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Background: Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports' classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification., Results: The final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607)., Conclusion: In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further., (© 2024. The Author(s).)

Introduction and Purpose: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security., Methods and Results: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing., Discussion and Conclusions: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background and Purpose: The Brain Tumor Reporting and Data System (BT-RADS) is a structured radiology reporting algorithm that was introduced to provide uniformity in posttreatment primary brain tumor follow-up and reporting, but its interrater reliability (IRR) assessment has not been widely studied. Our goal is to evaluate the IRR among neuroradiologists and radiology residents in the use of BT-RADS., Materials and Methods: This retrospective study reviewed 103 consecutive MR studies in 98 adult patients previously diagnosed with and treated for primary brain tumor (January 2019 to February 2019). Six readers with varied experience (4 neuroradiologists and 2 radiology residents) independently evaluated each case and assigned a BT-RADS score. Readers were blinded to the original score reports and the reports from other readers. Cases in which at least 1 neuroradiologist scored differently were subjected to consensus scoring. After the study, a post hoc reference score was also assigned by 2 readers by using future imaging and clinical information previously unavailable to readers. The interrater reliabilities were assessed by using the Gwet AC2 index with ordinal weights and percent agreement., Results: Of the 98 patients evaluated (median age, 53 years; interquartile range, 41-66 years), 53% were men. The most common tumor type was astrocytoma (77%) of which 56% were grade 4 glioblastoma. Gwet index for interrater reliability among all 6 readers was 0.83 (95% CI: 0.78-0.87). The Gwet index for the neuroradiologists' group (0.84 [95% CI: 0.79-0.89]) was not statistically different from that for the residents' group (0.79 [95% CI: 0.72-0.86]) (χ 2 = 0.85; P = .36). All 4 neuroradiologists agreed on the same BT-RADS score in 57 of the 103 studies, 3 neuroradiologists agreed in 21 of the 103 studies, and 2 neuroradiologists agreed in 21 of the 103 studies. Percent agreement between neuroradiologist blinded scores and post hoc reference scores ranged from 41%-52%., Conclusions: A very good interrater agreement was found when tumor reports were interpreted by independent blinded readers by using BT-RADS criteria. Further study is needed to determine if this high overall agreement can translate into greater consistency in clinical care., (© 2024 by American Journal of Neuroradiology.)

Over the last decades, magnetic resonance imaging (MRI) has emerged as a valuable adjunct to prenatal ultrasound for evaluating fetal malformations. Several radiological societies advocate for standardised and structured reporting practices to enhance the uniformity of imaging language. Compared to narrative formats, standardised and structured reports offer enhanced content quality, minimise reader variability, have the potential to save reporting time, and streamline the communication between specialists by employing a shared lexicon. Structured reporting holds promise for mitigating medico-legal liability, while also facilitating rigorous scientific data analyses and the development of standardised databases. While structured reporting templates for fetal MRI are already in use in some centres, specific recommendations and/or guidelines from international societies are scarce in the literature. The purpose of this paper is to propose a standardised and structured reporting template for fetal MRI to assist radiologists, particularly those with less experience, in delivering systematic reports. Additionally, the paper aims to offer an overview of the anatomical structures that necessitate reporting and the prevalent normative values for fetal biometrics found in current literature., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma., Methods: Our work aims to leverage the potential of Natural Language Processing and Transformer-based models to deal with automatic SR registry filling. With the availability of 174 Italian radiology reports, we investigate a rule-free generative Question Answering approach based on the Italian-specific version of T5: IT5. To address information content discrepancies, we focus on the six most frequently filled items in the annotations made on the reports: three categorical (multichoice), one free-text (free-text), and two continuous numerical (factual). In the preprocessing phase, we encode also information that is not supposed to be entered. Two strategies (batch-truncation and ex-post combination) are implemented to comply with the IT5 context length limitations. Performance is evaluated in terms of strict accuracy, f1, and format accuracy, and compared with the widely used GPT-3.5 Large Language Model. Unlike multichoice and factual, free-text answers do not have 1-to-1 correspondence with their reference annotations. For this reason, we collect human-expert feedback on the similarity between medical annotations and generated free-text answers, using a 5-point Likert scale questionnaire (evaluating the criteria of correctness and completeness)., Results: The combination of fine-tuning and batch splitting allows IT5 ex-post combination to achieve notable results in terms of information extraction of different types of structured data, performing on par with GPT-3.5. Human-based assessment scores of free-text answers show a high correlation with the AI performance metrics f1 (Spearman's correlation coefficients>0.5, p-values<0.001) for both IT5 ex-post combination and GPT-3.5. The latter is better at generating plausible human-like statements, even if it systematically provides answers even when they are not supposed to be given., Conclusions: In our experimental setting, a fine-tuned Transformer-based model with a modest number of parameters (i.e., IT5, 220 M) performs well as a clinical information extraction system for automatic SR registry filling task. It can extract information from more than one place in the report, elaborating it in a manner that complies with the response specifications provided by the SR registry (for multichoice and factual items), or that closely approximates the work of a human-expert (free-text items); with the ability to discern when an answer is supposed to be given or not to a user query., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Designing a framework representing radiology results in a standards-based data structure using joint Radiological Society of North America/American College of Radiology Common Data Elements (CDEs) as the semantic labels on standard structures. This allows radiologist-created report data to integrate with artificial intelligence-generated results for use throughout downstream systems., Materials and Methods: We developed a framework modeling radiology findings as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) observations using CDE set/element identifiers as standardized semantic labels. This framework deploys CDE identifiers to specify radiology findings and attributes, providing consistent labels for radiology report concepts-diagnoses, recommendations, tabular/quantitative data-with built-in integration with RadLex, SNOMED CT, LOINC, and other ontologies. Observation structures fit within larger HL7 FHIR DiagnosticReport resources, providing output including both nuanced text and structured data., Results: Labeling radiology findings as discrete data for interchange between systems requires two components: structure and semantics. CDE definitions provide semantic identifiers for findings and their component values. The FHIR observation resource specifies a structure for associating identifiers with radiology findings in the context of reports, with CDE-encoded observations referring to definitions for CDE identifiers in a central repository. The discussion includes an example of encoding pulmonary nodules on a chest CT as CDE-labeled observations, demonstrating the application of this framework to exchange findings throughout the imaging workflow, making imaging data available to downstream clinical systems., Discussion: CDE-labeled observations establish a lingua franca for encoding, exchanging, and consuming radiology data at the level of individual findings, facilitating use throughout healthcare systems., Importance: CDE-labeled FHIR observation objects can increase the value of radiology results by facilitating their use throughout patient care., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. LungCT ScreeningReporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement. Unfortunately, due to variability in radiology reports, extraction of Lung-RADS scores is non-trivial, and best practices do not exist. The purpose of this project is to compare mechanisms to extract Lung-RADS scores from free-text radiology reports., Methods: We retrospectively analyzed reports of LCS low-dose computed tomography (LDCT) examinations performed at a multihospital integrated healthcare network in New York State between January 2016 and July 2023. We compared three methods of Lung-RADS score extraction: manual physician entry at time of report creation, manual LCS specialist entry after report creation, and an internally developed, rule-based natural language processing (NLP) algorithm. Accuracy, recall, precision, and completeness (i.e., the proportion of LCS exams to which a Lung-RADS score has been assigned) were compared between the three methods., Results: The dataset includes 24,060 LCS examinations on 14,243 unique patients. The mean patient age was 65 years, and most patients were male (54 %) and white (75 %). Completeness rate was 65 %, 68 %, and 99 % for radiologists' manual entry, LCS specialists' entry, and NLP algorithm, respectively. Accuracy, recall, and precision were high across all extraction methods (>94 %), though the NLP-based approach was consistently higher than both manual entries in all metrics., Discussion: An NLP-based method of LCS score determination is an efficient and more accurate means of extracting Lung-RADS scores than manual review and data entry. NLP-based methods should be considered best practice for extracting structured Lung-RADS scores from free-text radiology reports., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors have no conflicts of interest related to the submission of this manuscript. Dr. Bade is a site PI on industry-sponsored trials for Nucleix, Delfi Diagnostics, and Biodesix., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background:  The purpose of this article is to review the different bone tumor radiology reporting systems [Bone Reporting and Data System (Bone-RADS), Osseous Tumor Reporting and Data System (OT-RADS), Solitary Bone Tumor Imaging Reporting and Data System (BTI-RADS), and Radiological Evaluation Score for Bone Tumors (REST)] and summarize their advantages and disadvantages., Methods:  A selective search of PubMed was performed for literature regarding the definition and discussion of bone tumor reporting systems. No time frame was selected, but the search was particularly focused on current literature on musculoskeletal radiology lexicon., Results:  To date, four major reporting systems has been proposed to standardize and systematize the reporting of imaging studies of bone tumors: Bone-RADS, OT-RADS, BTI-RADS, and REST. Both Bone-RADS and OT-RADS aid in the characterization and management of bone lesions on CT and MRI. OT-RADS and REST can be applied to MRI and radiography, respectively., Conclusion:  Radiologists play a central role in the detection and characterization of asymptomatic (or incidentally detected) and symptomatic bone tumors. There are several existing bone tumor reporting systems with various advantages and disadvantages including emphasis on lesion characterization as well as management of incidentally detected bone lesions., Key Points:   · Four bone tumor reporting systems have been proposed thus far.. · Bone-RADS guides management of incidental bone lesions on CT and MRI.. · OT-RADS guides management of bone lesions on MRI with high accuracy.. · BTI-RADS classifies bone tumors on CT and MRI.., Citation Format: · Ghasemi A, Ahlawat S, . Bone Reporting and Data System (Bone-RADS) and Other Proposed Practice Guidelines for Reporting Bone Tumors. Fortschr Röntgenstr 2024; 196: 1134 - 1142., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives., (© RSNA, 2024.)

Objective: Radiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence (AI) models like GPT-4 (Generative Pre-trained Transformer 4), there is growing interest in evaluating their potential for optimizing or generating radiology reports. This study aimed to compare the quality and content of radiologist-generated and GPT-4 AI-generated radiology reports., Methods: A comparative study design was employed in the study, where a total of 100 anonymized radiology reports were randomly selected and analyzed. Each report was processed by GPT-4, resulting in the generation of a corresponding AI-generated report. Quantitative and qualitative analysis techniques were utilized to assess similarities and differences between the two sets of reports., Results: The AI-generated reports showed comparable quality to radiologist-generated reports in most categories. Significant differences were observed in clarity (p = 0.027), ease of understanding (p = 0.023), and structure (p = 0.050), favoring the AI-generated reports. AI-generated reports were more concise, with 34.53 fewer words and 174.22 fewer characters on average, but had greater variability in sentence length. Content similarity was high, with an average Cosine Similarity of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore F1 of 0.8775., Conclusion: The results of this proof-of-concept study suggest that GPT-4 can be a reliable tool for generating standardized radiology reports, offering potential benefits such as improved efficiency, better communication, and simplified data extraction and analysis. However, limitations and ethical implications must be addressed to ensure the safe and effective implementation of this technology in clinical practice., Clinical Relevance Statement: The findings of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an advanced AI model, has the potential to significantly contribute to the standardization and optimization of radiology reporting, offering improved efficiency and communication in clinical practice., Key Points: • Large language model-generated radiology reports exhibited high content similarity and moderate structural resemblance to radiologist-generated reports. • Performance metrics highlighted the strong matching of word selection and order, as well as high semantic similarity between AI and radiologist-generated reports. • Large language model demonstrated potential for generating standardized radiology reports, improving efficiency and communication in clinical settings., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

BI-RADS® is a standardization system for breast imaging reports and results created by the American College of Radiology to initially address the lack of uniformity in mammography reporting. The system consists of a lexicon of descriptors, a reporting structure with final categories and recommended management, and a structure for data collection and auditing. It is accepted worldwide by all specialties involved in the care of breast diseases. Its implementation is related to the Mammography Quality Standards Act initiative in the United States (1992) and breast cancer screening. After its initial creation in 1993, four additional editions were published in 1995, 1998, 2003 and 2013. It is adopted in several countries around the world and has been translated into 6 languages. Successful breast cancer screening programs in high-income countries can be attributed in part to the widespread use of BI-RADS®. This success led to the development of similar classification systems for other organs (e.g., lung, liver, thyroid, ovaries, colon). In 1998, the structured report model was adopted in Brazil. This article highlights the pioneering and successful role of BI-RADS®, created by ACR 30 years ago, on the eve of publishing its sixth edition, which has evolved into a comprehensive quality assurance tool for multiple imaging modalities. And, especially, it contextualizes the importance of recognizing how we are using BI-RADS® in Brazil, from its implementation to the present day, with a focus on breast cancer screening., Competing Interests: Conflicts to interest: none to declare., (© 2024. Federação Brasileira de Ginecologia e Obstetrícia. All rights reserved.)

Objective: The aim of the study was to evaluate the interobserver agreement and diagnostic accuracy of COVID-19 Reporting and Data System (CO-RADS), in patients suspected COVID-19 pneumonia., Methods: Two hundred nine nonenhanced chest computed tomography images of patients with clinically suspected COVID-19 pneumonia were included. The images were evaluated by 2 groups of observers, consisting of 2 residents-radiologists, using CO-RADS. Reverse transcriptase-polymerase chain reaction (PCR) was used as a reference standard for diagnosis in this study. Sensitivity, specificity, area under receiver operating characteristic curve (AUC), and intraobserver/interobserver agreement were calculated., Results: COVID-19 Reporting and Data System was able to distinguish patients with positive PCR results from those with negative PCR results with AUC of 0.796 in the group of residents and AUC of 0.810 in the group of radiologists. There was moderate interobserver agreement between residents and radiologist with κ values of 0.54 and 0.57., Conclusions: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Patients have a right to their medical records, and it has become commonplace for institutions to set up online portals through which patients can access their electronic health information, including radiology reports. However, institutional approaches vary on how and when such access is provided. Many institutions have advocated built-in "embargo" periods, during which radiology reports are not immediately released to patients, to give ordering clinicians the opportunity to first receive, review, and discuss the radiology report with their patients. To understand current practices, a telephone survey was conducted of 83 hospitals identified in the 2019-2020 U.S. News & World Report Best Hospitals Rankings. Of 70 respondents, 91% (64 of 70) offered online portal access. Forty-two percent of those with online access (27 of 64 respondents) reported a delay of 4 days or longer, and 52% (33 of 64 respondents) indicated that they first send reports for review by the referring clinician before releasing to the patient. This demonstrates a lack of standardized practice in prompt patient access to health records, which may soon be mandated under the final rule of the 21st Century Cures Act. This article discusses considerations and potential benefits of early access for patients, radiologists, and primary care physicians in communicating health information and providing patient-centered care. © RSNA, 2021.

BACKGROUND. The Vesical Imaging Reporting and Data System (VI-RADS), based on multiparametric MRI (mpMRI), was developed to provide accurate information for the diagnosis of muscle-invasive bladder cancer (MIBC). OBJECTIVE. The purpose of our study was to evaluate the interobserver agreement and diagnostic performance of VI-RADS among readers with different levels of experience. METHODS. This retrospective study included 91 consecutive patients who underwent mpMRI before transurethral resection of bladder tumor (TURBT) from July 2010 through August 2018. After attending a training session, seven radiologists (five radiologists experienced in bladder MRI and two inexperienced radiologists) reviewed and scored all MRI examinations according to VI-RADS. The interobserver agreement was assessed by kappa statistics. ROC analysis was used to evaluate the diagnostic performance for MIBC. AUCs were estimated. RESULTS. Among 91 patients (72 men and 19 women; mean age ± SD, 73.2 ± 10.2 years), 48 (52.7%) had MIBC and 43 (47.3%) had non-muscle-invasive bladder cancer. Sixty-eight patients were treated with TURBT, and 23 were treated with radical cystectomy. Interobserver agreement was moderate to substantial (κ = 0.60-0.80) among the experienced readers, substantial (κ = 0.67) between the two inexperienced readers, and moderate to substantial (κ = 0.55-0.75) between the experienced and inexperienced readers. The pooled AUC was 0.88 (range, 0.82-0.91) for experienced readers and 0.84 (range, 0.83-0.85) for inexperienced readers, and 0.87 for all readers. Using a VI-RADS score of 4 or greater as the cutoff value for MIBC, the pooled sensitivity and specificity were 74.1% (range, 66.0-80.9%) and 94.1% (range, 88.6-97.7%) for experienced readers and 63.9% (range, 59.6-68.1%) and 86.4% (range, 84.1-88.6%) for inexperienced readers. Using a VI-RADS score of 3 or greater as the cutoff value, the pooled sensitivity and specificity were 83.4% (range, 80.9-85.1%) and 77.3% (range, 61.4-88.6%) for experienced readers and 82.0% (range, 80.9-83.0%) and 73.9% (range, 72.7-75.0%) for inexperienced readers. CONCLUSION. We observed moderate to substantial interobserver agreement and a pooled AUC of 0.87 among radiologists of different levels of expertise using VI-RADS. CLINICAL IMPACT. VI-RADS could help determine the depth and range of excision in TURBT, decreasing the risk of complications and enhancing the accuracy of pathologic diagnosis.

OBJECTIVE. This retrospective study aimed to investigate the capability of the already-proposed thyroid imaging reporting and data system for detecting diffuse thyroid disease (DTD-TIRADS) on ultrasound (US) by assessing interobserver agreement and diagnostic performance. MATERIALS AND METHODS. A total of 180 patients who underwent thyroid US before thyroid surgery were included. Three radiologists blinded to the pathologic and serologic data independently categorized the US features according to a four-category DTD-TIRADS classification system. On the basis of the pathologic results of thyroid parenchyma, diagnostic performance values were calculated using ROC curve analyses. Interobserver agreements of each US feature and DTD-TIRADS category among the three radiologists were also assessed. RESULTS. Of the 180 patients, 143 (79.4%) had normal thyroid parenchyma and 37 (20.6%) had diffuse thyroid disease (DTD). The areas under the ROC curve for DTD were not significantly different among the three radiologists: 0.876 (95% CI, 0.819-0.920) for radiologist 1, 0.883 (95% CI, 0.827-0.926) for radiologist 2, and 0.861 (95% CI, 0.801-0.908) for radiologist 3 ( p > .05). The cutoff for the diagnosis of DTD was category III DTD-TIRADS. The sensitivity, specificity, and accuracy of DTD-TIRADS for detecting DTD were 86.5%, 81.1%, and 82.2% for radiologist 1; 86.5%, 83.2%, and 83.9% for radiologist 2; and 83.8%, 82.5%, and 82.8% for radiologist 3, respectively. Interobserver agreement of DTD-TIRADS categorization was almost perfect (κ = 0.81). CONCLUSION. DTD-TIRADS has high diagnostic performance and almost-perfect interobserver agreement. Thus, DTD-TIRADS can be considered to be an effective classification system for diagnosing DTD.

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

Purpose: Many underserved remote locations without specialists would benefit from the ability to quickly and easily share images of radiographs with trained radiologists using WhatsApp messenger. However, there is limited evidence on the role of WhatsApp messenger for sharing chest x-ray (CXR) images to aid diagnosis and management. The objective of the study was to determine the diagnostic accuracy and inter-observer agreement of WhatsApp messenger images of digital CXR compared to viewing on Picture Archiving and Communication System (PACS) monitor., Methods: Two pulmonologists reported 400 WhatsApp messenger images of digital CXR each. After a wash period of two weeks, they reviewed the original CXR images on PACS and again reported their findings. Diagnostic agreement was measured using kappa value, diagnostic accuracy was evaluated by sensitivity and specificity., Results: The diagnostic agreement between WhatsApp and PACS images for both the readers was high in case of normal CXR (0.84), Pneumonia (0.85) and Active Koch's (0.79) and Old Koch's (0.71). The inter-observer agreement between two readers on WhatsApp images was good in cases of normal chest x-ray (0.74), Active Koch's (0.61) and Pneumonia (0.74) and low in COPD (0.31) and Pleural Effusion (0.28) and Carcinoma Lung (0.40). In terms of radiological lesion, inter-observer agreement between two readers on WhatsApp images was good in terms of the zonal involvement, moderate in case of infiltrates, consolidation, nodules, and fibrosis, fair in cavity, effusion (0.28) and poor in hilar lymphadenopathy (0.14). The sensitivity in the diagnosis of nodules, effusion and hilar lymphadenopathy was <50% in both the readers., Conclusion: CXR transmission via WhatsApp is able to identify clinical findings similar to viewing the same image on a PACS monitor in cases of Pneumonia and normal subjects. Active and old Koch's has good comparability whereas; diagnostic agreement is poor in COPD, cavity, pleural effusion and hilar lymphadenopathy, requiring more caution during interpretation., Competing Interests: Conflicts of interest All authors have none to declare., (Copyright © 2020 Tuberculosis Association of India. Published by Elsevier B.V. All rights reserved.)

Background: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated., Research Question: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis., Study Design and Methods: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality., Results: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90)., Interpretation: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities to RadLex terms. We manually construct a normalization corpus by annotating entities from three types of reports. This contains 1706 entity mentions. We propose two deep learning-based NLP methods based on a pre-trained language model (BERT) for automatic normalization. First, we employ BM25 to retrieve candidate concepts for the BERT-based models (re-ranker and span detector) to predict the normalized concept. The results are promising, with the best accuracy (78.44%) obtained by the span detector. Additionally, we discuss the challenges involved in corpus construction and propose new RadLex terms., (©2020 AMIA - All rights reserved.)

Hospitals often set protocols based on well defined standards to maintain the quality of patient reports. To ensure that the clinicians conform to the protocols, quality assurance of these reports is needed. Patient reports are currently written in free-text format, which complicates the task of quality assurance. In this paper, we present a machine learning based natural language processing system for automatic quality assurance of radiology reports on breast cancer. This is achieved in three steps: we i) identify the top-level structure (headings) of the report, ii) classify the report content into the top-level headings, and iii) convert the free-text detailed findings in the report to a semi-structured format (post-structuring). Top level structure and content of report were predicted with an F1 score of 0.97 and 0.94, respectively, using Support Vector Machine (SVM) classifiers. For automatic structuring, our proposed hierarchical Conditional Random Field (CRF) outperformed the baseline CRF with an F1 score of 0.78 versus 0.71. The determined structure of the report is represented in semi-structured XML format of the free-text report, which helps to easily visualize the conformance of the findings to the protocols. This format also allows easy extraction of specific information for other purposes such as search, evaluation, and research.

Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: To assess the accuracy and agreement of radiology information system (RIS) kerma-area product (KAP) data with respect to automatically populated dose management system (DMS) data for digital radiography (DR)., Methods: All adult radiographic examinations over 12 months were exported from the RIS and DMS at three centres. Examinations were matched by unique identifier fields, and grouped by examination type. Each centre's RIS sample completeness was calculated, as was the percentage of the RIS examination KAP values within 5% of their DMS counterparts (used as an accuracy metric). For each centre, the percentage agreement between the RIS and DMS examination median KAP values was computed using a Bland-Altman analysis. At two centres, up to 42.5% of the RIS KAP units entries were blank or invalid; corrections were attempted to improve data quality in these cases., Results: Statistically significant intersite variation was seen in RIS data accuracy and the agreement between the uncorrected RIS and DMS median KAP data, with a Bland-Altman bias of up to 11.1% (with a -31.7% to 53.9% 95% confidence interval) at one centre. Attempts to correct invalid KAP units increased accuracy but produced worse agreement at one centre, a slight improvement at another and no significant change in the third., Conclusion: The RIS data poorly represented the DMS data., Advances in Knowledge: RIS KAP data are a poor surrogate for DMS data in DR. RIS data should only be used in patient dose surveys with an understanding of its limitations and potential inaccuracies.

Purpose: To propose a new strategy to prevent communication errors caused by unread radiology reports., Materials and Methods: Medical emergencies were prefixed with triple stars on radiology reports, and the attending physician was contacted by telephone. Semi-emergencies (medical issues needing addressing within 2 weeks) were prefixed with double stars. Two weeks later, the duty radiologist would search the double-starred reports, and reviewed relevant patient charts to confirm that the information had been appropriately understood and acted upon. If not, the duty radiologist contacted the referral physician by telephone. One year after implementing this strategy, we retrospectively evaluated 1-year worth of data for all the reports of CT, MRI, nuclear medicine and ultrasonography (April 2018 to March 2019)., Results: Three hundred and twenty-one reports were double starred (0.52% of 62,143 reports, 1.32 reports/day), and transmission of relevant information was incomplete in 23 cases (7.17%). Causes of incomplete transmission were (1) reports not being opened (n = 17), (2) relevant information on reports being overlooked (n = 5), and (3) the wrong report being opened (n = 1). Sixty-five reports contained triple stars (0.10%, 0.27 reports/day)., Conclusion: The proposed strategy may be effective in preventing communication errors in radiology reports with important findings requiring semi-emergency clinical action.

Methods: The aim of the paper is twofold. First, we present Starviewer, a DICOM viewer developed in C++ with a core component built on top of open-source libraries. The viewer supports extensions that implement functionalities and front-ends for specific use cases. Second, we propose an adaptable evaluation framework based on a set of criteria weighted according to user needs. The framework can consider different user profiles and allow criteria to be decomposed in subcriteria and grouped in more general categories making a multi-level hierarchical structure that can be analysed at different levels of detail to make scores interpretation more comprehensible., Results: Different examples to illustrate Starviewer functionalities and its extensions are presented. In addition, the proposed evaluation framework is used to compare Starviewer with four open-source viewers regarding their functionalities for daily clinical practice. In a range from 0 to 10, the final scores are: Horos (7.7), Starviewer (6.2), Weasis (6.0), Ginkgo CADx (4.1), and medInria (3.8)., Conclusions: Starviewer provides basic and advanced features for daily image diagnosis needs as well as a modular design that enables the development of custom extensions. The evaluation framework is useful to understand and prioritize new development goals, and can be easily adapted to express different needs by altering the weights. Moreover, it can be used as a complement to maturity models., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Structured reporting of ultrasound examinations can add value throughout the imaging chain. Reports may be created in a more efficient manner, with increased accuracy and clarity. Communication with referring providers and patients may be improved. Patient care can be enhanced through improved adherence with guidelines and local best practices. Radiology departments may benefit from improved billing and quality reporting. Consistent discrete data can enable research and collaborations between institutions. This article will review the multifaceted impact of structuring ultrasound reports.

Objectives: To assess the prognostic implications of standardized reporting systems for coronary computed tomography angiography (CCTA) and coronary artery calcium scores (CACS) in patients with stable chest pain., Background: The Coronary Artery Disease Reporting And Data System (CAD-RADS) and Coronary Artery Calcium - Data and Reporting System (CAC-DRS) aim to improve communication of CACS and CCTA results, but its influence on prognostication is unknown., Methods: Images from 1769 patients who underwent CCTA as part of the Scottish Computed Tomography of the HEART (SCOT-HEART) multi-center randomized controlled trial were assessed. CACS were classified as CAC-DRS 0 to 3 based on Agatston scores. CCTA were classified as CAD-RADS 0 to 5 based on the most clinically relevant finding per patient. The primary outcome was the five-year events of fatal and non-fatal myocardial infarction., Results: Patients had a mean age of 58 ± 10 years and 56% were male. CAC-DRS 0, 1, 2 and 3 occurred in 642 (36%), 510 (29%), 239 (14%) and 379 (21%) patients respectively. CAD-RADS 0, 1, 2, 3, 4A, 4B and 5 occurred in 622 (35%), 327 (18%), 211 (12%), 165 (9%), 221 (12%), 42 (2%) and 181 (10%) patients respectively. Patients classified as CAC-DRS 3 were at an increased risk of fatal or non-fatal myocardial infarction compared to CAC-DRS 0 patients (hazard ratio (HR) 9.41; 95% confidence interval (CI) 3.24, 27.31; p < 0.001). Patients with higher CAD-RADS categories were at an increased risk of fatal or non-fatal myocardial infarction, with patients classified as CAD-RADS 4B at the highest risk compared to CAD-RADS 0 patients (HR 19.14; 95% CI 4.28, 85.53; p < 0.001)., Conclusion: Patients with higher CAC-DRS and CAD-RADS scores were at increased risk of subsequent fatal and non-fatal myocardial infarction. This confirms that the classification provides additional prognostic discrimination for future coronary heart disease events., Competing Interests: Conflicts of interest MCW has performed consultancy for GE Healthcare. RC has a research grant from GE Healthcare., (Copyright © 2020 [The Author/The Authors]. Published by Elsevier Inc. All rights reserved.)

Objective: We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption., Methods: Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography., Results: CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%., Discussion: Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved., (Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

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.

In this work, we evaluated the change of primary monitor characteristics in two consecutive years. Sixty-six primary monitors were included in the analysis. The monitors were located at radiology physicians' offices and radiology reading rooms. All primary monitors were equipped with the manufacturer's built-in photometers and connected to the BarcoMediCalQA web service for manual and automatic quality control measurements. External photometer/illuminance meter (RaySafe Solo Light) was used to measure the luminance values. Measured luminance values of the TG18LN1-18 and TG18UNL80 test patterns were used to evaluate the primary monitors performance. In a comparison of the quality assurance (QA) measurement results for the same monitors that were performed within 2 years, the luminance of 25 displays remained statistically the same (P > 0.01). The luminance of 17 displays decreased (P < 0.01) in 2017 when compared with 2016, the luminance of 24 displays increased (P < 0.01) in 2017 when compared with 2016. For the annual measurements of the MLD in 2016 and 2017, 25 out of 66 displays showed a decrease of MLD values in 2017 compared with the same measurements in 2016 and 41 displays showed an increase of MLD in 2017. All tested primary displays had the MLD value less than 17.2%. The mean value of illuminance measured in 2016 was 5.8 lux ± 3.1 lux. In 2017, the mean value of illuminance measured was 8.7 lux ± 5.3 lux. Although it is expected that monitors luminance values will decrease over time, we found displays with increased luminance. This is possibly due to the multiple monitor calibrations that were performed between two annual monitor QA tests. Based on the findings of this work, more efficient display QA programs with a shorter time interval than 1 year are needed., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

OBJECTIVE. Variability in reports of the same imaging study performed at the same institution may be confusing to referrers and patients, hindering results communication. The purpose of this study was to introduce a quality improvement initiative aimed at reducing variation in radiology report templates at a large academic center. MATERIALS AND METHODS. The quality improvement initiative was undertaken at a large multisite, multidivision academic radiology practice performing more than 820,000 radiologic examinations annually. A project charter defined the goals, scope, and personnel of the initiative and an escalation pathway for conflict resolution. Standard requirements for templates were initially developed. From September 2017 to May 2018, an oversight committee (24 organ system and modality-based work groups with representatives from nine subspecialty divisions) reviewed and harmonized all institutional radiology report templates across the enterprise irrespective of the imaging site or division that interpreted the study. The primary outcome measure was percentage reduction in report templates after harmonization. The secondary outcome measure was monthly adherence to harmonized templates for 9 months after implementation assessed by manual review of 40 randomly selected reports per month. The paired t test was used to assess template reduction, and the chi-square trend test was used to study trend in adherence to harmonized templates. RESULTS. Among 19,687 total templates at baseline, 597 harmonized templates remained after harmonization ( p < 0.001). There was variation in template reduction by work group (multiple p < 0.05; reduction range, 79.2-99.3%). Radiologist adherence to harmonized templates ranged from 88.0% to 100%, unchanged in the 9 months after implementation ( p = 0.23, chi-square trend). CONCLUSION. A radiology report harmonization initiative reduced 97.0% of report templates with a sustained high degree of adherence to harmonized templates after implementation at a large multisite multidivision academic radiology practice.

Objective: To evaluate diagnostic performance and inter-reviewer agreement (IRA) of the Gynecologic Imaging Reporting and Data System (GI-RADS) for diagnosis of adnexal masses (AMs) by pelvic ultrasound (US)., Patients and Methods: A prospective multicenter study included 308 women (mean age, 41 ± 12.5 years; range, 15-73 years) with 325 AMs detected by US. All US examinations were analyzed, and AMs were categorized into five categories according to the GI-RADS classification. We used histopathology and US follow-up as the reference standards for calculating diagnostic performance of GI-RADS for detecting malignant AMs. The Fleiss kappa (κ) tests were applied to evaluate the IRA of GI-RADS scoring results for predicting malignant AMs., Results: A total of 325 AMs were evaluated: 127 (39.1%) were malignant and 198 (60.9%) were benign. Of 95 AMs categorized as GI-RADS 2 (GR2), none was malignant; of 94 AMs categorized as GR3, three were malignant; of 13 AMs categorized as GR4, six were malignant; and of 123 AMs categorized as GR5, 118 were malignant. On a lesion-based analysis, the GI-RADS had a sensitivity, a specificity, and an accuracy of 92.9%, 97.5%, and 95.7%, respectively, when regarding only those AMs classified as GR5 for predicting malignancy. Considering combined GR4 and GR5 as a predictor for malignancy, the sensitivity, specificity, and accuracy of GI-RADS were 97.6%, 93.9%, and 95.4%, respectively. The IRA of the GI-RADS category was very good (κ = 0.896). The best cutoff value for predicting malignant AMs was >GR3., Conclusions: The GI-RADS is very valuable for improving US structural reports., Key Points: • There is still a lack of a standard in the assessment of AMs. • GI-RADS is very valuable for improving US structural reports of AMs. • GI-RADS criteria are easy and work at least as well as IOTA.

OBJECTIVE. The purpose of this quality improvement project was to create sustained improvement in the frequency of more complete imaging histories provided with imaging examinations submitted directly by ordering providers. A secondary purpose was increasing the number of characters submitted by ordering providers with imaging examinations. MATERIALS AND METHODS. A multidisciplinary team defined the components of a complete imaging history, a process that underwent several improvement cycles. Audits were regularly performed using consensus, and the project team regularly evaluated the completeness of the imaging histories. The final components of the definition of a complete imaging history included responses regarding what happened, when it happened, where the patient was experiencing pain, and the ordering provider's concern. Prompts were inserted into the electronic physician order entry process, and performance was monitored for an additional 18 months. RESULTS. A total of 10,236 orders were placed by providers in the study clinic from March 13, 2017, to December 16, 2018. Of the orders audited in the baseline period, 16.0% (64/397) of orders audited in the baseline period contained all four history components, which increased to 52.0% (2200/4234; absolute increase of 36.0%, relative increase of 225.0%; p < 0.0001) in the subsequent time periods, and improvement was sustained. The mean number of characters providers entered in the imaging histories they submitted increased from 45.4 characters per order during the baseline period to 75.4 (66.1% increase, p < 0.0001) after the intervention. CONCLUSION. By collaborating with a multidisciplinary team, we created a standardized definition of an imaging history, engineered our systems to include supportive prompts in the order entry interface, and sustainably improved the quality of imaging histories provided.

Radiologists often encounter imaging requisitions that lack important information needed for accurate diagnostic studies. Reason for exam Imaging Reporting and Data System (RI-RADS) is proposed as a grading system for evaluation of the quality of clinically pertinent information provided in imaging requisitions. Three categories of information are suggested as key indicators of quality: impression, clinical findings, and the diagnostic question. This scheme is intended to improve the quality of imaging requisitions and overall patient care., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Medical education in China is in a transitional period, from passive learning models to experiential education. We modified an experiential education method for radiology education. The aim of this study is to evaluate the effect of this method on undergraduate radiology education., Method: With the help of the picture archiving and communication system (PACS) and RadiAnt DICOM Viewer, we modified an experiential education method that simulates similar working conditions for undergraduate medical students to formulate radiology diagnosis similar to clinical radiologists. A total of 101 students were allocated into either the experiential education group or the control group. The final examination scores and a 5-point Likert scale self-assessment questionnaire of radiologic skills were collected from all the students as an objective assessment and a subjective assessment respectively. A questionnaire was also used to assess the satisfaction with the experiential model in the experiential education group. Mann-Whitney U test was used to compare the ranked data, and t-tests were used to compare the numeric data., Results: The experiential education group demonstrated significantly higher scores (7.4 ± 1.3) compared to the control group (6.7 ± 1.5, p < 0.05) in the question type "description and diagnosis". The self-assessment questionnaire indicated that the experiential education was related to increased familiarity with the diagnosis thinking principle and the sequences and reconstruction methods of computer tomography (CT) imaging, which also strengthen participants' self-confidence to perform future clinical work (p < 0.05). The self-assessment questionnaire in the experiential education group showed that the majority of students were satisfied with the organization (82.5%), interactivity (85%) and quality (85%) of the learning activity. Most students found this model of learning to be helpful for studying radiology (85%) and for understanding anatomy (90%)., Conclusion: Compared with the traditional radiology education approach, the experiential education method showed greater efficacy in improving students' analysis and diagnostic skills and their self-confidence.

Contrast material-enhanced (CE) US is a recognized imaging tool in the characterization of focal liver lesions and uses microbubble contrast agents to increase signal backscattering from the blood. The European Federation of Societies for Ultrasound in Medicine and Biology and the World Federation for Ultrasound in Medicine and Biology strongly recommend the use of CE US in the characterization of hepatocellular nodules in individuals with liver cirrhosis. CE US was recently approved by the Food and Drug Administration for liver indications in adult and pediatric patients. CE US Liver Imaging Reporting and Data System (LI-RADS) criteria were recently proposed by the American College of Radiology and include eight distinct diagnostic categories: LR-1 (definitely benign), LR-2 (probably benign), LR-3 (intermediate malignancy probability), LR-4 (probably hepatocellular carcinoma [HCC]), LR-5 (definitely HCC), LR-NC (cannot be categorized due to image degradation), LR-TIV (tumor in vein), and LR-M (probably or definitely malignant but not HCC specific). CE US LI-RADS criteria can be used to produce a structured report for HCC diagnosis. However, the variability of US equipment in terms of sensitivity to microbubble signal, interreader variability, large number of HCC nodules classified as LR-3, and wide washout temporal range for LR-M observations are limitations., (© RSNA, 2019.)

Value-based imaging requires appropriate utilization and the delivery of consistently high-quality imaging at an acceptable cost. Challenges include developing standardized imaging protocols, ensuring consistent application by technologists, and monitoring quality. These challenges increase as enterprises grow in geographical extent and complexity through mergers or partnerships. Our imaging enterprise includes a university hospital and clinic system, a large county hospital and healthcare system, and a pediatric hospital and health system. Studies across the three systems are interpreted by one large academic radiology group with expertise in various subspecialties. Our goals were as follows: (1) Standardize imaging protocols; (2) adapt the imaging protocols to specific modalities and available equipment; and (3) disseminate this knowledge across all of the sites of care. Our approach involved three components: (1) facilitation of imaging protocol definition across subspecialty radiologist teams; (2) creation of a database which links the clinical imaging protocols to the scanner/machine specific acquisition protocols; and (3) delivery of a protocol library and updates to all users regardless of location. We successfully instituted a process for the development, implementation, and delivery of standardized imaging protocols in a complex, multi-institutional healthcare system. Key elements for success include (1) a Project Champion who is able to articulate the importance of protocol standardization in improving the quality of patient care, (2) strong, effective modality-specific operational committees, (3) a Project Lead to manage the process efficiently, and (4) an electronic publishing of the protocol database to facilitate ease of access and use.

Background: The Liver Imaging Reporting and Data System (LI-RADS) is widely adopted for noninvasive diagnosis of hepatocellular carcinoma (HCC). It's updated to version 2018 recently, with some major changes compared with v2017. However, the diagnostic performance of LI-RADS v2018 and its difference with v2017 are yet to be validated., Purpose: To compare the diagnostic performances of LI-RADS on MR for diagnosing HCC between v2017 and v2018., Study Type: Retrospective., Subjects: In all, 181 patients with 217 hepatic observations (146 HCCs, 16 non-HCC malignancies and 55 benign lesions) with liver MRI and pathological or follow-up imaging diagnoses., Field Strength/sequence: 1.5 T or 3 T MRI. Dual-echo T 1 WI, T 2 WI, diffusion-weighted imaging, and a liver acquisition with volume acceleration. Assessment Images were independently interpreted by three radiologists, and then in consensus for observations with different LR categories, according to LI-RADS v2017 and v2018, separately., Statistical Tests: Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and Youden index., Results: When adopting LR-5 as a predictor of HCC, the sensitivity (80.8% vs. 71.2%), NPV (69.6% vs. 60.7%), and accuracy (83.9% vs. 77.9%) were all increased for LI-RADS v2018 compared with v2017, with a greater Youden index (0.709 vs. 0.627). However, the diagnostic performances of MRI for diagnosing HCC were not changed while adopting LR-4/5 as a predictor. The threshold growths of 76% (19/25) observations in v2017 were revised to subthreshold growth in v2018, and 16 LR-4 observations in v2017 were changed to LR-5 based on v2018., Data Conclusion: The diagnostic performance of LI-RADS v2018 for diagnosing HCC is superior to v2017, with a greater sensitivity, NPV, and accuracy. The revisions in v2018 mainly affect the categorization when adopting LR-5 as a predictor of HCC., Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:746-755., (© 2019 International Society for Magnetic Resonance in Medicine.)

The major goal of the radiology report is to deliver timely, accurate, and actionable information to the patient care team and the patient. Structured reporting offers multiple advantages over traditional free-text reporting, including reduction in diagnostic error, comprehensiveness, adherence to national consensus guidelines, revenue capture, data collection, and research. Various technological innovations enhance integration of structured reporting into everyday clinical practice. This review discusses the benefits of innovations in radiology reporting to the clinical decision process, the patient experience, the cost of imaging, and the overall contributions to the health of the population. Future directions, including the use of artificial intelligence, are reviewed., (Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background The 2017 version of the Liver Imaging Reporting and Data System (LI-RADS) recently included standardized interpretation, reporting, and management guidelines for US (US LI-RADS); however, this system has not yet been validated. Purpose To evaluate the diagnostic performance of US LI-RADS version 2017 for detecting hepatocellular carcinoma (HCC) in participants at high risk and to determine the clinical factors associated with a poor visualization score. Materials and Methods This study included 407 prospectively recruited participants (mean age, 56 years; age range, 28-76 years) with cirrhosis at high risk for HCC who underwent US surveillance from November 2011 to August 2012. Two radiologists retrospectively analyzed US images, assigning a LI-RADS category (US-1 = negative, US-2 = subthreshold, US-3 = positive) and a visualization score (A = no or minimal limitations, B = moderate limitations, C = severe limitations). The sensitivity and specificity for diagnosing HCC were calculated on a per-patient and per-lesion basis, using pathologic results and typical CT or MRI as reference standards. The risk factors for a poor visualization score were determined by using univariable and multivariable analyses. Results Of 429 lesions in 407 participants, there were 32 HCCs in 28 participants. In the per-lesion analysis, the specificity for US-3 was 366 of 397 (92%; 95% confidence interval [CI]: 89%, 95%) and the sensitivity was 11 of 32 (34%; 95% CI: 20%, 52%). In the per-patient analysis, the specificity for US-3 was 352 of 379 (93%; 95% CI: 90%, 95%) and the sensitivity was 11 of 28 (39%; 95% CI: 24%, 58%). Visualization score C (114 of 407 [28%] participants) had the highest false-negative rate (six of seven [86%] participants). High body weight (adjusted odds ratio [OR], 2.1 [95% CI: 1.2, 3.6]; P = .01), Child-Pugh class B disease (OR, 2.9 [95% CI: 1.7, 4.9]; P < .001), and moderate to severe fatty liver (OR, 1.7 [95% CI: 1.0, 2.8]; P = .047) were associated with a poor visualization score of C. Conclusion The US-3 category demonstrated high specificity but low sensitivity for diagnosing hepatocellular carcinoma. The visualization score C had a higher false-negative rate than scores A or B, and patients with high body weight, Child-Pugh class B disease, and moderate to severe fatty liver may present limitations for US surveillance. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

An enterprise imaging (EI) strategy is an organized plan to optimize the electronic health record (EHR) so that healthcare providers have intuitive and immediate access to all patient clinical images and their associated documentation, regardless of source. We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patient experience. Step seven suggests how EI can enhance the work life of caregivers and step eight describes how to develop EI governance. Step nine describes the plan to implement an EI project, and finally, step 10, to understand cybersecurity from a patient safety perspective and to protect images from accidental and malicious intrusion.

Determining the clinical impact of imaging exams at the enterprise level is problematic, as radiology reports historically have been created with the content meant primarily for the referring provider. Structured reporting can establish the foundation for enterprise monitoring of imaging outcomes without manual review providing the framework for assessment of utilization and quality. Ultrasound (US) for deep vein thrombosis evaluation (DVT) is an ideal testbed for assessing this functionality. The system standard template for Doppler US for extremity venous evaluation for DVT was updated with a discrete fixed picklist of impression options and implemented system wide. Template utilization and interpretive outcomes were actively monitored and use reinforced as part of standard clinical practice. From January 1, 2017 to December 31, 2017, 9111 US exams for DVT were performed with 8997 utilizing structured reporting (98.75%). Of those in the structured reporting group, 1074 (11.79%) were positive for any type of DVT with 732 (8.03%) reported as Acute/New above the knee. Positive rates for any type of DVT were 10.29% emergency department, 14.17% inpatient, and 13.20% outpatient. While being the lowest positive rate, the emergency department had the highest overall volume of exams. Structured reporting for DVT US assessment outcomes can be implemented with a very high rate of radiologist adoption and adherence providing accurate determination of positive rates, month by month, in differing patient locations. Structured elements can be used to automatically trigger downstream processes; in our institution, this will alert providers in the EHR if the patient does not receive anticoagulation within 2 h of a positive test. This lays the foundation for effective enterprise assessment of imaging outcomes forming the basis of future quality and safety initiatives on optimizing health system resource utilization.

Reliable transmission of ultrasound measurements into radiology reports is fraught with potential sources of error. In a conventional workflow, measurements are either written by hand on worksheets and/or dictated from worksheets or the images themselves into the radiology report. Valuable physician time is spent dictating, checking, and editing these values and this process is error-prone. Our approach was to use a transfer-software application to auto-populate measurements, with a goal of achieving >90% utilization rate by both technologists and radiologists. Implementation involved creating measurement fields for each measurement on each ultrasound unit of our multisite academic department. These fields were then mapped in both the transfer-software and the dictation software, to set up a 1:1:1 correspondence for each field. As a result, each measurement acquired by the technologist would automatically populate the radiology report within the dictation software. We created and mapped 128 fields for 39 exam templates. After implementation, technologist utilization rate was 86%-96% and overall radiologist utilization rate was 92%-93%. Radiology resident utilization rate was highest, at 95%-96%. We provide a guide for implementation and lessons learned., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Radiology reports are the principal form of communication with the referring provider. Unfortunately, they can be a form of communication riddled with errors and inscrutable statements burying the intended meaning, failing to achieve the main task for which it was made: communicating key imaging findings as they pertain to the clinical question being posed. Structured reporting is a multifaceted and modular solution to problematic reports, with variable iterations and benefits. Structured reports have been adapted across departments and even national societies, with standardized format, content and language. Newer developments include contextual reporting and common data elements. Herein, we discuss the various forms and levels of structured reporting and the latest advancements, as well as the general acceptance within radiology. We also discuss some areas for improvement as the practice of structured reporting matures.

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.