Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study

OBJECTIVES

In this multi-center study, we proposed a structured reporting (SR) framework for non-small cell lung cancer (NSCLC) and developed a software-assisted tool to automatically translate image-based findings and annotations into TNM classifications. The aim of this study was to validate the software-assisted SR tool for NSCLC, assess its potential clinical impact in a proof-of-concept study, and evaluate current reporting standards in participating institutions.

METHODS

A framework for SR and staging of NSCLC was developed in a multi-center collaboration. SR annotations and descriptions were used to generate semi-automated TNM classification. The SR and TNM classification tools were evaluated by nine radiologists on n = 20 representative [18F]FDG PET/CT studies and compared to the free text reporting (FTR) strategy. Results were compared to a multidisciplinary team reference using a generalized linear mixed model (GLMM). Additionally, participants were surveyed on their experience with SR and TNM classification.

RESULTS

Overall, GLMM analysis revealed that readers using SR were 1.707 (CI: 1.137-2.585) times more likely to correctly classify TNM status compared to FTR strategy (p = 0.01) resulting in increased overall TNM correctness in 71.9% (128/178) of cases compared to 62.8% (113/180) FTR. The primary source of variation in classification accuracy was explained by case complexity. Participants rated the potential impact of SR and semi-automated TNM classification as positive across all categories with improved scores after template validation.

CONCLUSION

This multi-center study yielded an effective software-assisted SR framework for NSCLC. The SR and semi-automated classification tool improved TNM classification and were perceived as valuable.

CRITICAL RELEVANCE STATEMENT

Software-assisted SR provides robust input for semi-automated rule-based TNM classification in non-small-cell lung carcinoma (NSCLC), improves TNM correctness compared to FTR, and was perceived as valuable by radiology physicians.

KEY POINTS

SR and TNM classification are underutilized across participating centers for NSCLC staging. Software-assisted SR has emerged as a promising strategy for oncologic assessment. Software-assisted SR facilitates semi-automated TNM classification with improved staging accuracy compared to free-text reports in NSCLC.

"3 + X D" structured report in radiology standardized resident training: Can it meet high-level teaching objectives?

PURPOSE

To evaluate the impact of the innovative "3 + X D" structured report (SR) designed based on Bloom's taxonomy on the learning outcomes of radiology residents during standardized training.

METHODS

This is a prospective study that recruited 120 radiology residents from our hospital between 2020 and 2022. Randomly selected 60 residents from the 2020 grade to constituted the control group, and randomly selected 60 residents from the 2021 grade to formed the experimental group. The former group was trained utilizing the Free-text Reports (FTR) template, while the latter group received training with the "3 + X D" structured reports (SR) template. The learning outcomes of both groups was evaluated utilizing both objective and subjective measures. Objective assessments encompassed examinations of theoretical knowledge, diagnostic skills, and total scores, aligning with the cognitive domains of remembering, understanding, applying, and analyzing as outlined by Bloom's Taxonomy. Subjective assessments, on the other hand, comprised survey questionnaires administered to residents and feedback from clinical instructors, which correlated with the higher-order cognitive level of analyzing, evaluating, and creating within Bloom's Taxonomy.

RESULTS

On 60 residents (mean age, 24.15 years ± 2.11[SD]; 25 male) from control group, and 60 residents (mean age, 24.58 years ± 1.88 [SD]; 27 male) from experimental group. Following the training, significant improvements were observed in the theoretical knowledge, diagnostic skills, and total scores for both groups (p < 0.001). Furthermore, the experimental group demonstrated significantly higher diagnostic skills and total scores compared to the control group (p < 0.001). However, no significant difference was observed in the theoretical knowledge exam between the two groups (p = 0.236). The questionnaire used for subjective assessments had good reliability (Cronbach α was 0.826) and acceptable validity (The KMO was 0.692). Additionally, the survey questionnaires indicated that the experimental group rated higher than the control group in terms of cultivating imaging thinking ability, diagnostic confidence, diagnostic speed, and the convenience of the templates (p < 0.001). Clinicians' feedback scores for the experimental group markedly surpassed those for the control group (p < 0.05).

CONCLUSIONS

Utilizing the "3 + X D" SR template grounded in Bloom's taxonomy for training, the professional competency of radiology residents, particularly their diagnostic skills, saw a marked enhancement, successfully meeting the higher-level educational objectives. Consequently, the "3 + X D" SR template is highly recommended for the standardized training of radiology residents.

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby improving diagnostic precision and accuracy and thus enhancing current tests. However, the integration of AI within health care is fraught with difficulties. Heterogeneity among health care system applications, reliance on proprietary closed-source software, and rising cybersecurity threats pose significant challenges. Moreover, before their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow, which is difficult to achieve without dedicated software. Finally, the use of AI techniques in health care raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in health care and provides guidelines on how to move forward. We describe an open-source solution that we developed that integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offer a pathway toward responsible, fair, and effective deployment of AI models in health care. In addition, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model to enhance standardisation and reproducibility.

Perceptions of radiologists on structured reporting for cancer imaging-a survey by the European Society of Oncologic Imaging (ESOI)

OBJECTIVES

To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template.

MATERIALS AND METHODS

An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients.

RESULTS

A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = - 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = - 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036).

CONCLUSION

Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians.

CLINICAL RELEVANCE STATEMENT

Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement.

KEY POINTS

• The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.

ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language

Background

To investigate the feasibility of the large language model (LLM) ChatGPT for classifying liver lesions according to the Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, and to compare classification performance on structured vs. unstructured reports.

Methods

LI-RADS classifiable liver lesions were included from German written structured and unstructured MRI reports with report of size, location, and arterial phase contrast enhancement as minimum inclusion requirements. The findings sections of the reports were propagated to ChatGPT (GPT-3.5), which was instructed to determine LI-RADS scores for each classifiable liver lesion. Ground truth was established by two radiologists in consensus. Agreement between ground truth and ChatGPT was assessed with Cohen's kappa. Test-retest reliability was assessed by passing a subset of n = 50 lesions five times to ChatGPT, using the intraclass correlation coefficient (ICC).

Results

205 MRIs from 150 patients were included. The accuracy of ChatGPT at determining LI-RADS categories was poor (53% and 44% on unstructured and structured reports). The agreement to the ground truth was higher (k = 0.51 and k = 0.44), the mean absolute error in LI-RADS scores was lower (0.5 ± 0.5 vs. 0.6 ± 0.7, p < 0.05), and the test-retest reliability was higher (ICC = 0.81 vs. 0.50), in free-text compared to structured reports, respectively, although structured reports comprised the minimum required imaging features significantly more frequently (Chi-square test, p < 0.05).

Conclusions

ChatGPT attained only low accuracy when asked to determine LI-RADS scores from liver imaging reports. The superior accuracy and consistency throughout free-text reports might relate to ChatGPT's training process.

Clinical relevance statement

Our study indicates both the necessity of optimization of LLMs for structured clinical data input and the potential of LLMs for creating machine-readable labels based on large free-text radiological databases.

ESR paper on structured reporting in radiology-update 2023

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians' preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine.While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists' workflow.The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due-policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.Key points1. Various national societies have established initiatives for structured reporting in radiology.2. Almost no monetary or structural incentives exist that favor structured reporting.3. A consensus on technical standards for structured reporting is still missing.4. The application of large language models may help structuring radiological reports.5. Policy makers should incentivize the usage of structured radiological reporting.

Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period

OBJECTIVES

To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability.

METHODS

A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016. Reports were segmented by a natural language processing algorithm and converted into a 20-dimension document vector. For analysis, dimensionality was reduced to a 2D visualization with t-distributed stochastic neighbor embedding and matched with metadata. Linguistic standardization was assessed by comparing distinct report types' vector spreads (e.g., run-off MR angiography) between reporting standards. Changes in report type distinguishability (e.g., CT abdomen/pelvis vs. MR abdomen) were measured by comparing the distance between their centroids.

RESULTS

Structured reports showed lower document vector spread (thus higher linguistic similarity) compared with free-text reports overall (21.9 [free-text] vs. 15.9 [structured]; - 27.4%; p < 0.001) and for most report types, e.g., run-off MR angiography (15.2 vs. 1.8; - 88.2%; p < 0.001) or double-rule-out CT (26.8 vs. 10.0; - 62.7%; p < 0.001). No changes were observed for reports continued to be written in free text, e.g., CT head reports (33.2 vs. 33.1; - 0.3%; p = 1). Distances between the report types' centroids increased with structured reporting (thus better linguistic distinguishability) overall (27.3 vs. 54.4; + 99.3 ± 98.4%) and for specific report types, e.g., CT abdomen/pelvis vs. MR abdomen (13.7 vs. 37.2; + 171.5%).

CONCLUSION

Structured reporting and the use of factual language yield more homogenous and standardized radiology reports on a linguistic level, tailored to specific reporting scenarios and imaging studies.

CLINICAL RELEVANCE

Information transmission to referring physicians, as well as automated report assessment and content extraction in big data analyses, may benefit from standardized reporting, due to consistent report organization and terminology used for pathologies and normal findings.

KEY POINTS

• Natural language processing and t-distributed stochastic neighbor embedding can transform radiology reports into numeric vectors, allowing the quantification of their linguistic standardization. • Structured reporting substantially increases reports' linguistic standardization (mean: - 27.4% in vector spread) and distinguishability (mean: + 99.3 ± 98.4% increase in vector distance) compared with free-text reports. • Higher standardization and homogeneity outline potential benefits of structured reporting for information transmission and big data analyses.

Natural language processing in radiology: Clinical applications and future directions

Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utilized in the medical field with increased reliance on electronic health records. As findings in radiology are primarily communicated via text, the field is particularly suited to benefit from NLP based applications. Furthermore, rapidly increasing imaging volume will continue to increase burden on clinicians, emphasizing the need for improvements in workflow. In this article, we highlight the numerous non-clinical, provider focused, and patient focused applications of NLP in radiology. We also comment on challenges associated with development and incorporation of NLP based applications in radiology as well as potential future directions.

Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows

From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the wide spectrum of diseases and their manifestations superimposed on medical team-specific and discipline-specific communication patterns often impairs shared understanding and the shared use of common medical terminology. Common terms are currently used in medicine to ensure interoperability and facilitate integration of biomedical information for clinical practice and emerging scientific and educational applications alike, from database integration to supporting basic clinical operations such as billing. Such common terminologies can be provided in ontologies, which are formalized representations of knowledge in a particular domain. Ontologies unambiguously specify common concepts and describe the relationships between those concepts by using a form that is mathematically precise and accessible to humans and machines alike. RadLex^® is a key RSNA initiative that provides a shared domain model, or ontology, of radiology to facilitate integration of information in radiology education, clinical care, and research. As the contributions of the computational components of common radiologic workflows continue to increase with the ongoing development of big data, artificial intelligence, and novel image analysis and visualization tools, the use of common terminologies is becoming increasingly important for supporting seamless computational resource integration across medicine. This article introduces ontologies, outlines the fundamental semantic web technologies used to create and apply RadLex, and presents examples of RadLex applications in everyday radiology and research. It concludes with a discussion of emerging applications of RadLex, including artificial intelligence applications. ^© RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Structured Reporting in Cross-Sectional Imaging of the Heart: Reporting Templates for CMR Imaging of Ischemia and Myocardial Viability and for Cardiac CT Imaging of Coronary Heart Disease and TAVI Planning

BACKGROUND

 Structured reporting allows a high grade of standardization and thus a safe and unequivocal report communication. In the past years, the radiological societies have started several initiatives to base radiological reports on structured reporting rather than free text reporting.

METHODS

 Upon invitation of the working group for Cardiovascular Imaging of the German Society of Radiology, in 2018 an interdisciplinary group of Radiologists, Cardiologists, Pediatric Cardiologists and Cardiothoracic surgeons -all experts on the field of cardiovascular MR and CT imaging- met for interdisciplinary consensus meetings at the University Hospital Cologne. The aim of these meetings was to develop and consent templates for structured reporting in cardiac MR and CT of various cardiovascular diseases.

RESULTS

 Two templates for structured reporting of CMR in ischemia imaging and vitality imaging and two templates for structured reporting of CT imaging for planning Transcatheter Aortic Valve Implantation (TAVI; pre-TAVI-CT) and coronary CT were discussed, consented and transferred to a HTML 5/IHR MRRT compatible format. The templates were made available for free use on the website www.befundung.drg.de.

CONCLUSION

 This paper suggests consented templates in German language for the structured reporting of cross-sectional CMR imaging of ischemia and vitality as well as reporting of CT imaging pre-TAVI and coronary CT. The implementation of these templates is aimed at providing a constant level of high reporting quality and increasing the efficiency of report generation as well as a clinically based communication of imaging results.

KEY POINTS

  · Structured reporting offers a constant level of high reporting quality and increases the efficiency of report generation as well as a clinically based communication of imaging results.. · For the first time templates in German language for the structured reporting of CMR imaging of ischemia and vitality and CT imaging pre-TAVI and coronary CT are reported.. · These templates will be made available on the website www.befundung.drg.de and can be commented via strukturierte-befundung@drg.de..

ZITIERWEISE

· Soschynski M, Bunck AC, Beer M et al. Structured Reporting in Cross-Sectional Imaging of the Heart: Reporting Templates for CMR Imaging of Ischemia and Myocardial Viability and for Cardiac CT Imaging of Coronary Heart Disease and TAVI Planning. Fortschr Röntgenstr 2023; DOI: 10.1055/a-1981-1196.

[Image interpretation and the radiological report]

BACKGROUND

The radiological report is the cornerstone of communication between radiologists and referring physicians and patients, respectively. The report is comprised of image interpretation on the one hand and communication of this interpretation on the other hand.

OBJECTIVES AND METHODS

To outline different types of radiological reports (regarding content as well as structure) and their communication. To this end, current guidelines are summarized and clinical examples are presented.

RESULTS

The radiological report is typically a written piece of free text prose and highly individualized regarding its quality, precision, and structure. In order to improve the understanding of the written report, additional material (e.g., annotations, images, tables) can be supplemented (multimedia-enhanced reporting). In terms of standardization, national and international radiological associations promote structured reporting in radiology. However, this is not without issues.

CONCLUSION

Effective communication should improve patient care and it should be clear and provided in a timely manner. As communication in clinical reality is often hampered by various factors, internal standard operating procedures (SOPs) should be developed to improve communication workflows. to improve communication procedures.

German Radiological Society and the Professional Association of German Radiologists Position Paper on Coronary computed tomography: Clinical Evidence and Quality of Patient Care in Chronic Coronary Syndrome

This position paper is a joint statement of the German Radiological Society (DRG) and the Professional Association of German Radiologists (BDR), which reflects the current state of knowledge about coronary computed tomography. It is based on preclinical and clinical studies that have investigated the clinical relevance as well as the technical requirements and fundamentals of cardiac computed tomography. CITATION FORMAT: · Langenbach MC, Sandstede J, Sieren M et al. DRG and BDR Position Paper on Coronary CT: Clinical Evidence and Quality of Patient Care in Chronic Coronary Syndrome. Fortschr Röntgenstr 2023; 195: 115 - 133.

[German Radiological Society and the Professional Association of German Radiologists position paper on coronary computed tomography: clinical evidence and quality of patient care in chronic coronary syndrome]

This position paper is a joint statement of the German Radiological Society (DRG) and the Professional Association of German Radiologists (BDR), which reflects the current state of knowledge about coronary computed tomography (CT). It is based on preclinical and clinical studies that have investigated the clinical relevance as well as the technical requirements and fundamentals of cardiac computed tomography.

Eye-tracking differences between free text and template radiology reports: a pilot study

Purpose

One possible limitation of structured template radiology reports is that radiologists look back and forth between viewing and dictation monitors, thereby impacting the length of time spent reviewing images and generating a report. We hypothesize that the total time spent viewing case images is diminished and/or the total time spent creating a report is prolonged when the report is generated using a structured template compared with free text format.

Approach

Three neuroradiologists and three senior residents viewed five brain magnetic resonance imaging cases with unique findings while eye position was recorded. Participants generated reports for each case utilizing both structured templates and free text dictation. The time spent viewing images was compared with the time spent looking at the dictation screen.

Results

The two main hypotheses were confirmed: the total time viewing images diminished with templates versus free text dictation and the total time to create a report was prolonged with templates. The mean time (s) spent on the "image" region of interest approached statistical significance as a function of the report type [free: attendings = 236.79 (154.43), residents = 223.55 (77.79); template: attendings = 163.40 (73.42), residents = 182.48 (77.47)] and was overall lower with the template reporting for both attendings and residents ( F = 3.77 , p = 0.0623 ), but it did not differ as a function of seniority ( F = 0.017 , p = 0.8977 ).

Conclusions

Template-based radiology reports have significant potential to alter the way radiologists view images and report on them, spending more time viewing the report monitor rather than diagnostic images compared with free text dictation. Many radiologists prefer templates for reporting as the structured format may aid in conducting a more systematic or thorough search for findings, although prior work on this assumption is mixed. Future eye-tracking studies could further elucidate whether and how templates and free reports impact the detection and classification of radiographic findings.

Performance of the Neck Imaging Reporting and Data System as applied by general neuroradiologists to predict recurrence of head and neck cancers

BACKGROUND

The Neck Imaging Reporting and Data System (NI-RADS) is used to assess imaging after head and neck cancer treatment. We evaluated NI-RADS with general neuroradiologists rather than with head and neck subspecialists.

METHODS

Computed tomography and magnetic resonance imaging examinations with/without positron emission tomography from May 2018 to September 2020 were retrospectively identified. NI-RADS scores at the primary site and lymph nodes were provided by 21 neuroradiologists. Recurrence status was based on clinical and imaging findings. Area under the curve (AUC) was used to assess accuracy.

RESULTS

We assessed 608 scans from 464 patients. For NI-RADS categories 1, 2, and 3, primary site recurrence rates were 5%, 29%, and 65% with AUC of 0.765, while lymph node recurrence rates were 3%, 10%, and 80% with AUC of 0.820.

CONCLUSIONS

NI-RADS as used by general neuroradiologists is effective in separating head and neck cancers into discrete categories for predicting recurrent disease.

Structured bone marrow report as an assessment tool in patients with hematopoietic disorders

BACKGROUND

There are multiple hematological and other entities (metastases, infections) that can affect the bone marrow (BM). The gold standard imaging technique for BM examination is magnetic resonance imaging (MRI). Technological advances have made it possible to digitalize image files and create applications that help to produce higher quality structured reports, facilitating the analysis of data and unifying the criteria collected, making it possible to fill an existing gap. The aim of this study is to present a structured report model applicable to BM studies by MRI.

METHODS

We have carried out a systematic review following the recommendations of the PRISMA checklist report to explore previous publications applying structured BM MRI reporting. Eligibility criteria: the selection of articles carried out by MeSH thesaurus. Original or review articles of BM pathology assessed by MRI. Our group with a wide experience in the evaluation of BM by MRI have designed a model for BM report using eight items: demographic data, diagnostic suspicion, technical data, type of exam initial or control, distribution and patterns involvement, complications and location, total assessment comments.

RESULTS

We have not found articles that reflect the existence of a structured report of BM examination by MRI. Only one descriptive article has been identified on guidelines for acquisition, interpretation and reporting which refers to a single entity. With the selected parameters, a software has been developed that allows to fill in the sections of the structured report with ease and immediacy and to send the result directly to the clinician.

DISCUSSION

Structured reports are the result of applying a logical structure to the radiological report, and the rules of elaboration comprise several criteria: (I) using a uniform language. The standardization of terminology avoids ambiguity in reporting and makes it easier to compare reports. (II) Accurately describe the radiological findings, following a prescribed order with review questions and answers. (III) Drafting using diagnostic screening tables. (IV) Respect the radiologists' workflow by facilitating the work and not hindering it. The final report of this work has been the product of the clinical-radiological collaboration in our working group.

Quantifying the Effects of Structured Reporting on Report Turnaround Times and Proofreading Workload in Neuroradiology

RATIONALE AND OBJECTIVES

To assess the effects of a change from free text reporting to structured reporting on resident reports, the proofreading workload and report turnaround times in the neuroradiology daily routine.

MATERIALS AND METHODS

Our neuroradiology section introduced structured reporting templates in July 2019. Reports dictated by residents during dayshifts from January 2019 to March 2020 were retrospectively assessed using quantitative parameters from report comparison. Through automatic analysis of text-string differences between report states (i.e. draft, preliminary and final report), Jaccard similarities and edit distances of reports following read-out sessions as well as after report sign-off were calculated. Furthermore, turnaround times until preliminary and final report availability to clinicians were investigated. Parameters were visualized as trending line graphs and statistically compared between reporting standards.

RESULTS

Three thousand five hundred thirty-eight reports were included into analysis. Mean Jaccard similarity of resident drafts and staff-reviewed final reports increased from 0.53 ± 0.37 to 0.79 ± 0.22 after the introduction of structured reporting (p < .001). Both mean overall edits on draft reports by residents following read-out sessions (0.30 ± 0.45 vs. 0.09 ± 0.29; p < .001) and by staff radiologists during report sign-off (0.17 ± 0.28 vs. 0.12 ± 0.23, p < .001) decreased. With structured reporting, mean turnaround time until preliminary report availability to clinicians decreased by 20.7 minutes (246.9 ± 207.0 vs. 226.2 ± 224.9; p < .001). Similarly, final reports were available 35.0 minutes faster on average (558.05 ± 15.1 vs. 523.0 ± 497.3; p = .002).

CONCLUSION

Structured reporting is beneficial in the neuroradiology daily routine, as resident drafts require fewer edits in the report review process. This reduction in proofreading workload is likely responsible for lower report turnaround times.

Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing

To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations.

How does DICOM support big data management? Investigating its use in medical imaging community

The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

[Structured reporting in radiology : German and European radiology societies' point of view]

Zahlreiche Publikationen belegen den herausragenden Wert einer strukturierten Befundung sowohl für die Kommunikation mit zuweisenden klinischen Kollegen als auch für die Weiterverwendung der Befunddaten in anderen Kontexten. Obwohl das Thema bereits seit vielen Jahren in der Radiologie bekannt ist, hat sich die strukturierte Befundung noch nicht flächendeckend in der klinischen Routine etablieren können. Alle größeren radiologischen Fachgesellschaften haben sich klar für die strukturierte Befundung ausgesprochen und verfolgen etliche Initiativen auf diesem Gebiet. Dazu zählt der Aufbau frei zugänglicher Sammlungen von Befundvorlagen und die Qualitätssicherung derselben sowie die Pflege und Entwicklung standardisierter Begriffslexika. Im vorliegenden Artikel werden insbesondere die Aktivitäten der Deutschen Röntgengesellschaft und der European Society of Radiology dargestellt sowie ein kurzer Überblick über Vor- und Nachteile und verfügbare Ressourcen gegeben.

ENT Residents Benefit from a Structured Operation Planning Approach in the Training of Functional Endoscopic Sinus Surgery

Background and Objectives: Preoperative planning utilizing computed tomographies (CT) is of utmost importance in functional endoscopic sinus surgery (FESS). Frequently, no uniform documentation and planning structures are available to residents in training. Consequently, overall completeness and quality of operation planning may vary greatly. The objective of the present study was to evaluate the impact of a structured operation planning (SOP) approach on the report quality and user convenience during a 4-day sinus surgery course. Materials and Methods: Fifteen participant were requested to plan a FESS procedure based on a CT scan of the paranasal sinuses that exhibited common pathological features, in a conventional manner, using a free text. Afterwards, the participants reevaluated the same scans by means of a specifically designed structured reporting template. Two experienced ENT surgeons assessed the collected conventional operation planning (COP) and SOP methods independently with regard to time requirements, overall quality, and legibility. User convenience data were collected by utilizing visual analogue scales. Results: A significantly greater time expenditure was associated with SOPs (183 s vs. 297 s, p = 0.0003). Yet, legibility (100% vs. 72%, p < 0.0001) and overall completeness (61.3% vs. 22.7%, p < 0.0001) of SOPs was significantly superior to COPs. Additionally, description of highly relevant variants in anatomy and pathologies were outlined in greater detail. User convenience data delineated a significant preference for SOPs (VAS 7.9 vs. 6.9, p = 0.0185). Conclusions: CT-based planning of FESS procedures by residents in training using a structured approach is more time-consuming while producing a superior report quality in terms of detailedness and readability. Consequently, SOP can be considered as a valuable tool in the process of preoperative evaluations, especially within residency.

[Integration of structured reporting into the routine radiological workflow]

Klinisches/methodisches Problem

Strukturierte Befundung ist seit Jahren eines der meist diskutierten Themen in der Radiologie. Aktuell herrscht ein Mangel an nutzerfreundlichen Softwarelösungen, welche in die bestehende IT-Infrastruktur der Kliniken und Praxen integriert sind und effiziente Dateneingaben erlauben.

Radiologische Standardverfahren

Radiologische Befunde werden meist als Freitext über Spracherkennungssysteme diktiert oder per Tastatur eingegeben. Zudem werden Textbausteine für die Erstellung von Normalbefunden verwendet und bei Bedarf durch Freitextinhalte ergänzt.

Methodische Innovationen

Softwarebasierte Befundungssysteme können Spracherkennungssysteme mit radiologischen Befundvorlagen in Form von interaktiven Entscheidungsbäumen vereinen. Eine technische Integration in RIS(Radiologieinformationssystem)-, PACS(„picture archiving and communication system“)- und AV(„advanced visualization“)-Systeme über Programmierschnittstellen und Interoperabilitätsstandards ermöglicht effiziente Prozesse und die Generierung maschinenlesbarer Befunddaten.

Leistungsfähigkeit

Strukturierte, semantisch annotierte klinische Daten, die über ein strukturiertes Befundungssystem erhoben werden, stehen unmittelbar für epidemiologische Datenauswertungen und kontinuierliches KI(Künstliche Intelligenz)-Training zur Verfügung.

Bewertung

Der Einsatz der strukturierten Befundung in der radiologischen Routinediagnostik ist mit einer initialen Umstellungsphase verbunden. Eine erfolgreiche Implementierung setzt eine enge Verzahnung der technischen Infrastruktur mehrerer Systeme voraus.

Empfehlung für die Praxis

Durch die Nutzung einer hybriden, softwarebasierten Befundungslösung können radiologische Befunde mit unterschiedlichen Stufen der Struktur generiert werden. Klinische Fragestellungen oder Informationen können aus klinischen Subsystemen semiautomatisch übertragen werden, um vermeidbare Fehler zu eliminieren und die Produktivität zu erhöhen.

Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

Noninterpretive Uses of Artificial Intelligence in Radiology

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.

Structured Reporting of Computed Tomography Examinations in Post-Lung Transplantation Patients

OBJECTIVE

The aim of this study was to evaluate the benefits and potential of structured reports (SR) for chest computed tomography after lung transplantation.

METHODS

Free-text reports (FTR) and SR were generated for 49 computed tomography scans. Clinical routine reports were used as FTR. Two pulmonologists rated formal aspects, completeness, clinical utility, and overall quality. Wilcoxon and McNemar tests were used for statistical analysis.

RESULTS

Structured reports received significantly higher ratings for all formals aspects (P < 0.001, respectively). Completeness was higher in SR with regard to evaluation of bronchiectases, bronchial anastomoses, bronchiolitic and fibrotic changes (P < 0.001, respectively), and air trapping (P = 0.012), but not signs of pneumonia (P = 0.5). Clinical utility and overall quality were rated significantly higher for SR than FTR (P < 0.001, respectively). However, report type did not influence initiation of further diagnostic or therapeutic measures (P = 0.307 and 1.0).

CONCLUSIONS

Structured reports are superior to FTR with regard to formal aspects, completeness, clinical utility, and overall satisfaction of referring pulmonologists.

Structured Reporting of Acute Ischemic Stroke - Consensus-Based Reporting Templates for Non-Contrast Cranial Computed Tomography, CT Angiography, and CT Perfusion

PURPOSE

 Structured reporting is an essential step in establishing standardized quality standards in diagnostic radiology. The German Society of Radiology and the German Society of Neuroradiology aim to provide templates for the structured reporting of different radiological examinations.

METHOD

 The Information Technology working group of the German Society of Radiology developed structured templates for the radiological reporting of different indications in consensus with specialist support by experts.

RESULTS

 We present a template for the structured reporting of examinations of patients with acute ischemic stroke by non-contrast computed tomography, CT angiography, and CT perfusion. This template is provided on the website www.befundung.drg.de for free use.

CONCLUSION

 Implementation of the structured template may increase quality and provide a minimum standard for radiological reports in patients with acute ischemic stroke.

KEY POINTS

· The German Society of Radiology and the German Society of Neuroradiology are providing support for the development of structured templates in German.. · We present a template for the structured reporting of examinations of patients with acute ischemic stroke by non-contrast computed tomography, CT angiography, and CT perfusion. This template is provided on the website www.befundung.drg.de for free use.. · Implementation of the structured template may increase quality and provide a minimum standard for radiological reports in patients with acute ischemic stroke..

CITATION FORMAT

· Brendle C, Bender B, Selo N et al. Structured Reporting of Acute Ischemic Stroke - Consensus-Based Reporting Templates for Non-Contrast Cranial Computed Tomography, CT Angiography, and CT Perfusion. Fortschr Röntgenstr 2021; 193: 1315 - 1317.

Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal

Simple Summary

Structured reporting in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Abstract

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated. Results: After the second Delphi round of the SR RC staging, the panelists’ single scores and sum of scores were 3.8 (range 2–4) and 169, and the SR RC restaging panelists’ single scores and sum of scores were 3.7 (range 2–4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 (p < 0.01) and 0.82 (p < 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging. Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.

Structured Reporting in the Characterization of Renal Cysts by Contrast-Enhanced Ultrasound (CEUS) Using the Bosniak Classification System-Improvement of Report Quality and Interdisciplinary Communication

BACKGROUND

This study aims to evaluate the potential benefits of structured reporting (SR) compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) of cystic renal lesions, based on the Bosniak classification.

METHODS

Fifty patients with cystic renal lesions who underwent CEUS were included in this single-center study. FTR created in clinical routine were compared to SR retrospectively generated by using a structured reporting template. Two experienced urologists evaluated the reports regarding integrity, effort for information extraction, linguistic quality, and overall quality.

RESULTS

The required information could easily be extracted by the reviewers in 100% of SR vs. 82% of FTR (p < 0.001). The reviewers trusted the information given by SR significantly more with a mean of 5.99 vs. 5.52 for FTR (p < 0.001). SR significantly improved the linguistic quality (6.0 for SR vs. 5.68 for FTR (p < 0.001)) and the overall report quality (5.98 for SR vs. 5.58 for FTR (p < 0.001)).

CONCLUSIONS

SR significantly increases the quality of radiologic reports in CEUS examinations of cystic renal lesions compared to conventional FTR and represents a promising approach to facilitate interdisciplinary communication in the future.

Structured Reporting Using CEUS LI-RADS for the Diagnosis of Hepatocellular Carcinoma (HCC)-Impact and Advantages on Report Integrity, Quality and Interdisciplinary Communication

Simple Summary

Contrast-enhanced ultrasound (CEUS) is an increasingly accepted imaging modality for visualizing hepatocellular carcinoma (HCC) and is recommended as a secondary imaging option by most leading hepatology societies. In recent years, the use of structured reporting (SR) has been recommended by several societies to standardize report content and improve report quality of various diagnostic modalities when compared to conventional free-text reports (FTR). Our single-center study aimed to evaluate the use of SR using a CEUS LI-RADS software template in CEUS examinations of 50 HCC patients. SR significantly increased report integrity, satisfaction of the referring physicians, linguistic quality and overall report quality compared to FTR. Therefore, the use of SR in CEUS examinations of HCC patients may represent a valuable tool to facilitate clinical decision-making and improve interdisciplinary communication in the future.

Abstract

Background: Our retrospective single-center study aims to evaluate the impact of structured reporting (SR) using a CEUS LI-RADS template on report quality compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) for the diagnosis of hepatocellular carcinoma (HCC). Methods: We included 50 patients who underwent CEUS for HCC staging. FTR created after these examinations were compared to SR retrospectively generated by using template-based online software with clickable decision trees. The reports were evaluated regarding report completeness, information extraction, linguistic quality and overall report quality by two readers specialized in internal medicine and visceral surgery. Results: SR significantly increased report completeness with at least one key feature missing in 31% of FTR vs. 2% of SR (p < 0.001). Information extraction was considered easy in 98% of SR vs. 86% of FTR (p = 0.004). The trust of referring physicians in the report was significantly increased by SR with a mean of 5.68 for SR vs. 4.96 for FTR (p < 0.001). SR received significantly higher ratings regarding linguistic quality (5.79 for SR vs. 4.83 for FTR (p < 0.001)) and overall report quality (5.75 for SR vs. 5.01 for FTR (p < 0.001)). Conclusions: Using SR instead of conventional FTR increases the overall quality of reports in CEUS examinations of HCC patients and may represent a valuable tool to facilitate clinical decision-making and improve interdisciplinary communication in the future.

[Quality in the appraisal of head and neck sonography results in university hospitals-a random sample]

Hintergrund

Die Ultraschalldiagnostik gilt für den Radiologen, Hals-Nasen-Ohren-Arzt (HNO) oder Mund-Kiefer-Gesichts-Chirurgen als Standard in der Abklärung zahlreicher Pathologien. Es besteht ein Konsens, dass die digitale Dokumentation heute dringend notwendig ist, um die Qualität der sonographischen Dokumentationen zu verbessern und zu standardisieren. Es häufen sich Publikationen zur Implementierung standardisierter Befunddokumentation einschließlich der Kopf- und Halssonographie.

Ziel der Arbeit

Die vorliegende Arbeit zielt darauf ab, die Qualität von routinemäßig angefertigten Kopf- und Halssonographiebefunden nach Kriterien der Kassenärztlichen Vereinigung (KV) Bayern an einer Auswahl deutscher HNO-Universitätskliniken stichprobenartig zu ermitteln.

Material und Methoden

Insgesamt wurden retrospektiv 70 zufällig ausgewählte, anonymisierte schriftliche Befunde einschließlich Bildmaterial von insgesamt 7 HNO-Universitätskliniken stichprobenartig nach KV-Kriterien durch einen erfahrenen Prüfer der KV Bayern ausgewertet und deskriptiv analysiert.

Ergebnisse

Von 70 Befunden konnten 69 ausgewertet werden. Die Dokumentationsvollständigkeit lag im Mittel bei 80,6 %. Neun Befunde waren vollständig korrekt dokumentiert (13 %). Die Dokumentationsvollständigkeit der einzelnen Kliniken lag zwischen 68,1 % und 93 %. Mit 88,5 % vs. 75 % erbrachte eine strukturierte Befundung eine höhere Befundvollständigkeit. In 75 % der Fälle verfügten die Kliniken mit strukturiertem Befund auch über digitale Dokumentationslösungen.

Schlussfolgerung

Die Vollständigkeit und Qualität von routinemäßig angefertigten Kopf- und Halssonographiebefunden an einer Auswahl von HNO-Universitätskliniken ist insgesamt optimierbar. Die Implementierung strukturierter Befundmasken und die Umstellung der analogen Dokumentation auf digitale Lösungen sowie Vernetzung mit dem Klinikinformationssystem (KIS) und Bildarchivierungs- und Kommunikationssystem (PACS) sollte weiter vorangetrieben werden. Darüber hinaus sind leitende Ärzte dazu angehalten, die Befundqualität unerfahrener Kollegen regelmäßig zu prüfen und im Rahmen der Facharztausbildung auf die Erfüllung entsprechender Standards wie der KV-Ultraschallvereinbarung hinzuarbeiten.

Structured reporting has the potential to reduce reporting times of dual-energy x-ray absorptiometry exams

BACKGROUND

In recent years, structured reporting has been shown to be beneficial with regard to report completeness and clinical decision-making as compared to free-text reports (FTR). However, the impact of structured reporting on reporting efficiency has not been thoroughly evaluted yet. The aim of this study was to compare reporting times and report quality of structured reports (SR) to conventional free-text reports of dual-energy x-ray absorptiometry exams (DXA).

METHODS

FTRs and SRs of DXA were retrospectively generated by 2 radiology residents and 2 final-year medical students. Time was measured from the first view of the exam until the report was saved. A random sample of DXA reports was selected and sent to 2 referring physicians for further evaluation of report quality.

RESULTS

A total of 104 DXA reports (both FTRs and SRs) were generated and 48 randomly selected reports were evaluated by referring physicians. Reporting times were shorter for SRs in both radiology residents and medical students with median reporting times of 2.7 min (residents: 2.7, medical students: 2.7) for SRs and 6.1 min (residents: 5.0, medical students: 7.5) for FTRs. Information extraction was perceived to be significantly easier from SRs vs FTRs (P <  0.001). SRs were rated to answer the clinical question significantly better than FTRs (P <  0.007). Overall report quality was rated significantly higher for SRs compared to FTRs (P <  0.001) with 96% of SRs vs 79% of FTRs receiving high or very high-quality ratings. All readers except for one resident preferred structured reporting over free-text reporting and both referring clinicians preferred SRs over FTRs for DXA.

CONCLUSIONS

Template-based structured reporting of DXA might lead to shorter reporting times and increased report quality.

Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation

Artificial intelligence (AI) is a broad field of computational science that includes many subsets. Today the most widely used subset in medical imaging is machine learning (ML). Many articles have focused on the use of ML for pattern recognition to detect and potentially diagnose various pathologies. However, AI algorithm development is now directed toward workflow management. AI can impact patient care at multiple stages of their imaging experience and assist in efficient and effective scheduling, imaging performance, worklist prioritization, image interpretation, and quality assurance. The purpose of this manuscript was to review the potential AI applications in radiology focusing on workflow management and discuss how ML will affect cardiothoracic imaging.

[Back pain : State of the art and guidelines]

Pain in general and back pain in particular are associated with a variety of pathological, clinical, and sociocultural factors. There are numerous clinical and therapeutic treatment as well as imaging-options available and comprehensive knowledge is required to meet the individual clinical needs of those affected. This requires a high degree of interdisciplinary cooperation. In addition, back pain is covered differently by various numbers of insurance companies. Imaging methods, including the example of periradicular image-assisted interventions, are presented with regard to their indication and efficiency. The existing guidelines and evaluation recommendations with different structural and targeted approaches are discussed in addition to extensive legal aspects in the literature. In addition, the structured reports and the certificated curricula of the AG Bildgebende Verfahren des Bewegungsapparates (Working Group "Imaging Procedures of the Musculoskeletal System") of the Deutsche Röntgengesellschaft ("German Society of Radiology") are recommended for the quality assurance.

The use of structured reporting of head and neck ultrasound ensures time-efficiency and report quality during residency

PURPOSE

Free text reports (FTR) of head and neck ultrasound studies are currently deployed in most departments. Because of a lack of composition and language, these reports vary greatly in terms of quality and reliability. This may impair the learning process during residency. The purpose of the study was to analyze the longitudinal effects of using structured reports (SR) of head and neck ultrasound studies during residency.

METHODS

Attending residents (n = 24) of a tripartite course on head and neck ultrasound, accredited by the German Society for Ultrasound in Medicine (DEGUM), were randomly allocated to pictures of common diseases. Both SRs and FTRs were compiled. All reports were analyzed concerning completeness, acquired time and legibility. Overall user contentment was evaluated by a questionnaire.

RESULTS

SRs achieved significantly higher ratings regarding completeness (95.6% vs. 26.4%, p < 0.001), description of pathologies (72.2% vs. 58.9%, p < 0.001) and legibility (100% vs. 52.4%, p < 0.001) with a very high inter-rater reliability (Fleiss' kappa 0.9). Reports were finalized significantly faster (99.1 s vs. 115.0 s, p < 0.001) and user contentment was significantly better when using SRs (8.3 vs. 6.3, p < 0.001). In particular, only SRs showed a longitudinally increasing time efficiency (- 20.1 s, p = 0.036) while maintaining consistent completeness ratings.

CONCLUSIONS

The use of SRs of head and neck ultrasound studies results in an increased longitudinal time-efficiency while upholding the report quality at the same time. This may indicate an additive learning effect of structured reporting. Superior outcomes in terms of comprehensiveness, legibility and time-efficiency can be observed immediately after implementation.

Strategies for Implementing a Standardized Structured Radiology Reporting Program

Radiology practices are increasingly implementing standardized report templates to overcome the drawbacks of individual templates. However, implementing a standardized structured reporting program is not necessarily straightforward. This article provides practical guidance for radiologists who wish to implement standardized structured reporting in their practice. Challenges that radiology groups encounter tend to fall into two categories: technical and organizational. Defining and carrying out technical work can be tedious but tends to be relatively straightforward, whereas overcoming organizational challenges often requires changes in individuals' strongly held values, beliefs, roles, and relationships. Established organizational change models can help frame the organizational strategy to implement a standardized structured reporting program. Once leadership support is secured, a standardized structured reporting committee can be convened to establish report priorities, standards, design principles, and guidelines. Report standards help to establish the common framework upon which all report templates are constructed, helping to ensure report consistency. By using these standards, committee members can create reports relevant to their subspecialties, which can then be edited for formatting and content. Once report templates have been developed, edited, and published, an abbreviated form of the same process can be used to maintain the reports, which can be accomplished with much less effort than that initially required to create the templates. After standardized structured report templates are implemented and become embedded in practice, most radiologists eventually appreciate the merits of the program. ^©RSNA, 2018.

Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs

BACKGROUND

Data used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application.

MATERIALS AND METHODS

We included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution's picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs.

RESULTS

Once implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634-1.000) for detection of fractures.

CONCLUSION

We demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.

Standard Lexicons, Coding Systems and Ontologies for Interoperability and Semantic Computation in Imaging

Standard clinical terms, codes, and ontologies promote clarity and interoperability. Within radiology, there is a variety of relevant content resources, tools and technologies. These provide the basis for fundamental imaging workflows such as reporting and billing, and also facilitate a range of applications in quality improvement and research. This article reviews the key characteristics of lexicons, coding systems, and ontologies. A number of standards are described, including International Classification of Diseases-10-Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and RadLex. Tools for accessing this material are reviewed, such as the National Center for Biomedical Ontology BioPortal system. Web services are discussed as a mechanism for semantic application development. Several example systems, workflows, and research applications using semantic technology are also surveyed.

Impact of structured reporting on developing head and neck ultrasound skills

BACKGROUND

Reports of head and neck ultrasound examinations are frequently written by hand as free texts. This is a serious obstacle to the learning process of the modality due to a missing report structure and terminology. Therefore, there is a great inter-observer variability in overall report quality. Aim of the present study was to evaluate the impact of structured reporting on the learning process as indicated by the overall report quality of head and neck ultrasound examinations within medical school education.

METHODS

Following an immersion course on head and neck ultrasound, previously documented images of three common pathologies were handed out to 58 medical students who asked to create both standard free text reports (FTR) and structured reports (SR). A template for structured reporting of head and neck ultrasound examinations was created using a web-based approach. FTRs and SRs were evaluated with regard to overall quality, completeness, required time to completion and readability by two independent raters (Paired Wilcoxon test, 95% CI). Ratings were assessed for inter-rater reliability (Fleiss' kappa). Additionally, a questionnaire was utilized to evaluate user satisfaction.

RESULTS

SRs received significantly better ratings in terms of report completeness (97.7% vs. 53.5%, p < 0.001) regarding all items. In addition, pathologies were described in more detail using SRs (70% vs. 51.1%, p < 0.001). Readability was significantly higher in all SRs when compared to FTRs (100% vs. 54.4%, p < 0.001). Mean time to complete was significantly lower (79.6 vs. 205.4 s, p < 0.001) and user satisfaction was significantly higher when using SRs (8.5 vs. 4.1, p < 0.001). Also, inter-rater reliability was very high (Fleiss' kappa 0.93).

CONCLUSIONS

SRs of head and neck ultrasound examinations provide more detailed information with a better readability in a time-saving manner within medical education. Also, medical students may benefit from SRs in their learning process due to the structured approach and standardized terminology.

Structured reporting of head and neck ultrasound examinations

Background

Reports of head and neck ultrasound examinations are frequently written by hand as free texts. Naturally, quality and structure of free text reports is variable, depending on the examiner’s individual level of experience. Aim of the present study was to compare the quality of free text reports (FTR) and structured reports (SR) of head and neck ultrasound examinations.

Methods

Both standard FTRs and SRs of head and neck ultrasound examinations of 43 patients were acquired by nine independent examiners with comparable levels of experience. A template for structured reporting of head and neck ultrasound examinations was created using a web-based approach. FTRs and SRs were evaluated with regard to overall quality, completeness, required time to completion, and readability by four independent raters with different specializations (Paired Wilcoxon test, 95% CI) and inter-rater reliability was assessed (Fleiss’ kappa). A questionnaire was used to compare FTRs vs. SRs with respect to user satisfaction (Mann-Whitney U test, 95% CI).

Results

By comparison, completeness scores of SRs were significantly higher than FTRs’ completeness scores (94.4% vs. 45.6%, p < 0.001), and pathologies were described in more detail (91.1% vs. 54.5%, p < 0.001). Readability was significantly higher in all SRs when compared to FTRs (100% vs. 47.1%, p < 0.001). The mean time to complete a report, however, was significantly higher in SRs (176.5 vs. 107.3 s, p < 0.001). SRs achieved significantly higher user satisfaction ratings (VAS 8.87 vs. 1.41, p < 0.001) and a very high inter-rater reliability (Fleiss’ kappa 0.92).

Conclusions

As compared to FTRs, SRs of head and neck ultrasound examinations are more comprehensive and easier to understand. On the balance, the additional time needed for completing a SR is negligible. Also, SRs yield high inter-rater reliability and may be used for high-quality scientific data analyses.

LI-RADS: a great opportunity not to be missed

The Liver Imaging Reporting and Data System (LI-RADS) is a widespread comprehensive system for standardising the reporting and data collection of liver imaging to standardise chronic liver disease evaluation. However, the LI-RADS, based on the identification of some categories of lesions by means of a conceptual and nonquantitative probability approach, has many limitations. In fact, recently, the European Association for the Study of the Liver Guidelines regarding the management of hepatocellular carcinoma did not accept the LI-RADS. The aim of this paper was to critically analyse the LI-RADS, focusing on some interesting issues such as the absence of a clear distinction between two different imaging modalities (computed tomography and MRI), the lack of validation of some major features, the assessment of its ancillary features and its complexity. Despite these limitations, the LI-RADS represents a great opportunity for the radiological community. We must not let it escape, but time and experience are necessary for its improvement.

Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology

The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA's role in leading, organizing, and catalyzing change during this important time in radiology. © RSNA, 2019.

Big data, artificial intelligence, and structured reporting

The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.

Objective Comparison Using Guideline-based Query of Conventional Radiological Reports and Structured Reports

BACKGROUND

This feasibility study of text-mining-based scoring algorithm provides an objective comparison of structured reports (SR) and conventional free-text reports (cFTR) by means of guideline-based key terms. Furthermore, an open-source online version of this ranking algorithm was provided with multilingual text-retrieval pipeline, customizable query and real-time-scoring.

MATERIALS AND METHODS

Twenty-five patients with suspected stroke and magnetic resonance imaging were re-assessed by two independent/blinded readers [inexperienced: 3 years; experienced >6 years/Board-certified). SR and cFTR were compared with guideline-query using the cosine similarity score (CSS) and Wilcoxon signed-rank test.

RESULTS

All pathological findings (18/18) were identified by SR and cFTR. The impressions section of the SRs of the inexperienced reader had the highest median (0.145) and maximal (0.214) CSS and were rated significantly higher (p=2.21×10^-5 and p=1.4×10^-4, respectively) than cFTR (median=0.102). CSS was robust to variations of query.

CONCLUSION

Objective guideline-based comparison of SRs and cFTRs using the CSS is feasible and provides a scalable quality measure that can facilitate the adoption of structured reports in all fields of radiology.

Structured Reporting of IR Procedures: Effect on Report Compliance, Accuracy, and Satisfaction

PURPOSE

To compare effect of free-text versus structured reporting of IR procedures on report quality and report coding and value.

MATERIALS AND METHODS

In this retrospective study, 432 common consecutive free-text IR reports created during 4 months (from September 2013 to December 2013) before implementation of structured reporting (February 2014) and 415 structured IR reports created after implementation (from September 2014 to December 2014) were reviewed to assess ease of use and compliance with reporting requirements for regulatory requirements and coding. IR staff and trainees and referring physicians to IR were surveyed on procedure report attributes, such as detail, quality, and clarity.

RESULTS

Structured reporting increased compliance with reporting fluoroscopy time, radiation dose, and contrast administration compared with free-text reports (402/432 [93.1%] vs 251/415 [60.5%], P < .001; 402/432 [93.1%] vs 242/415 [58.3%], P < .001; and 395/432 [91.4%] vs 257/415 [61.9%], P < .001). Structured reporting decreased addendum requests for insufficient documentation from 43% (121/435 [28%] to 50/415 [12%], P = .01). Most IR physicians found structured reports to require less time to complete (21/26 [81%]), to be easier to complete (23/26 [89%]), and to have a similar or higher level of detail (19/26 [73%]) compared with free-text reports. Referring physicians were more satisfied with structured reports compared with free-text reports (6.9/10 vs 5.6/10, P = .03).

CONCLUSIONS

Structured IR reporting compared with free-text reporting improves compliance with radiation dose and contrast reporting, reporting and coding efficiency, and satisfaction among IR and referring physicians.