in terms of modalities, frequency of radiological procedures, involvement of medical physics experts (MPEs), legal requirements, and local issues (support by information technology (IT), modality interfaces, protocol mapping, clinical workflow, and associated costs). Critical relevance statement Despite the great advantages of dose management systems for optimising radiation protection, distribution remains insufficient. This questionnaire shows that reasons include: a lack of DICOM interfaces, insufficient harmonisation of procedure names, lack of medical physicist and IT support, and costs. Key Points Quantitative radiation dose information is essential for justification and optimisation in medical imaging. Guidelines are required to ensure radiation dose management systems quality and for acceptance testing. Verifying dose data management is crucial before dose management systems clinical implementation. Medical physics experts are professionals who have important responsibilities for the proper management of dose monitoring. Graphical Abstract

Dose management systems (DMS) are an essential tool for quality assurance and optimising patient radiation exposure. For radiologists and medical physicists, they are important for managing many radiation protection tasks. In addition, they help fulfil the requirements of Directive 2013/59/EURATOM regarding the electronic transmission of dosimetric data and the detection of unintended patient exposures. The EuroSafe Imaging Clinical Dosimetry and Dose Management Working Group launched a questionnaire on the use of DMS in European member states and analysed the results in terms of modalities, frequency of radiological procedures, involvement of medical physics experts (MPEs), legal requirements, and local issues (support by information technology (IT), modality interfaces, protocol mapping, clinical workflow, and associated costs). Critical relevance statement: Despite the great advantages of dose management systems for optimising radiation protection, distribution remains insufficient. This questionnaire shows that reasons include: a lack of DICOM interfaces, insufficient harmonisation of procedure names, lack of medical physicist and IT support, and costs. Key Points: Quantitative radiation dose information is essential for justification and optimisation in medical imaging. Guidelines are required to ensure radiation dose management systems quality and for acceptance testing. Verifying dose data management is crucial before dose management systems clinical implementation. Medical physics experts are professionals who have important responsibilities for the proper management of dose monitoring. [ABSTRACT FROM AUTHOR]

Background: The radiology report is vital for providing imaging information to guide patient treatment, and template-based reporting can potentially increase the reporting completeness. In 2014, a national reporting template for radiological staging of rectal cancer using magnetic resonance imaging (MRI) was implemented in Sweden. Purpose: To evaluate the impact of the national reporting template by comparing and analysing differences in content and completeness in MRI reports between 2010 and 2016. Focus was to compare reporting completeness (i) between different reporting years and (ii) between three defined reporting styles. Material and Methods: 493 MRI reports were gathered from 10 hospitals in four healthcare regions in Sweden, comprising 243 reports from 2010 and 250 reports from 2016. Reports were classified into three reporting styles: Expanded structured, Minimised structured, and Unstructured, and analysed using qualitative content analysis based on the national template. Results: In 2010, all reports adhered to Unstructured reporting. In 2016, 44, 42, and 164 reports were conformant to Expanded structured, Minimised structured, and Unstructured reporting, respectively. A comparison between the years revealed a reporting completeness of 48% for 2010 reports and 72% for 2016 reports. Among the 2016 reporting styles, Unstructured reporting had the largest gap compared to the national template, with completeness at 64% versus 77.5% for Minimised structured reporting and 93% for Expanded structured reporting. Conclusion: Implementation of template-based reporting according to Expanded structure is key to conform to national decided evidence-based practice for radiological staging of rectal cancer. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

Background: Magnetic resonance imaging (MRI) is the first-line imaging modality for local staging of rectal cancer. The radiology report should deliver all relevant available imaging information to guide treatment. Purpose: To explore and describe if there was a gap between the contents in MRI reports for primary staging of rectal cancer in Sweden in 2010 compared to evidence-based practice. Material and Methods: A total of 243 primary MRI staging reports from 2010, collected from 10 hospitals in four healthcare regions in Sweden, were analyzed using content analysis with a deductive thematic coding scheme based on evidence-based practice. Focus was on: (i) most frequently reported findings; (ii) correlation to key prognostic findings; and (iii) identifying if any findings being reported were beyond the information defined in evidence-based practice. Results: Most frequently reported findings were spread through the bowel wall or not, local lymph node description, tumor length, and distance of tumor from anal verge. These items accounted for 35% of the reporting content. Of all reported content, 86% correlated with the evidence-based practice. However, these included more information than was generally found in the reports. When adjusting for omitted information, 48% of the reported content were accounted for. Of the reported content, 20% correlated to key pathological prognostic findings. Six types of findings were reported beyond the evidence-based practice, representing 14% of the total reporting content. Conclusion: There was a gap between everyday practice and evidence-based practice in 2010. This indicates a need for national harmonization and implementation of standardized structured reporting templates. [ABSTRACT FROM AUTHOR]

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The clinical deployment of artificial intelligence (AI) applications in medical imaging is perhaps the greatest challenge facing radiology in the next decade. One of the main obstacles to the incorporation of automated AI-based decision-making tools in medicine is the failure of models to generalize when deployed across institutions with heterogeneous populations and imaging protocols. The most well-understood pitfall in developing these AI models is overfitting, which has, in part, been overcome by optimizing training protocols. However, overfitting is not the only obstacle to the success and generalizability of AI. Underspecification is also a serious impediment that requires conceptual understanding and correction. It is well known that a single AI pipeline, with prescribed training and testing sets, can produce several models with various levels of generalizability. Underspecification defines the inability of the pipeline to identify whether these models have embedded the structure of the underlying system by using a test set independent of, but distributed identically, to the training set. An underspecified pipeline is unable to assess the degree to which the models will be generalizable. Stress testing is a known tool in AI that can limit underspecification and, importantly, assure broad generalizability of AI models. However, the application of stress tests is new in radiologic applications. This report describes the concept of underspecification from a radiologist perspective, discusses stress testing as a specific strategy to overcome underspecification, and explains how stress tests could be designed in radiology-by modifying medical images or stratifying testing datasets. In the upcoming years, stress tests should become in radiology the standard that crash tests have become in the automotive industry. Keywords: Computer Applications-General, Informatics, Computer-aided Diagnosis © RSNA, 2021., Competing Interests: Disclosures of conflicts of interest: T.E. No relevant relationships. L.H.S. Consultant for Merck and Regeneron (member, DSMB and endpoint analysis committees). F.Z.M. No relevant relationships. L.D. No relevant relationships., (2021 by the Radiological Society of North America, Inc.)