Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study.

BACKGROUND: Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. METHODS: An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222). FINDINGS: 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001). INTERPRETATION: PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes. FUNDING: National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.

OBJECTIVES: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. MATERIALS AND METHODS: Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT". RESULTS: The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification. CONCLUSION: An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability. CLINICAL RELEVANCE STATEMENT: Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging. KEY POINTS: Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

PURPOSE: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans. METHODS: We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices. RESULTS: In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with 14 × $$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. CONCLUSION: Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.

From Acceptable to Superlative: Scaling a Technologist Coaching Intervention to Improve Image Quality.

OBJECTIVE: To explore factors influencing the expansion of the peer-based technologist Coaching Model Program (CMP) from its origins in mammography and ultrasound to all imaging modalities at a single tertiary academic medical center. METHODS: After success in mammography and ultrasound, efforts to expand the CMP across all Stanford Radiology modalities commenced in September 2020. From February to April 2021 as lead coaches piloted the program in these novel modalities, an implementation science team designed and conducted semistructured stakeholder interviews and took observational notes at learning collaborative meetings. Data were analyzed using inductive-deductive approaches informed by two implementation science frameworks. RESULTS: Twenty-seven interviews were collected across modalities with radiologists (n = 5), managers (n = 6), coaches (n = 11), and technologists (n = 5) and analyzed with observational notes from six learning meetings with 25 to 40 recurrent participants. The number of technologists, the complexity of examinations, or the existence of standardized auditing criteria for each modality influenced CMP adaptations. Facilitators underlying program expansion included cross-modality learning collaborative, thoughtful pairing of coach and technologist, flexibility in feedback frequency and format, radiologist engagement, and staged rollout. Barriers included lack of protected coaching time, lack of pre-existing audit criteria for some modalities, and the need for privacy of auditing and feedback data. DISCUSSION: Adaptations to each radiology modality and communication of these learnings were key to disseminating the existing CMP to new modalities across the entire department. An intermodality learning collaborative can facilitate the dissemination of evidence-based practices across modalities.

An Upstream Reparative Justice Framework for Improving Diversity in Radiology.

Health care workforce diversity is vital in combating health disparities. Despite much recent attention to downstream strategies to improve diversity in radiology, such as increased recruitment efforts and holistic application review, workforce diversity has not tangibly improved in recent decades. Yet, little discussion has been devoted to defining the obstacles that might delay, complicate, or altogether prevent persons from groups that have been traditionally marginalized and minoritized from a career in radiology. Refocusing attention to upstream barriers to medical education is vital to develop sustainable workforce diversity efforts in radiology. The purpose of this article is to highlight the varied obstacles students and trainees from historically underrepresented communities may face along the radiology career pathway and to provide concrete corollary programmatic solutions. Using a reparative justice framework, which encourages race- and gender-conscious repair of historical injustices, and the socioecological model, which recognizes an individual's choices are informed by historical and ongoing systems of power, this article advocates for tailored programs to improve justice, equity, diversity, and inclusion in radiology.

Beyond the Binary: Moving the Radiology Workforce Toward Gender Inclusion, From the AJR Special Series on DEI.

Gender representation in radiology has traditionally been evaluated and reported through binary models, accompanied by advocacy efforts focused on increasing the number of women in radiology. A paucity of data exists to understand the entire gender composition of the radiology workforce, including representation of people who are transgender and gender diverse. Further, little information exists on how to provide a supportive work environment for radiologists and support staff who identify as belonging to an underrepresented gender minority group. Intentional efforts to comprehensively understand the gender representation of the radiology workforce can help to establish a diverse workforce that is more representative of the patient populations that we serve, while promoting high-quality inclusive health care. Moving beyond gender binary thought and practices can help foster a culture of inclusion and belonging in radiology. This article provides practical steps that radiology practices can take to understand and support gender diversity beyond the binary in the radiology workforce, including providing definitions and inclusive language, understanding limitations of historical methods of gender data collection in radiology and relevant published literature, establishing best practices for future data collection, and developing a strategic vision with action items to create a more inclusive work environment.

Practical Approaches to Advancing Health Equity in Radiology, From the AJR Special Series on DEI.

Despite significant advances in health care, many patients from medically under-served populations are impacted by existing health care disparities. Radiologists are uniquely positioned to decrease health disparities and advance health equity efforts in their practices. However, literature on practical tools for advancing radiology health equity efforts applicable to a wide variety of patient populations and care settings is lacking. Therefore, this article seeks to equip radiologists with an evidence-based and practical knowledge tool kit of health equity strategies, presented in terms of four pillars of research, clinical care, education, and innovation. For each pillar, equity efforts across diverse patient populations and radiology practice settings are examined through the lens of existing barriers, current best practices, and future directions, incorporating practical examples relevant to a spectrum of patient populations. Health equity efforts provide an opportune window to transform radiology through personalized care delivery that is responsive to diverse patient needs. Guided by compassion and empathy as core principles of health equity, the four pillars provide a helpful framework to advance health equity efforts as a step toward social justice in health.

Osteopathic Versus Allopathic Radiologist Workforce Characteristics: A Medicare Administrative and Claims Data Analysis.

PURPOSE: Radiologist medical school pathways have received little attention in recent workforce investigations. With osteopathic enrollment increasing, we assessed the osteopathic versus allopathic composition of the radiologist workforce. METHODS: Linking separate Medicare Doctors and Clinicians Initiative databases and Physician and Other Supplier Files from 2014 through 2019, we assessed (descriptively and using multivariate panel logistic regression modeling) individual and practice characteristics of radiologists who self-reported medical degrees. RESULTS: Between 2014 and 2019, as the number of osteopathic radiologists increased 46.0% (4.7% to 6.0% of total radiologist workforce), the number of allopathic radiologists increased 12.1% (representing a relative workforce decrease from 95.3% to 94.0%). For each year since completing training, practicing radiologists were 3.7% less likely to have osteopathic (versus allopathic) degrees (odds ratio [OR] = 0.96 per year, P < .01). Osteopathic radiologists were less likely to work in urban (versus rural) areas (OR = 0.95), and compared with the Midwest, less likely to work in the Northeast (OR = 0.96), South (OR = 0.95), and West (OR = 0.94) (all P < .01). Except for cardiothoracic imaging (OR = 0.78, P = .24), osteopathic radiologists were more likely than allopathic radiologists to practice as general (rather than subspecialty) radiologists (range OR = 0.37 for nuclear medicine to OR = 0.65 for neuroradiology, all P < .01). CONCLUSIONS: Osteopathic physicians represent a fast-growing earlier-career component of the radiologist workforce. Compared with allopathic radiologists, they more frequently practice as generalist radiologists, in rural areas, and in the Midwest. Given recent calls for greater general and rural radiology coverage, increasing osteopathic representation in the national radiologist workforce could improve patient access.

Imaging of Sarcopenia.

Sarcopenia is currently underdiagnosed and undertreated, but this is expected to change because sarcopenia is now recognized with a specific diagnosis code that can be used for billing in some countries, as well as an expanding body of research on prevention, diagnosis, and management. This article focuses on practical issues of increasing interest by highlighting 3 hot topics fundamental to understanding sarcopenia in older adults: definitions and terminology, current diagnostic imaging techniques, and the emerging role of opportunistic computed tomography.

Charges for Shoppable Musculoskeletal Imaging Examinations: CMS Transparency Compliance and Variability Among 250 U.S. Hospitals.

As of January 2021, among other transparency requirements, the Centers for Medicare & Medicaid Services require that hospitals publish consumer-friendly displays of charges for shoppable health care services, including four musculoskeletal imaging examinations. Of 250 selected U.S. hospitals, all published charges for these four examinations, although 21% did not provide charges within consumer-friendly displays. Bed count was larger for compliant hospitals than for noncompliant hospitals (500 vs 384 beds). All four examinations had widely variable charges (representing a 73.8-fold difference).

Variables Influencing Radiology Volume Recovery During the Next Phase of the Coronavirus Disease 2019 (COVID-19) Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has reduced radiology volumes across the country as providers have decreased elective care to minimize the spread of infection and free up health care delivery system capacity. After the stay-at-home order was issued in our county, imaging volumes at our institution decreased to approximately 46% of baseline volumes, similar to the experience of other radiology practices. Given the substantial differences in severity and timing of the disease in different geographic regions, estimating resumption of radiology volumes will be one of the next major challenges for radiology practices. We hypothesize that there are six major variables that will likely predict radiology volumes: (1) severity of disease in the local region, including potential subsequent "waves" of infection; (2) lifting of government social distancing restrictions; (3) patient concern regarding risk of leaving home and entering imaging facilities; (4) management of pent-up demand for imaging delayed during the acute phase of the pandemic, including institutional capacity; (5) impact of the economic downturn on health insurance and ability to pay for imaging; and (6) radiology practice profile reflecting amount of elective imaging performed, including type of patients seen by the radiology practice such as emergency, inpatient, outpatient mix and subspecialty types. We encourage radiology practice leaders to use these and other relevant variables to plan for the coming weeks and to work collaboratively with local health system and governmental leaders to help ensure that needed patient care is restored as quickly as the environment will safely permit.

ACR Statement on Safe Resumption of Routine Radiology Care During the Coronavirus Disease 2019 (COVID-19) Pandemic.

The ACR recognizes that radiology practices are grappling with when and how to safely resume routine radiology care during the coronavirus disease 2019 (COVID-19) pandemic. Although it is unclear how long the pandemic will last, it may persist for many months. Throughout this time, it will be important to perform safe, comprehensive, and effective care for patients with and patients without COVID-19, recognizing that asymptomatic transmission is common with this disease. Local idiosyncrasies prevent a single prescriptive strategy. However, general considerations can be applied to most practice environments. A comprehensive strategy will include consideration of local COVID-19 statistics; availability of personal protective equipment; local, state, and federal government mandates; institutional regulatory guidance; local safety measures; health care worker availability; patient and health care worker risk factors; factors specific to the indication(s) for radiology care; and examination or procedure acuity. An accurate risk-benefit analysis of postponing versus performing a given routine radiology examination or procedure often is not possible because of many unknown and complex factors. However, this is the overriding principle: If the risk of illness or death to a health care worker or patient from health care-acquired COVID-19 is greater than the risk of illness or death from delaying radiology care, the care should be delayed; however, if the opposite is true, the radiology care should proceed in a timely fashion.

Everything Every Radiologist Always Wanted (and Needs) to Know About Clinical Decision Support.

As of January 2020, clinical decision support needs to be implemented across US health systems for advanced diagnostic imaging services. This article reviews the history, importance, and hurdles of clinical decision support and discusses a few pearls and pitfalls regarding its implementation.

Assessment of the Radiology Support, Communication and Alignment Network to Reduce Medical Imaging Overutilization: A Multipractice Cohort Study.

PURPOSE: The aim of this study was to determine whether participation in Radiology Support, Communication and Alignment Network (R-SCAN) results in a reduction of inappropriate imaging in a wide range of real-world clinical environments. METHODS: This quality improvement study used imaging data from 27 US academic and private practices that completed R-SCAN projects between January 25, 2015, and August 8, 2018. Each project consisted of baseline, educational (intervention), and posteducational phases. Baseline and posteducational imaging cases were rated as high, medium, or low value on the basis of validated ACR Appropriateness Criteria®. Four cohorts were generated: a comprehensive cohort that included all eligible practices and three topic-specific cohorts that included practices that completed projects of specific Choosing Wisely topics (pulmonary embolism, adnexal cyst, and low back pain). Changes in the proportion of high-value cases after R-SCAN intervention were assessed for each cohort using generalized estimating equation logistic regression, and changes in the number of low-value cases were analyzed using Poisson regression. RESULTS: Use of R-SCAN in the comprehensive cohort resulted in a greater proportion of high-value imaging cases (from 57% to 79%; odds ratio, 2.69; 95% confidence interval, 1.50-4.86; P = .001) and 345 fewer low-value cases after intervention (incidence rate ratio, 0.45; 95% confidence interval, 0.29-0.70; P < .001). Similar changes in proportion of high-value cases and number of low-value cases were found for the pulmonary embolism, adnexal cyst, and low back pain cohorts. CONCLUSIONS: R-SCAN participation was associated with a reduced likelihood of inappropriate imaging and is thus a promising tool to enhance the quality of patient care and promote wise use of health care resources.

Multisite Implementation of Radiology-TEACHES (Technology-Enhanced Appropriateness Criteria Home for Education Simulation).

PURPOSE: After encouraging results from a single-institution pilot, a novel case-based education portal using integrated clinical decision support at the simulated point of order entry was implemented at multiple institutions to evaluate whether the program is scalable and results transferable. The program was designed to fill key health systems' science gaps in traditional medical education curricula, ultimately aiding the transition from volume to value in health care. The module described uses commonly encountered medical vignettes to provide learners with a low-stakes educational environment to improve their awareness and apply curricular content regarding appropriate resource utilization, patient safety, and cost. METHODS: In 2016 and 2017, the team implemented the modules at eight US medical schools. A total of 199 learners participated in this institutional review board-approved study; 108 completed the module, and 91 were in the control group. RESULTS: The module group had higher posttest scores than their control group peers, after controlling for pretest scores (ß = 4.05, P < .001). The greatest knowledge gains were on questions related to chest radiography (22% improvement) and adnexal cysts (20.33% improvement) and the least on items related to pulmonary embolism (0.33% improvement). The majority of learners expressed satisfaction with the educational content provided (70.4%) and an increased perception to appropriately select imaging studies (65.2%). CONCLUSIONS: This program is promising as a standardized educational resource for widespread implementation in developing health systems science curricula. Learners at multiple institutions judged this educational resource as valuable and, through this initiative, synthesized practice behaviors by applying evidence-based guidelines in a cost-effective, safe, and prudent manner.

Optimizing Performance by Preventing Disruptive Behavior in Radiology.

Disruptive behaviors impede delivery of high-value health care by negatively impacting patient outcomes and increasing costs. Health care is brimming with potential triggers of disruptive behavior. Given omnipresent environmental and cultural factors such as constrained resources, stressful environments, commercialization, fatigue, unrealistic expectation of perfectionism, and burdensome documentation, a burnout epidemic is raging, and medical providers are understandably at tremendous risk to succumb and manifest these unprofessional behaviors. Each medical specialty has its own unique challenges. Radiology is not exempt; these issues do not respect specialty or professional boundaries. Unfortunately, preventive measures are too frequently overlooked, provider support programs rarely exist, and often organizations either tolerate or ineffectively manage the downstream disruptive behaviors. This review summarizes the background, key definitions, contributing factors, impact, prevention, and management of disruptive behavior. Every member of the health care team can gain from an improved understanding and awareness of the contributing factors and preventive measures. Application of these principles can foster a just culture of understanding, trust, support, respect, and teamwork balanced with accountability. The authors discuss these general topics along with specific issues for radiologists in the current medical environment. Patients, providers, health care organizations, and society all stand to benefit from better prevention of these behaviors. There is a strong moral, ethical, and business case to address this issue head-on. ©RSNA, 2018.