The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.

PURPOSE OF REVIEW: The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. RECENT FINDINGS: Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.

An Image Quality-informed Framework for CT Characterization.

Background Lack of standardization in CT protocol choice contributes to radiation dose variation. Purpose To create a framework to assess radiation doses within broad CT categories defined according to body region and clinical imaging indication and to cluster indications according to the dose required for sufficient image quality. Materials and Methods This was a retrospective study using Digital Imaging and Communications in Medicine metadata. CT examinations in adults from January 1, 2016 to December 31, 2019 from the University of California San Francisco International CT Dose Registry were grouped into 19 categories according to body region and required radiation dose levels. Five body regions had a single dose range (ie, extremities, neck, thoracolumbar spine, combined chest and abdomen, and combined thoracolumbar spine). Five additional regions were subdivided according to dose. Head, chest, cardiac, and abdomen each had low, routine, and high dose categories; combined head and neck had routine and high dose categories. For each category, the median and 75th percentile (ie, diagnostic reference level [DRL]) were determined for dose-length product, and the variation in dose within categories versus across categories was calculated and compared using an analysis of variance. Relative median and DRL (95% CI) doses comparing high dose versus low dose categories were calculated. Results Among 4.5 million examinations, the median and DRL doses varied approximately 10 times between categories compared with between indications within categories. For head, chest, abdomen, and cardiac (3 266 546 examinations [72%]), the relative median doses were higher in examinations assigned to the high dose categories than in examinations assigned to the low dose categories, suggesting the assignment of indications to the broad categories is valid (head, 3.4-fold higher [95% CI: 3.4, 3.5]; chest, 9.6 [95% CI: 9.3, 10.0]; abdomen, 2.4 [95% CI: 2.4, 2.5]; and cardiac, 18.1 [95% CI: 17.7, 18.6]). Results were similar for DRL doses (all P < .001). Conclusion Broad categories based on image quality requirements are a suitable framework for simplifying radiation dose assessment, according to expected variation between and within categories. © RSNA, 2021 See also the editorial by Mahesh in this issue.

Multi-institutional Experience with Patient Image Access Through Electronic Health Record Patient Portals.

The objective is to determine patients' utilization rate of radiology image viewing through an online patient portal and to understand its impact on radiologists. IRB approval was waived. In this two-part, multi-institutional study, patients' image viewing rate was retrospectively assessed, and radiologists were anonymously surveyed for the impact of patient imaging access on their workflow. Patient access to web-based image viewing via electronic patient portals was enabled at 3 institutions (all had open radiology reports) within the past 5 years. The number of exams viewed online was compared against the total number of viewable imaging studies. An anonymized survey was distributed to radiologists at the 3 institutions, and responses were collected over 2 months. Patients viewed 14.2% of available exams - monthly open rate varied from 7.3 to 41.0%. A total of 254 radiologists responded to the survey (response rate 32.8%); 204 were aware that patients could view images. The majority (155/204; 76.0%) felt no impact on their role as radiologists; 11.8% felt negative and 9.3% positive. The majority (63.8%) were never approached by patients. Of the 86 who were contacted, 46.5% were contacted once or twice, 46.5% 3-4 times a year, and 4.7% 3-4 times a month. Free text comments included support for healthcare transparency (71), concern for patient confusion and anxiety (45), and need for attention to radiology reports and image annotations (15). A small proportion of patients viewed their radiology images. Overall, patients' image viewing had minimal impact on radiologists. Radiologists were seldom contacted by patients. While many radiologists feel supportive, some are concerned about causing patient confusion and suggest minor workflow modifications.

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., "flattening the curve") have included shelter-in-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services. METHODS: We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes. RESULTS: ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001). CONCLUSION: Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases.

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.

Social Media Tools for Department and Practice Communication and Branding in the Digital Age.

With nearly 70% of adults in the United States using at least one social media platform, a social media presence is increasingly important for departments and practices. Patients, prospective faculty and trainees, and referring physicians look to social media to find information about our organizations. The authors present a stepwise process for planning, executing, and evaluating an organizational social media strategy. This process begins with alignment with a strategic plan to set goals, identification of the target audience(s), selection of appropriate social media channels, tracking effectiveness, and resource allocation. The article concludes with a discussion of advantages and disadvantages of social media through a review of current literature. ©RSNA, 2018.

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

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

Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow.

Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow.

Streamlining emergent hand and wrist radiography with a modified four-view protocol.

This study aims to determine whether a modified four-view hand and wrist study performs comparably to the traditional seven views in the evaluation of acute hand and wrist fractures. This retrospective study was approved by the institutional review board with waiver of informed consent. Two hundred forty patients (50 % male; ages 18-92 years) with unilateral three-view hand (posteroanterior, oblique, and lateral) and four-view wrist (posteroanterior, oblique, lateral, and ulnar deviation) radiographs obtained concurrently following trauma were included in this study. Four emergency radiologists interpreted the original seven images, with two radiologists independently evaluating each study. The patients' radiographs were then recombined into four-view series using the three hand images and the ulnar deviated wrist image. These were interpreted by the same radiologists following an 8-week delay. Kappa statistics were generated to measure inter-observer and inter-method agreement. Generalized linear mixed model analysis was performed between the seven- and four-view methods. Of the 480 reports generated in each of the seven- and four-view image sets, 142 (29.6 %) of the seven-view and 126 (26.2 %) of the four-view reports conveyed certain or suspected acute osseous findings. Average inter-observer kappa coefficients were 0.7845 and 0.8261 for the seven- and four-view protocols, respectively. The average inter-method kappa was 0.823. The odds ratio of diagnosing injury using the four-view compared to the seven-view algorithm was 0.69 (CI 0.45-1.06, P = 0.0873). The modified four-view hand and wrist radiographic series produces diagnostic results comparable to the traditional seven views for acute fracture evaluation.

A Practice Quality Improvement Project: Reducing Dose of Routine Chest CT Imaging in a Busy Clinical Practice.

The purpose of this report is to describe our experience with the implementation of a practice quality improvement (PQI) project in thoracic imaging as part of the American Board of Radiology Maintenance of Certification process. The goal of this PQI project was to reduce the effective radiation dose of routine chest CT imaging in a busy clinical practice by employing the iDose(4) (Philips Healthcare) iterative reconstruction technique. The dose reduction strategy was implemented in a stepwise process on a single 64-slice CT scanner with a volume of 1141 chest CT scans during the year. In the first annual quarter, a baseline effective dose was established using the standard filtered back projection (FBP) algorithm protocol and standard parameters such as kVp and mAs. The iDose(4) technique was then applied in the second and third annual quarters while keeping all other parameters unchanged. In the fourth quarter, a reduction in kVp was also implemented. Throughout the process, the images were continually evaluated to assure that the image quality was comparable to the standard protocol from multiple other scanners. Utilizing a stepwise approach, the effective radiation dose was reduced by 23.62 and 43.63 % in quarters two and four, respectively, compared to our initial standard protocol with no perceived difference in diagnostic quality. This practice quality improvement project demonstrated a significant reduction in the effective radiation dose of thoracic CT scans in a busy clinical practice.

Toward Data-Driven Radiology Education-Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA).

The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

Building blocks for a clinical imaging informatics environment.

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study.

Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points-after image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot-a conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology.

Standardizing imaging findings representation: harnessing Common Data Elements semantics and Fast Healthcare Interoperability Resources structures.

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.

Response criteria in oncologic imaging: review of traditional and new criteria.

There has been a proliferation and divergence of imaging-based tumor-specific response criteria over the past 3 decades whose purpose is to achieve objective assessment of treatment response in oncologic clinical trials. The World Health Organization (WHO) criteria, published in 1981, were the first response criteria and made use of bidimensional measurements of tumors. The Response Evaluation Criteria in Solid Tumors (RECIST) were created in 2000 and revised in 2009. The RECIST criteria made use of unidimensional measurements and addressed several pitfalls and limitations of the original WHO criteria. Both the WHO and RECIST criteria were developed during the era of cytotoxic chemotherapeutic agents and are still widely used. However, treatment strategies changed over the past decade, and the limitations of using tumor size alone in patients undergoing targeted therapy (including arbitrarily determined cutoff values to categorize tumor response and progression, lack of information about changes in tumor attenuation, inability to help distinguish viable tumor from nonviable components, and inconsistency of size measurements) necessitated revision of these criteria. More recent criteria that are used for targeted therapies include the Choi response criteria for gastrointestinal stromal tumor, modified RECIST criteria for hepatocellular carcinoma, and Immune-related Response Criteria for melanoma. The Cheson criteria and Positron Emission Tomography Response Criteria in Solid Tumors make use of positron emission tomography to provide functional information and thereby help determine tumor viability. As newer therapeutic agents and approaches become available, it may be necessary to further modify existing anatomy-based response-assessment methodologies, verify promising functional imaging methods in large prospective trials, and investigate new quantitative imaging technologies.

Automated detection of IVC filters on radiographs with deep convolutional neural networks.

PURPOSE: To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. METHODS: A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. RESULTS: On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%). CONCLUSION: Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.

Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities.

While many case studies have described the implementation of self-scheduling tools, which allow patients to schedule visits and imaging studies asynchronously online, none have explored the impact of self-scheduling on equitable access to care.1 Using an electronic health record patient portal, University of California San Francisco deployed a self-scheduling tool that allowed patients to self-schedule diagnostic imaging studies. We analyzed electronic health record data for the imaging modalities with the option to be self-scheduled from January 1, 2021 to September 1, 2021. We used descriptive statistics to compare demographic characteristics and created a multivariable logistic regression model to identify predictors of patient self-scheduling utilization. Among all active patient portal users, Latinx, Black/African American, and non-English speaking patients were less likely to self-schedule studies. Patients with Medi-Cal, California's Medicaid program, and Medicare insurance were also less likely to self-schedule when compared with commercially insured patients. Efforts to facilitate use of patient portal-based applications are necessary to increase equitability and decrease disparities in access.

Functional imaging of the pelvic floor.

The clinical treatment of patients with anorectal and pelvic floor dysfunction is often difficult. Dynamic cystocolpoproctography (DCP) has evolved from a method of evaluating the anorectum for functional disorders to its current status as a functional method of evaluating the global pelvic floor for defecatory disorders and pelvic organ prolapse. It has both high observer accuracy and a high yield of positive diagnoses. Clinicians find it a useful diagnostic tool that can alter management decisions from surgical to medical and vice versa in many cases. Functional radiography provides the maximum stress to the pelvic floor, resulting in levator ani relaxation accompanied by rectal emptying-which is needed to diagnose defecatory disorders. It also provides organ-specific quantificative information about female pelvic organ prolapse-information that usually can only be inferred by means of physical examination. The application of functional radiography to the assessment of defecatory disorders and pelvic organ prolapse has highlighted the limitations of physical examination. It has become clear that pelvic floor disorders rarely occur in isolation and that global pelvic floor assessment is necessary. Despite the advances in other imaging methods, DCP has remained a practical, cost-effective procedure for the evaluation of anorectal and pelvic floor dysfunction. In this article, the authors describe the technique they use when performing DCP, define the radiographic criteria used for diagnosis, and discuss the limitations and clinical utility of DCP.

What is a wiki, and how can it be used in resident education?

Training as a radiology resident is a complex task. Residents frequently encounter multiple hospital systems, each with unique workflow patterns and heterogenous information systems. We identified an opportunity to ease some of the resulting anxiety and frustration by centralizing high-quality resources using a wiki. In this manuscript, we describe our choice of wiki software, give basic information about hardware requirements, detail steps for configuration, outline information included on the wiki, and present the results of a resident acceptance survey.