The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.

Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertinent information for prioritizing the critical cases. We developed an automated NLP pipeline using a transformer-based ClinicalBERT++ model which was fine-tuned on 3 M radiology reports and compared against the traditional BERT model. We validated the models on both internal hold-out ED cases from EUH as well as external cases from Mayo Clinic. We also evaluated the model by combining different sections of the radiology reports. On the internal test set of 3819 reports, the ClinicalBERT++ model achieved 0.96 f1-score while the BERT also achieved the same performance using the reason for exam and impression sections. However, ClinicalBERT++ outperformed BERT on the external test dataset of 2039 reports and achieved the highest performance for classifying critical finding reports (0.81 precision and 0.54 recall). The ClinicalBERT++ model has been successfully applied to large-scale radiology reports from 5 different sites. Automated NLP system that can analyze free-text radiology reports, along with the reason for the exam, to identify critical radiology findings and recommendations could enable automated alert notifications to clinicians about the need for clinical follow-up. The clinical significance of our proposed model is that it could be used as an additional layer of safeguard to clinical practice and reduce the chance of important findings reported in a radiology report is not overlooked by clinicians as well as provide a way to retrospectively track large hospital databases for evaluating the documentation of the critical findings.

Intellectual property: A primer for radiologists.

Typically the creative product of the mind, intellectual property often forms the basis of a new product, service line, or company. Intellectual property law is complicated and nuanced, and poorly understood by many physicians, innovators, and entrepreneurs. Successfully navigating the process of intellectual property protection is critical in facilitating the translation of innovation into clinical practice. We define intellectual property and common terms used in intellectual property law and offer justification for the importance of intellectual property protections. We additionally highlight resources to assist radiologists with intellectual property protection and outline basic guidelines to successfully initiate discussions around intellectual property with third party vendors and consultants. SUMMARY: Proactive intellectual property protection is critically important for radiologist innovators seeking to bring new ideas to the marketplace.

COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development.

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

Balancing the Scales: An Analysis of Social Determinants of Health, Radiology Report Acuity, and Radiology Staffing Models in an Academic Health System.

PURPOSE: Social determinants of health, including race and insurance status, contribute to patient outcomes. In academic health systems, care is provided by a mix of trainees and faculty members. The optimal staffing ratio of trainees to faculty members (T/F) in radiology is unknown but may be related to the complexity of patients requiring care. Hospital characteristics, patient demographics, and radiology report findings may serve as markers of risk for poor outcomes because of patient complexity. METHODS: Descriptive characteristics of each hospital in an urban five-hospital academic health system, including payer distribution and race, were collected. Radiology department T/F ratios were calculated. A natural language processing model was used to classify multimodal report findings into nonacute, acute, and critical, with report acuity calculated as the fraction of acute and critical findings. Patient race, payer type, T/F ratio, and report acuity score for hospital 1, a safety net hospital, were compared with these factors for hospitals 2 to 5. RESULTS: The fraction of patients at hospital 1 who are Black (79%) and have Medicaid insurance (28%) is significantly higher than at hospitals 2 to 5 (P < .0001), with the exception of hospital 3 (80.1% black). The T/F ratio of 1.37 at hospital 1 as well as its report acuity (28.9%) were significantly higher (P < .0001 for both). CONCLUSIONS: T/F ratio and report acuity are highest at hospital 1, which serves the most at-risk patient population. This suggests a potential overreliance on trainees at a site whose patients may require the greatest expertise to optimize care.

Shared Decision Making: Radiology's Role and Opportunities.

OBJECTIVE. The purpose of this article is to present a targeted literature review describing the current state of radiology initiatives in support of shared decision making and gaps that offer opportunities for innovation and improvement. CONCLUSION. Breaking down the shared decision-making process into its four major components (access to information, comprehension of the information, appraisal of the information, application of knowledge in care decisions) reveals the role of radiologists in the decision-making process and opportunities for expanding this role.

The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.

Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths that can potentially benefit radiology. Specifically, adversarial networks have the potential to decrease radiation exposure to patients through minimizing repeat imaging due to artifact, decreasing acquisition time, and generating higher quality images from low-dose or no-dose studies. The authors provide an overview of a specific type of adversarial network called a "generalized adversarial network" and review its uses in current medical imaging research.

Patient Knowledge Regarding Colorectal Cancer Risk, Opinion of Screening, and Preferences for a Screening Test.

AIMS: To assess patient knowledge about colorectal cancer incidence and prognosis as well as willingness to undergo screening with various tests (eg, optical colonoscopy, stool-based tests, computed tomographic colonography (CTC)). MATERIALS AND METHODS: A survey was administered to consecutive patients of a general academic-based internal medicine clinic. RESULTS: Survey response rate was 86.3%. A majority of respondents (55%) reported being aware of general information about colorectal cancer, and 99% indicated a belief that colorectal cancer screening was a good idea. A majority of respondents (73%) were willing to undergo optical colonoscopy, and some were willing to undergo stool-based tests (48%), or CT colonography CTC (40%). A majority reported being more willing to undergo a colorectal cancer screening test if the test did not involve radiation (86%), did not involve insertion of a tube or device into the rectum (78%), did not involve a pre-proceduralpreprocedural bowel cleansing regimen (73%), or did not involve sedation (60%). CONCLUSION: Improved patient education about the negligible radiation risk associated with CTC or development of a non-invasive imaging test that did not involve a preprocedural bowel cleansing regimen may increase rates of colorectal cancer screening.

A Survey of Imaging Informatics Fellowships and Their Curricula: Current State Assessment.

In a 2016 survey of imaging informatics ("II") fellowship graduates, the surveyed fellowship graduates expressed the "opinion that II fellowships needed further formalization and standardization" Liao et al. (J Digit Imaging, 2016). This, coupled with the fact that the original published "standardized" curriculum is about 15 years out of date in our rapidly changing systems, suggests an opportunity for curriculum improvement. Before agreeing on improved structural and content suggestions for fellowships, we completed a current-state assessment of how each fellowship organizes its education and what requirements each have for fellowship completion. In this work, we aimed to collect existing information about imaging informatics fellowship curricula by contacting institutions across the country. A survey was completed by phone with the fellowship directors of existing imaging informatics fellowships across the country. Additionally, we collected existing documentation that outlines the curricula currently in use at institutions. We reviewed both the interview responses and documentation to assess overlapping trends and institutional differences in curriculum structure and content. All fellowships had suggested reading lists, didactic lectures, and a required project for each fellow. There were required practicum activities or teaching experience each in two fellowships, and one fellowship had a mandatory certification requirement for graduation. Curriculum topics in Technical Informatics or Business and Management were covered by a majority of institutions, while Quality and Safety and Research topics had inconsistent coverage across fellowships. Our plan is to reengage II fellowship directors to develop a core curriculum, which is part of the Society of Imaging Informatics in Medicine strategic plan.

Gender Reporting in Radiology Human Subjects Research.

PURPOSE: The aim of this report was to study the presence and extent of gender bias and reporting in radiology human subjects research. METHODS: For this bibliometric analysis, the authors reviewed all articles published between January 1, 2016, and June 30, 2016, in seven of the most cited general radiology journals. From each original research article studying human subjects, the number and gender of participants and whether gender-based results were reported were manually extracted. Articles evaluating gender-specific body parts were excluded. Article-level subject gender matching percentages were calculated and descriptive statistics reported. RESULTS: Of all 1,065 target journal articles during the study window, 522 met the human subjects research inclusion criteria. Of these, 48 (9.2%) made no mention at all of research subjects' gender. Of the 473 articles mentioning gender, 147 (31.1%) had more female and 308 (65.1%) more male subjects. But in aggregate, 105,763 of 254,102 (41.6%) of all subjects were male and 142,069 (55.9%) were female. By quartile distribution, subject gender matching was very variable (12.9% of articles with <25% match, 23.7% with 25%-50%, 29.4% with 50%-75%, and 34.0% with ≥75%). Of articles including subjects of both genders, however, only 27.5% (126 of 458) reported any gender-based results. CONCLUSIONS: In human subjects research published in the most cited general radiology journals, the gender of human subjects is a poorly controlled, and frequently neglected, variable. In an emerging era of personalized medicine, initiatives to ensure transparent reporting of gender-specific results may help catalyze otherwise overlooked discoveries to advance the health of all.

Imaging of Children With Nontraumatic Headaches.

OBJECTIVE: Headache in children is a common symptom and often is worrisome for clinicians and parents because of the breadth of possible underlying significant abnormalities, including meningitis, brain neoplasms, and intracranial hemorrhage. For this reason, many children with headaches undergo neuroimaging. Most neuroimaging studies performed of children with headaches have normal findings but may lead to significant downstream effects, including unnecessary exposure to ionizing radiation or sedation, as well as unnecessary cost to the health care system. In this article, we review the current evidence and discuss the role of neuroimaging in the diagnosis and management of pediatric headaches, with a special focus on tools that may aid in increasing the rate of positive findings, such as classification systems, algorithms, and red flag criteria. CONCLUSION: Many tools exist that can help in improving the appropriateness of neuroimaging in pediatric headache. The main issues that remain to be addressed include scientific proof of safety and validity of these tools and clarity regarding the risks, benefits, and cost-effectiveness of CT versus MRI in various clinical settings and scenarios.