Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week.

Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.

Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance.

PURPOSE: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth. PATIENTS AND METHODS: Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance. RESULTS: There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001). CONCLUSION: Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.

Comparative Effectiveness of Coronary CT Angiography and Standard of Care for Evaluating Acute Chest Pain: A Living Systematic Review and Meta-Analysis.

Purpose: To perform a living systematic review and meta-analysis of randomized controlled trials comparing the effectiveness of coronary CT angiography (CCTA) and standard of care (SOC) in the evaluation of acute chest pain (ACP). Materials and Methods: Multiple electronic databases were systematically searched, with the most recent search conducted on October 31, 2022. Studies were stratified into two groups according to the pretest probability for acute coronary syndrome (group 1 with predominantly low-to-intermediate risk vs group 2 with high risk). A meta-regression analysis was also conducted using participant risk, type of SOC used, and the use or nonuse of high-sensitivity troponins as independent variables. Results: The final analysis included 22 randomized controlled trials (9379 total participants; 4956 assigned to CCTA arms and 4423 to SOC arms). There was a 14% reduction in the length of stay and a 17% reduction in immediate costs for the CCTA arm compared with the SOC arm. In group 1, the length of stay was 17% shorter and costs were 21% lower using CCTA. There was no evidence of differences in referrals to invasive coronary angiography, myocardial infarction, mortality, rate of hospitalization, further stress testing, or readmissions between CCTA and SOC arms. There were more revascularizations (relative risk, 1.45) and medication changes (relative risk, 1.33) in participants with low-to-intermediate acute coronary syndrome risk and increased radiation exposure in high-risk participants (mean difference, 7.24 mSv) in the CCTA arm compared with the SOC arm. The meta-regression analysis found significant differences between CCTA and SOC arms for rate of hospitalization, further stress testing, and medication changes depending on the type of SOC (P < .05). Conclusion: The results support the use of CCTA as a safe, rapid, and less expensive in the short term strategy to exclude acute coronary syndrome in low- to intermediate-risk patients presenting with acute chest pain.Keywords: Acute Coronary Syndrome, Chest Pain, Emergency Department, Coronary Computed Tomography, Usual Care Supplemental material is available for this article. Published under a CC BY 4.0 license.

Detection of Low Blood Hemoglobin Levels on Pulmonary CT Angiography: A Feasibility Study Combining Dual-Energy CT and Machine Learning.

OBJECTIVES: To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. METHODS: Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The best model was evaluated in a separate test set (n = 24). RESULTS: Blood pool attenuation was significantly lower in cases than controls (p-values < 0.01), except in the right atrium (p = 0.06). The machine learning model had sensitivity, specificity, and accuracy of 83%, 92%, and 88%, respectively. Measurements at the descending aorta had the highest relative importance among all features; a threshold of 43 HU yielded sensitivity, specificity, and accuracy of 68%, 76%, and 72%, respectively. CONCLUSION: VNC imaging and machine learning shows good diagnostic performance for detecting anemia on DECT CTPA.

RadioGraphics Update: Pictorial Guide to CAD-RADS 2.0.

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.

Know your way around acute unenhanced CT during global iodinated contrast crisis: a refresher to ED radiologists.

Due to a contrast shortage crisis resulting from the decreased supply of iodinated contrast agents, the American College of Radiology (ACR) has issued a guidance statement followed by memoranda from various hospitals to preserve and prioritize the limited supply of contrast. The vast majority of iodinated contrast is used by CT, with a minority used by vascular and intervention radiology, fluoroscopy, and other services. A direct consequence is a paradigm shift to large volume unenhanced CT scans being utilized for acute and post traumatic patients in EDs, an uncharted territory for most radiologists and trainees. This article provides radiological diagnostic guidance and a pictorial example through systematic review of common unenhanced CT findings in the acute setting.

Detection of Incidental Pulmonary Embolism on Conventional Contrast-Enhanced Chest CT: Comparison of an Artificial Intelligence Algorithm and Clinical Reports.

BACKGROUND. Artificial intelligence (AI) algorithms have shown strong performance for detection of pulmonary embolism (PE) on CT examinations performed using a dedicated protocol for PE detection. AI performance is less well studied for detecting PE on examinations ordered for reasons other than suspected PE (i.e., incidental PE [iPE]). OBJECTIVE. The purpose of this study was to assess the diagnostic performance of an AI algorithm for detection of iPE on conventional contrast-enhanced chest CT examinations. METHODS. This retrospective study included 2555 patients (mean age, 53.2 ± 14.5 [SD] years; 1340 women, 1215 men) who underwent 3003 conventional contrast-enhanced chest CT examinations (i.e., not using pulmonary CTA protocols) between September 2019 and February 2020. A commercial AI algorithm was applied to the images to detect acute iPE. A vendor-supplied natural language processing (NLP) algorithm was applied to the clinical reports to identify examinations interpreted as positive for iPE. For all examinations that were positive by the AI-based image review or by NLP-based report review, a multireader adjudication process was implemented to establish a reference standard for iPE. Images were also reviewed to identify explanations of AI misclassifications. RESULTS. On the basis of the adjudication process, the frequency of iPE was 1.3% (40/3003). AI detected four iPEs missed by clinical reports, and clinical reports detected seven iPEs missed by AI. AI, compared with clinical reports, exhibited significantly lower PPV (86.8% vs 97.3%, p = .03) and specificity (99.8% vs 100.0%, p = .045). Differences in sensitivity (82.5% vs 90.0%, p = .37) and NPV (99.8% vs 99.9%, p = .36) were not significant. For AI, neither sensitivity nor specificity varied significantly in association with age, sex, patient status, or cancer-related clinical scenario (all p > .05). Explanations of false-positives by AI included metastatic lymph nodes and pulmonary venous filling defect, and explanations of false-negatives by AI included surgically altered anatomy and small-caliber subsegmental vessels. CONCLUSION. AI had high NPV and moderate PPV for iPE detection, detecting some iPEs missed by radiologists. CLINICAL IMPACT. Potential applications of the AI tool include serving as a second reader to help detect additional iPEs or as a worklist triage tool to allow earlier iPE detection and intervention. Various explanations of AI misclassifications may provide targets for model improvement.

What the Baby Formula and Medical Contrast Material Shortages Have in Common: Insights and Recommendations for Managing the Iodinated Contrast Media Shortage.

The impact of supply chain and supply chain logistics, including personnel directly and indirectly related to the movement of supplies, has come to light in a variety of industries since the global COVID-19 pandemic. Acutely, the experience with baby formula and iodinated contrast material exposes key vulnerabilities to supply chains. The rather sudden diminished availability of iodinated contrast material has forced health care systems to engage in more judicious use of product through catalyzing the adoption of behaviors that had been recommended and deemed reasonable prior to the shortage. The authors describe efforts at a large, academic safety net county health system to conserve iodinated contrast media by optimizing contrast media use in the CT department and changing ordering patterns of referring providers. Special attention is given to opportunities to conserve contrast material in cardiothoracic imaging, including low kV and dual-energy CT techniques. A values-based leadership philosophy and collaboration with key stakeholders facilitate effective response to the critical shortage and rapid deployment of iodinated contrast media conservation strategies. Last, while the single-supplier model is efficient and cost-effective, its application to critically necessary services such as health care must be questioned considering disruptions related to the COVID-19 pandemic. Keywords: CT, Intravenous Contrast Agents, CT-Spectral Imaging (Dual Energy) ©RSNA, 2022.

Contributions to the knowledge of the Cicadellini sharpshooters (Hemiptera: Cicadellidae) associated with citrus orchards in Argentina.

Xylella fastidiosa Wells et al. is a xylem-borne bacterium that causes some of the most important plant diseases to woody plants such as citrus, olives, almonds and other cultures. This pathogen is mainly transmitted by sharpshooters, among which the tribe Cicadellini (Cicadellinae) includes the largest number of proven vectors. The correct identification of the vectors, along with biological and phenological information, are necessary to identify the key vectors involved in the spread of the bacterium and, consequently, establish control strategies and evaluate risks at a local or regional scale. However, lack of information on the Cicadellini from Argentina has delayed the implementation of control measures. Based on surveys conducted in the main citrus producing areas along with bibliographic data, this contribution provides the first list of Cicadellini species from Argentina that are potential vectors of X. fastidiosa; an identification key to these sharpshooters is provided. Twelve species were recorded from northeastern citrus groves, while from northwestern orchards, with previous information totally absent, 10 species were recorded. Eight species are shared by all producing regions, and five of them are proven vectors of X. fastidiosa (Bucephalogonia xanthophis (Berg), Dilobopterus costalimai Young, Macugonalia cavifrons (Stål), M. leucomelas (Walker), Sonesimia grossa (Signoret)). This contribution provides 22 new insect-plant relationships, information on their natural enemies, the geographic distribution of all species is broadened and the female genitalia of three proven vectors are described for the first time.

Acometimento da função ventricular direita em pacientes com esclerose sistêmica e doença pulmonar intersticial: avaliação pelo doppler tecidual / Impairment of right ventricular function in patients with systemic sclerosis and interstitial lung disease: tissue doppler evaluation

Introdução: A esclerose sistêmica (ES) é uma doença autoimune do tecido conjuntivo que cursa com fibrose e disfunção microvascular. O envolvimento dos órgãos viscerais, incluindo os pulmões e o coração, é a principal causa de óbito na ES. Nesse contexto, analisamos a relação entre os parâmetros ventriculares direitos (VD) pela ecocardiografia com Doppler tecidual e o acometimento pulmonar em pacientes com ES. Métodos: Os pacientes que preencheram os Critérios de Classificação da ES de 2013 foram submetidos à ecocardiografia com Doppler tecidual para avaliação da função sistólica (fração de ejeção) ventricular esquerda (VE), enquanto a função sistólica do VD foi avaliada por meio da fração de variação de área do VD (fractional area change ­ FAC), velocidade (sistólica) do Doppler tecidual, índice de desempenho miocárdico (IDM) e excursão sistólica do plano anular tricúspide (TAPSE). A pressão sistólica pulmonar foi estimada por insuficiência tricúspide. A tomografia computadorizada de alta resolução (TCAR) de tórax avaliou a presença de fibrose pulmonar. De acordo com os resultados da TCAR, os pacientes foram divididos em 2 subgrupos: Grupo I, incluindo pacientes com fibrose pulmonar (n=26), e Grupo II sem fibrose (n=17). Resultados: Entre os 43 pacientes com ES, a maioria era do sexo feminino (86%) com idade de 51±12 anos. Todos os pacientes apresentavam função ventricular sistólica normal, avaliada pela FEVE>55% e FAC VD>35%. Não houve diferença significativa em termos de idade ou duração da doença para os grupos. Exceto pela diminuição das velocidades do Doppler tecidual em pacientes com fibrose pulmonar, todos os índices de desempenho do VD foram semelhantes. Conclusão: Em pacientes com ES e fibrose pulmonar, o Doppler tecidual identifica acometimento miocárdico longitudinal precoce do VD, apesar do desempenho sistólico radial preservado do VD.(AU)

Introduction: Systemic sclerosis (SSc) is an autoimmune tissue connective disease that courses with fibrosis and microvascular dysfunction. Involvement of the visceral organs, including the lungs and heart, is the main cause of death among patients with SSc. In this context, here we analyzed the relationship between right ventricle (RV) parameters assessed by tissue Doppler echocardiography and lung involvement in patients with SSc. Methods: Patients fulfilling the 2013 SSc Classification Criteria underwent tissue Doppler echocardiography for the assessment of left ventricular (LV) systolic function (ejection fraction) and RV fractional area change (FAC), tissue Doppler s' (systolic) velocity, myocardial performance index, and tricuspid annular plane systolic excursion for the assessment of RV systolic function. Pulmonary systolic pressure was estimated using tricuspid regurgitation. Chest high-resolution computed tomography was used to evaluate the presence of pulmonary fibrosis. The patients were divided into two subgroups accordingly: Group I, patients with pulmonary fibrosis (n=26); and Group II, those without fibrosis (n=17). Results: Among the 43 patients with SSc, most were female (86%), and the mean age was 51 ± 12 years. All patients had normal systolic ventricular function as evidenced by an LV ejection fraction > 55% and an RV FAC > 35%. No significant intergroup difference was noted in age or disease duration. Except for a decreased tissue Doppler s' velocity in patients with lung fibrosis, all indexes of RV performance were similar. Conclusion: In patients with SSc and pulmonary fibrosis, tissue Doppler identified early RV longitudinal myocardial involvement despite preserved RV radial systolic performance.(AU)

The educational community and its knowledge and perceptions of native and invasive alien species.

Environmental education seeks to foster an appreciation for nature and the impact of humans on it while introducing citizens to scientific thinking. Biological invasions affect different aspects of life on earth and mandate urgent management actions. Education and public awareness are strongly recommended for successful prevention and management of invasive alien species (IAS). This work presents a study on knowledge and perception of the educational community of Argentina about native species and IAS. We designed an on-line semi-structured questionnaire to examine perception of the environment, recognition of native species and IAS and awareness about biological invasions. Educators recognised an important number of biotic components, mostly represented by trees, birds and mammals. Recognition of native species and IAS, and awareness of biological invasions were different between NST (Natural Science Teachers) and non-NST. Respondents had different performances when they were exposed to recognising native species though written names or photographs. Out of 532 respondents, 56% knew what biological invasions are, 21% answered "Maybe" and 23% had never heard about them. We need to foster capacity-building and encourage a two-way communication between educators and scientists, formally and informally, to engage the participation of the whole society in recognition, prevention and management of IAS.

Deep Learning-Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19-positive and COVID-19-negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.

The RSNA Pulmonary Embolism CT Dataset.

Supplemental material is available for this article.

CT Findings and Patterns of e-Cigarette or Vaping Product Use-Associated Lung Injury: A Multicenter Cohort of 160 Cases.

BACKGROUND: e-Cigarette or vaping-induced lung injury (EVALI) causes a spectrum of CT lung injury patterns. Relative frequencies and associations with vaping behavior are unknown. RESEARCH QUESTION: What are the frequencies of imaging findings and CT patterns in EVALI and what is the relationship to vaping behavior? STUDY DESIGN AND METHODS: CT scans of 160 subjects with EVALI from 15 institutions were retrospectively reviewed. CT findings and patterns were defined and agreed on via consensus. The parenchymal organizing pneumonia (OP) pattern was defined as regional or diffuse ground-glass opacity (GGO) ± consolidation without centrilobular nodules (CNs). An airway-centered OP pattern was defined as diffuse CNs with little or no GGO, whereas a mixed OP pattern was a combination of the two. Other patterns included diffuse alveolar damage (DAD), acute eosinophilic-like pneumonia, and pulmonary hemorrhage. Cases were classified as atypical if they did not fit into a pattern. Imaging findings, pattern frequencies, and injury severity were correlated with substance vaped (marijuana derives [tetrahydrocannabinol] [THC] only, nicotine derivates only, and both), vaping frequency, regional geography, and state recreational THC legality. One-way analysis of variance, χ2 test, and multivariable analyses were used for statistical analysis. RESULTS: A total of 160 patients (79.4% men) with a mean age of 28.2 years (range, 15-68 years) with EVALI underwent CT scan. Seventy-seven (48.1%), 15 (9.4%), and 68 (42.5%) patients admitted to vaping THC, nicotine, or both, respectively. Common findings included diffuse or lower lobe GGO with subpleural (78.1%), lobular (59.4%), or peribronchovascular (PBV) sparing (40%). Septal thickening (50.6%), lymphadenopathy (63.1%), and CNs (36.3%) were common. PBV sparing was associated with younger age (P = .02). Of 160 subjects, 156 (97.5%) had one of six defined patterns. Parenchymal, airway-centered, and mixed OP patterns were seen in 89 (55.6%), 14 (8.8%), and 32 (20%) patients, respectively. Acute eosinophilic-like pneumonia (six of 160, 3.8%), DAD (nine of 160, 5.6%), pulmonary hemorrhage (six of 160, 3.8%), and atypical (four of 160, 2.5%) patterns were less common. Increased vaping frequency was associated with more severe injury (P = .008). Multivariable analysis showed a negative association between vaping for > 6 months and DAD pattern (P = .03). Two subjects (1.25%) with DAD pattern died. There was no relation between pattern and injury severity, geographic location, and state legality of recreational use of THC. INTERPRETATION: EVALI typically causes an OP pattern but exists on a spectrum of acute lung injury. Vaping habits do not correlate with CT patterns except for negative correlation between vaping > 6 months and DAD pattern. PBV sparing, not previously described in acute lung injury, is a common finding.

Quantitative CT Detects Undiagnosed Low Bone Mineral Density in Oncologic Patients Imaged With 18F-FDG PET/CT.

PURPOSE: We assessed the prevalence of low bone mineral density (BMD) in oncologic patients undergoing F-FDG PET/CT. PATIENTS AND METHODS: This is a retrospective analysis of 100 patients who underwent F-FDG PET/CT at a single center from October 2015 till May 2016. Quantitative CT (QCT) was used to assess BMD at the lumbar spine (BMDQCT) and femoral necks (BMDCTXA). SUVmax was used to evaluate metabolic activity of the bone marrow. Risk of osteoporosis-related fractures was calculated with femoral neck BMDCTXA and the FRAX algorithm, which was compared against measurements of CT attenuation of the trabecular bone at L1 (L1HU). RESULTS: Osteoporosis and osteopenia were respectively present in 16% and 46% of patients 50 years and older. Bone marrow SUVmax was correlated with BMD at the lumbar spine (ρ = 0.36, P < 0.001). Increased age and low marrow SUVmax were associated with low BMDQCT at the lumbar spine (both P < 0.001), whereas increased age, female sex, and low marrow SUVmax were associated with low BMDCTXA at the femoral necks (P < 0.001, P < 0.001, P = 0.01, respectively). L1HU had an area under the curve of 0.95 (95% confidence interval [CI], 0.90-0.99) for detecting increased risk for osteoporosis-related fracture, with best threshold of 125.8 HU (95% CI, 115.7-144.9) yielding sensitivity of 100% (95% CI, 0.92-1.00), specificity of 0.90 (95% CI, 0.76-0.97), and accuracy of 0.91 (95% CI, 0.79-0.97). CONCLUSIONS: Low BMD is frequent in oncologic patients undergoing F-FDG PET/CT. Decreased F-FDG avidity of the bone marrow correlates with decreased BMD, validating the link between osteoporosis and bone marrow fat. L1HU could be a simple and accurate approach for detecting patients at risk for osteoporosis-related fractures using PET/CTdata.

Dual-energy CT and coronary imaging.

Dual-energy computed tomography has been proposed for enhancing the evaluation of coronary artery disease in many fronts. However, the clinical translation of such applications has followed a slower pace of clinical translation. This paper will review the evidence supporting the use of dual-energy computed tomography in coronary artery disease (CAD) and provide some practical illustrations, while underscoring the challenges and gaps in knowledge that have contributed to this phenomenon.

Update on Multienergy CT: Physics, Principles, and Applications.

Multienergy CT involves acquisition of two or more CT measurements with distinct energy spectra. Using the differential attenuation of tissues and materials at different x-ray energies, multienergy CT allows distinction of tissues and materials beyond that possible with conventional CT. Multienergy CT technologies can operate at the source or detector level. Dual-source, rapid tube-voltage switching, and dual-layer detector CT are the most commonly used multienergy CT technologies. Most of the currently available technologies typically use two energy levels, commonly referred to as dual-energy CT. With use of two or more energy bins, photon-counting detector CT can perform multienergy CT beyond current dual-energy CT technologies. Multienergy CT postprocessing can be performed in the projection or image domain using two-material or multimaterial decomposition. The most commonly used multienergy CT images are virtual monoenergetic images (VMIs), iodine maps, virtual noncontrast (VNC) images, and uric acid images. Low-energy VMIs are used to boost contrast signal and enhance lesion conspicuity. High-energy VMIs are used to decrease some artifacts. Iodine maps are used to evaluate perfusion, characterize lesions, and evaluate response to therapy. VNC images are used to characterize lesions and save radiation dose by eliminating true noncontrast images from multiphasic acquisitions. Uric acid images are used for characterization of renal calculi and gout. Online supplemental material is available for this article. ©RSNA, 2020.

Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - Secondary Publication.

Routine screening CT for the identification of COVID-19 pneumonia is currently not recommended by most radiology societies. However, the number of CTs performed in persons under investigation (PUI) for COVID-19 has increased. We also anticipate that some patients will have incidentally detected findings that could be attributable to COVID-19 pneumonia, requiring radiologists to decide whether or not to mention COVID-19 specifically as a differential diagnostic possibility. We aim to provide guidance to radiologists in reporting CT findings potentially attributable to COVID-19 pneumonia, including standardized language to reduce reporting variability when addressing the possibility of COVID-19. When typical or indeterminate features of COVID-19 pneumonia are present in endemic areas as an incidental finding, we recommend contacting the referring providers to discuss the likelihood of viral infection. These incidental findings do not necessarily need to be reported as COVID-19 pneumonia. In this setting, using the term "viral pneumonia" can be a reasonable and inclusive alternative. However, if one opts to use the term "COVID-19" in the incidental setting, consider the provided standardized reporting language. In addition, practice patterns may vary, and this document is meant to serve as a guide. Consultation with clinical colleagues at each institution is suggested to establish a consensus reporting approach. The goal of this expert consensus is to help radiologists recognize findings of COVID-19 pneumonia and aid their communication with other healthcare providers, assisting management of patients during this pandemic.

Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans: Validation in the DHS.

BACKGROUND: Coronary artery calcium scoring only represents a small fraction of all information available in noncontrast cardiac computed tomography (CAC-CT). We hypothesized that an automated pipeline using radiomics and machine learning could identify phenotypic information about high-risk left ventricular hypertrophy (LVH) embedded in CAC-CT. METHODS: This was a retrospective analysis of 1982 participants from the DHS (Dallas Heart Study) who underwent CAC-CT and cardiac magnetic resonance. Two hundred twenty-four participants with high-risk LVH were identified by cardiac magnetic resonance. We developed an automated adaptive atlas algorithm to segment the left ventricle on CAC-CT, extracting 107 radiomics features from the volume of interest. Four logistic regression models using different feature selection methods were built to predict high-risk LVH based on CAC-CT radiomics, sex, height, and body surface area in a random training subset of 1587 participants. RESULTS: The respective areas under the receiver operating characteristics curves for the cluster-based model, the logistic regression model after exclusion of highly correlated features, and the penalized logistic regression models using least absolute shrinkage and selection operators with minimum or one SE λ values were 0.74 (95% CI, 0.67-0.82), 0.74 (95% CI, 0.67-0.81), 0.76 (95% CI, 0.69-0.83), and 0.73 (95% CI, 0.66-0.80) for detecting high-risk LVH in a distinct validation subset of 395 participants. CONCLUSIONS: Ventricular segmentation, radiomics features extraction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of LVH in participants undergoing CAC-CT, without the need for additional imaging or radiation exposure. Registration: URL http://www.clinicaltrials.gov. Unique identifier: NCT00344903.