ACR Appropriateness Criteria® Sepsis.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. A search for the underlying cause of infection typically includes radiological imaging as part of this investigation. This document focuses on thoracic and abdominopelvic causes of sepsis. In 2017, the global incidence of sepsis was estimated to be 48.9 million cases, with 11 million sepsis-related deaths (accounting for nearly 20% of all global deaths); therefore, understanding which imaging modalities and types of studies are acceptable or not acceptable is imperative. The 5 variants provided include the most commonly encountered scenarios in the setting of sepsis along with recommendations and data for each imaging study. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Handheld Lung Ultrasound to Detect COVID-19 Pneumonia in Inpatients: A Prospective Cohort Study.

Background: Chest imaging, including chest X-ray (CXR) and computed tomography (CT), can be a helpful adjunct to nucleic acid test (NAT) in the diagnosis and management of Coronavirus Disease 2019 (COVID-19). Lung point of care ultrasound (POCUS), particularly with handheld devices, is an imaging alternative that is rapid, highly portable, and more accessible in low-resource settings. A standardized POCUS scanning protocol has been proposed to assess the severity of COVID-19 pneumonia, but it has not been sufficiently validated to assess diagnostic accuracy for COVID-19 pneumonia. Purpose: To assess the diagnostic performance of a standardized lung POCUS protocol using a handheld POCUS device to detect patients with either a positive NAT or a COVID-19-typical pattern on CT scan. Methods: Adult inpatients with confirmed or suspected COVID-19 and a recent CT were recruited from April to July 2020. Twelve lung zones were scanned with a handheld POCUS machine. Images were reviewed independently by blinded experts and scored according to the proposed protocol. Patients were divided into low, intermediate, and high suspicion based on their POCUS score. Results: Of 79 subjects, 26.6% had a positive NAT and 31.6% had a typical CT pattern. The receiver operator curve for POCUS had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for a typical CT. Using a two-point cutoff system, POCUS had a sensitivity of 0.90 and 1.00 compared to NAT and typical CT pattern, respectively, at the lower cutoff; it had a specificity of 0.90 and 0.89 compared to NAT and typical CT pattern at the higher cutoff, respectively. Conclusions: The proposed lung POCUS protocol with a handheld device showed reasonable diagnostic performance to detect inpatients with a positive NAT or typical CT pattern for COVID-19. Particularly in low-resource settings, POCUS with handheld devices may serve as a helpful adjunct for persons under investigation for COVID-19 pneumonia.

Impact of Baseline Interstitial Lung Abnormalities on Pneumonitis Risk in Patients Receiving Immune Checkpoint Inhibitors for Non-Small-Cell Lung Cancer.

INTRODUCTION/BACKGROUND: Immune-related pneumonitis is a potentially fatal complication of treatment with immune checkpoint inhibitors (ICIs). Interstitial lung disease (ILD) is associated with increased risk for pneumonitis, but the impact of interstitial abnormalities (ILA) in the absence of ILD has not been extensively assessed. We examined the relationship between ILA on pretreatment chest computed tomography (CT) scans and risk of pneumonitis in patients with non-small-cell lung cancer (NSCLC). METHODS: This retrospective cohort study included consecutive adult patients who received ICI for NSCLC between January 2013 and January 2020 at our institution. Two thoracic radiologists blinded to clinical outcomes independently reviewed pre-ICI chest CTs to identify and categorize ILA using previously published definitions. We used uni- and multivariable analysis adjusted for age, radiation, and smoking status to assess for associations between ILA, clinicopathologic characteristics, and symptomatic (CTCAE grade ≥2) pneumonitis. RESULTS: Of 475 patients who received ICI treatment and met inclusion criteria, baseline ILA were present in 78 (16.4%) patients, most commonly as a subpleural nonfibrotic pattern. In total, 43 (9.1%) of 475 patients developed symptomatic pneumonitis. Pneumonitis occurred in 16.7% of patients with ILA compared to 7.6% patients without ILA (P < .05). Presence of ground glass and extent of lung parenchymal involvement were associated with an increased risk of pneumonitis. On multivariable analysis, baseline ILA remained associated with increased risk of symptomatic pneumonitis (OR 2.2, 95% CI, 1.0-4.5). CONCLUSIONS: Baseline ILAs are associated with the development of symptomatic pneumonitis in patients with NSCLC treated with ICI. Additional studies are needed to validate these observations.

Appropriate Reliance on Artificial Intelligence in Radiology Education.

Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the performance of human-AI teams. For a future in which radiologists use interpretive AI tools routinely in clinical practice, radiology education will need to evolve to provide radiologists with the skills to use AI appropriately and wisely. In this work, we examine how overreliance on AI may develop in radiology trainees and explore how this problem can be mitigated, including through the use of AI-augmented education. Radiology trainees will still need to develop the perceptual skills and mastery of knowledge fundamental to radiology to use AI safely. We propose a framework for radiology trainees to use AI tools with appropriate reliance, drawing on lessons from human-AI interactions research.

ACR Appropriateness Criteria® Routine Chest Imaging.

Routine chest imaging has been used to identify unknown or subclinical cardiothoracic abnormalities in the absence of symptoms. Various imaging modalities have been suggested for routine chest imaging. We review the evidence for or against the use of routine chest imaging in different clinical scenarios. This document aims to determine guidelines for the use of routine chest imaging as initial imaging for hospital admission, initial imaging prior to noncardiothoracic surgery, and surveillance imaging for chronic cardiopulmonary disease. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Evaluating the Performance of a Commercially Available Artificial Intelligence Algorithm for Automated Detection of Pulmonary Embolism on Contrast-Enhanced Computed Tomography and Computed Tomography Pulmonary Angiography in Patients With Coronavirus Disease 2019.

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P<.001), with an optimal Hounsfield unit of 362 (P=.048). Conclusion: The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast-enhanced CT scans in patients with COVID-19 regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

U.S. Newspaper Coverage of Lung Cancer Screening From 2010 to 2022.

BACKGROUND. Newspapers are an important source of information for the public about low-dose CT (LDCT) lung cancer screening (LCS) and may influence public perception and knowledge of this important cancer screening service. OBJECTIVE. The purpose of this article was to evaluate the volume, content, and other characteristics of articles pertaining to LCS that have been published in U.S. newspapers. METHODS. The ProQuest U.S. Newsstream database was searched for U.S. newspaper articles referring to LCS published between January 1, 2010 (the year of publication of the National Lung Screening Trial results), and March 28, 2022. Search terms included "lung cancer screening(s)," "lung screening(s)," "low dose screening(s)," and "LDCT." Search results were reviewed to identify those articles mentioning LCS. Characteristics of included articles and originating newspapers were extracted. Articles were divided among nine readers, who independently assessed article sentiment regarding LCS and additional article content using a standardized form. RESULTS. The final analysis included 859 articles, comprising 816 nonsyndicated articles published in a single newspaper and 43 syndicated articles published in multiple newspapers. Sentiment regarding LCS was positive in 76% (651/859) of articles, neutral in 21% (184/859), and negative in 3% (24/859). Frequency of positive sentiment was lowest (61%) for articles published from 2010 to 2012; frequency of negative sentiment was highest (8%) for articles published in newspapers in the highest quartile for weekly circulation. LCS enrollment criteria were mentioned in 52% of articles, smoking cessation programs in 28%, need for annual CT in 27%, and shared decision-making in 4%. Cost or insurance coverage for LCS was mentioned in 33% in articles. A total of 64% of articles mentioned at least one benefit of LCS (most commonly early detection or possible cure of lung cancer), and 23% mentioned at least one harm (most commonly false-positives). A total of 9% of articles interviewed or mentioned a radiologist. CONCLUSION. The sentiment of U.S. newspaper articles covering LCS from 2010 to 2022 was overall positive. However, certain key elements of LCS were infrequently mentioned. CLINICAL IMPACT. The findings highlight areas for potential improvement of LCS media coverage; radiologists have an opportunity to take a more active role in this coverage.

Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Chronic Pulmonary Manifestations of COVID-19 Infection: Imaging Evaluation.

This case presents a patient with severe COVID-19 pneumonia requiring intensive care unit admission and a prolonged hospital stay. The infection resulted in long-term morbidity, functional decline, and abnormal chest CT findings. The mechanisms for long-term lung injury after COVID-19 infection, imaging appearances, and the role of imaging in follow-up are discussed.

Trends in coronary calcium score and coronary CT angiography imaging volume during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic disrupted the delivery of preventative care and management of acute diseases. This study assesses the effect of the COVID-19 pandemic on coronary calcium score and coronary CT angiography imaging volume. MATERIALS AND METHODS: A single institution retrospective review of consecutive patients presenting for coronary calcium score or coronary CT angiography examinations between January 1, 2020 to January 4, 2022 was performed. The weekly volume of calcium score and coronary CT angiogram exams were compared. RESULTS: In total, 1,817 coronary calcium score CT and 5,895 coronary CT angiogram examinations were performed. The average weekly volume of coronary CTA and coronary calcium score CT exams decreased by up to 83% and 100%, respectively, during the COVID-19 peak period compared to baseline (P < 0.0001). The post-COVID recovery through 2020 saw weekly coronary CTA volumes rebound to 86% of baseline (P = 0.024), while coronary calcium score CT volumes remained muted at only a 53% recovery (P < 0.001). In 2021, coronary CTA imaging eclipsed pre-COVID rates (P = 0.012), however coronary calcium score CT volume only reached 67% of baseline (P < 0.001). CONCLUSIONS: A significant decrease in both coronary CTA and coronary calcium score CT volume occurred during the peak-COVID-19 period. In 2020 and 2021, coronary CTA imaging eventually superseded baseline rates, while coronary calcium score CT volumes only reached two thirds of baseline. These findings highlight the importance of resumption of screening exams and should prompt clinicians to be aware of potential undertreatment of patients with coronary artery disease.

Long-term Lung Abnormalities Associated with COVID-19 Pneumonia.

In the 3rd year of the SARS-CoV-2 pandemic, much has been learned about the long-term effects of COVID-19 pneumonia on the lungs. Approximately one-third of patients with moderate-to-severe pneumonia, especially those requiring intensive care therapy or mechanical ventilation, have residual abnormalities at chest CT 1 year after presentation. Abnormalities range from parenchymal bands to bronchial dilation to frank fibrosis. Less is known about the long-term pulmonary vascular sequelae, but there appears to be a persistent, increased risk of venothromboembolic events in a small cohort of patients. Finally, the associated histologic abnormalities resulting from SARS-CoV-2 infection are similar to those seen in patients with other causes of acute lung injury.

Rate of True-Positive Findings of COVID-19 Typical Appearance at Chest CT per RSNA Consensus Guidelines in an Increasingly Vaccinated Population.

Background RSNA consensus guidelines for COVID-19-related chest CT are widely used but, to the knowledge of the authors, their rate of true-positive findings for COVID-19 pneumonia in vaccinated patients has not been assessed. Purpose To assess the rate of true-positive findings of typical appearance for COVID-19 at chest CT by using RSNA guidelines in fully vaccinated patients with polymerase chain reaction (PCR)-confirmed COVID-19 infection compared with unvaccinated patients. Materials and Methods Included were patients with COVID-19 who had typical appearance on chest CT images and one PCR test for COVID-19 with a positive result or two tests with negative results within 7 days of undergoing chest CT between January 2021 and January 2022 at a quaternary academic medical center. True-positive findings were defined as chest CT images interpreted as COVID-19 typical appearance and PCR-confirmed COVID-19 infection within 7 days. Logistic regression models were constructed to quantify the association between PCR results and vaccination status, vaccination status and COVID-19 variants, and vaccination status and number of months. Results Included were 652 patients (median age, 59 years; IQR, 48-72 years; 371 men [57%]) with CT scans classified as typical appearance. Of those patients, 483 (74%) were unvaccinated and 169 (26%) were fully vaccinated. The overall rate of true-positive findings on CT images rated as typical appearance was lower in vaccinated versus unvaccinated patients (70 of 169 [41%; 95% CI: 34, 49] vs 352 of 483 [73%; 95% CI: 69, 77]; odds ratio [OR], 3.8 [95% CI: 2.6, 5.5]; P < .001). Unvaccinated patients were more likely to have true-positive findings on CT images compared with fully vaccinated patients during the peaks of COVID-19 variants Alpha (OR, 16; 95% CI: 6, 42; P < .001) and Delta (OR, 8; 95% CI: 4, 16; P < .001), but no statistical differences were found during the peak of the Omicron variant (OR, 1.7; 95% CI: 0.3, 11; P = .56). Conclusion Fully vaccinated patients with confirmed COVID-19 breakthrough infections had lower rates of true-positive findings of COVID-19 typical appearance at chest CT. © RSNA, 2022 Supplemental material is available for this article.