Mapping institution-specific study descriptions to RadLex Playbook entries

Simplified size adjusted dose reference levels for adult CT examinations: A regional study

PURPOSE

To investigate retrospective classification of adult patients into small, average, and large based on effective diameter (EDia) from localizer image of computed tomography (CT) scans and to develop regional diagnostic reference levels (DRLs) and achievable doses (AD).

METHOD

The patients falling within the mean ± standard deviation (SD) of EDia were classified as average; those below this range as small and above as large. The CTDI_vol,dose-length-product (DLP) and size-specific dose estimates (SSDE) of all adult patients undergoing CT examinations in 8 CT facilities for 11 months (Dec. 2019 - Oct. 2020) were evaluated. The 75th and 50th percentile values were compared with national and international values.

RESULTS

Of the total of 69,434 CT examinations, nearly 80% fell within average size. The 75th percentile values of CTDI_vol and DLP for small patients for abdomen-pelvic exams were nearly half of average sized patients. Similarly, the 75th percentile values for large patients were nearly double. Similar findings were not found for chest exams. Analysis of image quality and dose factors such as noise, mean axial length, slice thickness, mean number of sequences, use of iterative reconstruction and tube current modulation (TCM) resulted in identification of opportunities for improvement and optimization of different CT facilities.

CONCLUSIONS

DRLs for adult patients were found to vary widely with patient size and thus establishing DRLs only for standard sized patient is not adequate. Simplified and intuitive methods for size classification was shown to provide meaningful information for optimization for patients outside the standard size adult.

The LOINC RSNA radiology playbook - a unified terminology for radiology procedures

Objective

This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced.

Methods

The Regenstrief Institute and the Radiological Society of North America (RSNA) developed a unification plan consisting of six objectives 1) develop a unified model for radiology procedure names that represents the attributes with an extensible set of values, 2) transform existing LOINC procedure codes into the unified model representation, 3) create a mapping between all the attribute values used in the unified model as coded in LOINC (ie, LOINC Parts) and their equivalent concepts in RadLex, 4) create a mapping between the existing procedure codes in the RadLex Core Playbook and the corresponding codes in LOINC, 5) develop a single integrated governance process for managing the unified terminology, and 6) publicly distribute the terminology artifacts.

Results

We developed a unified model and instantiated it in a new LOINC release artifact that contains the LOINC codes and display name (ie LONG_COMMON_NAME) for each procedure, mappings between LOINC and the RSNA Playbook at the procedure code level, and connections between procedure terms and their attribute values that are expressed as LOINC Parts and RadLex IDs. We transformed all the existing LOINC content into the new model and publicly distributed it in standard releases. The organizations have also developed a joint governance process for ongoing maintenance of the terminology.

Conclusions

The LOINC/RSNA Radiology Playbook provides a universal terminology standard for radiology orders and results.

Local clinical diagnostic reference levels for chest and abdomen CT examinations in adults as a function of body mass index and clinical indication: a prospective multicenter study

OBJECTIVES

To compare institutional dose levels based on clinical indication and BMI class to anatomy-based national DRLs (NDRLs) in chest and abdomen CT examinations and to assess local clinical diagnostic reference levels (LCDRLs).

METHODS

From February 2017 to June 2018, after protocol optimization according to clinical indication and body mass index (BMI) class (< 25; ≥ 25), 5310 abdomen and 1058 chest CT series were collected from 5 CT scanners in a Swiss multicenter group. Clinical indication-based institutional dose levels were compared to the Swiss anatomy-based NDRLs. Statistical significance was assessed (p < 0.05). LCDRLs were calculated as the third quartile of the median dose values for each CT scanner.

RESULTS

For chest examinations, dose metrics based on clinical indication were always below P75 NDRL for CTDI_vol (range 3.9-6.4 vs. 7.0 mGy) and DLP (164.0-211.2 vs. 250 mGycm) in all BMI classes except for DLP in BMI ≥ 25 (248.8-255.4 vs. 250.0 mGycm). For abdomen examinations, they were significantly lower or not different than P50 NDRLs for all BMI classes (3.8-9.0 vs. 10.0 mGy and 192.9-446.8 vs. 470mGycm). The estimated LCDRLs show a drop in CTDI_vol (21% for chest and 32% for abdomen, on average) with respect to current DRLs. When considering BMI stratification, the largest LCDRL difference within the same clinical indication is for renal tumor (4.6 mGy for BMI < 25 vs. 10.0 mGy for BMI ≥ 25; - 117%).

CONCLUSION

The results suggest the necessity of estimating clinical indication-based DRLs, especially for abdomen examinations. Stratifying per BMI class allows further optimization of the CT doses.

KEY POINTS

• Our data show that clinical indication-based DRLs might be more appropriate than anatomy-based DRLs and might help in reducing large variations in dose levels for the same type of examinations. • Stratifying the data per patient-size subgroups (non-overweight, overweight) allows a better optimization of CT doses and therefore the possibility to set LCDRLs based on BMI class. • Institutions who are fostering continuous dose optimization and LDRLs should consider defining protocols based on clinical indication and BMI group, to achieve ALARA.

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.

Workflow Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient's electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges.

Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure

Radiology procedure codes are a fundamental part of most radiology workflows, such as ordering, scheduling, billing, and image interpretation. Nonstandardized unstructured procedure codes have typically been used in radiology departments. Such codes may be sufficient for specific purposes, but they offer limited support for interoperability. As radiology workflows and the various forms of clinical data exchange have become more sophisticated, the need for more advanced interoperability with use of standardized structured codes has increased. For example, structured codes facilitate the automated identification of relevant prior imaging studies and the collection of data for radiation dose tracking. The authors review the role of imaging procedure codes in radiology departments and across the health care enterprise. Standards for radiology procedure coding are described, and the mechanisms of structured coding systems are reviewed. In particular, the structure of the RadLex™ Playbook coding system and examples of the use of this system are described. Harmonization of the RadLex Playbook system with the Logical Observation Identifiers Names and Codes standard, which is currently in progress, also is described. The benefits and challenges of adopting standardized codes-especially the difficulties in mapping local codes to standardized codes-are reviewed. Tools and strategies for mitigating these challenges, including the use of billing codes as an intermediate step in mapping, also are reviewed. In addition, the authors describe how to use the RadLex Playbook Web service application programming interface for partial automation of code mapping. ^© RSNA, 2017.

Improving efficiency in the radiology department

The modern radiology department is built around the flow of information. Ordering providers request imaging studies to be performed, technologists complete the work required to perform the imaging studies, and radiologists interpret and report on the imaging findings. As each of these steps is performed, data flow between multiple information systems, most notably the radiology information system (RIS), the picture archiving and communication system (PACS) and the voice dictation system. Even though data flow relatively seamlessly, the majority of our systems and processes are inefficient. The purpose of this article is to describe the radiology value stream and describe how radiology informaticists in one department have worked to improve the efficiency of the value stream at each step. Through these examples, we identify and describe several themes that we believe have been crucial to our success.

Radiomics: Images Are More than Pictures, They Are Data

In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.

Development of a Knowledge-based Application Utilizing Ontologies for the Continuing Site-specific JJ1017 Master Maintenance

The purpose of this study was to develop the JJ1017 Knowledge-based Application (JKA) to support the continuing maintenance of a site-specific JJ1017 master defined by the JJ1017 guideline as a standard radiologic procedure master for medical information systems that are being adopted by some medical facilities in Japan. The method consisted of the following three steps: (1) construction of the JJ1017 Ontology (JJOnt) as a knowledge base using the Hozo (an environment for building/using ontologies); (2) development of modules (operation, I/O, graph modules) that are required to continue the maintenance of a site-specific JJ1017 master; and (3) unit testing of the JKA that consists of the JJOnt and the modules. As a result, the number of classes included in the JJOnt was 21,697. Within the radiologic procedure classes included in the above, the ratio of a JJ1017 master code for an external beam radiotherapy was the highest (51%). In unit testing of the JKA, we checked the main operations (e.g., keyword search of a JJ1017 master code/code meaning, editing the description of classes, etc.). The JJOnt is a knowledge base for implementing features that medical technologists find necessary in medical information systems. To enable medical technologists to exchange/retrieve semantically accurate information while using medical information systems in the future, we expect the JKA to support the maintenance and improvement of the site-specific JJ1017 master.

Mapping institution-specific study descriptions to RadLex Playbook entries

The naming of imaging procedures is currently not standardized across institutions. As a result, it is a challenge to establish national registries, for instance, a national registry of dose to facilitate comparisons among different types of CT procedures. RSNA's RadLex Playbook is an effort towards addressing this gap (by introducing a unique Playbook identifier called an RPID for each procedure), and the current research focuses on semi-automatically mapping institution-specific procedure descriptions to Playbook entries to assist with this standardization effort. We discuss an algorithm we have developed to facilitate the mapping process which first extracts RadLex codes from the procedure description and then uses the definition of an RPID to determine the most suitable RPID(s) for the extracted set of RadLex codes. We also developed a tool that has three modes of operations-a single procedure mapping mode that allows a user to map a single institution-specific procedure description to a Playbook entry, a bulk mode to process large number of descriptions, and an exploratory mode that assists a user to better understand how the selection of values for various Playbook attributes affects the resulting RPID. We validate our algorithms using 166 production CT procedure descriptions and discuss how the tool can be used by administrators to map institution-specific procedure descriptions to RPIDs.

The value proposition of structured reporting in interventional radiology

OBJECTIVE

The purposes of this article are to provide a brief overview of structured radiology reporting and to emphasize the anticipated benefits from a new generation of standardized interventional radiology procedure reports.

CONCLUSION

Radiology reporting standards and tools have evolved to enable automated data integration from multiple institutions using structured templates. In interventional radiology, data aggregated into clinical, research and quality registries from enriched structured reports could firmly establish the interventional radiology value proposition.