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.

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.

Translation of Quantitative Imaging Biomarkers into Clinical Chest CT.

Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).

Organ doses evaluation for chest computed tomography procedures with TL dosimeters: Comparison with Monte Carlo simulations.

PURPOSE: To evaluate organ doses in routine and low-dose chest computed tomography (CT) protocols using an experimental methodology. To compare experimental results with results obtained by the National Cancer Institute dosimetry system for CT (NCICT) organ dose calculator. To address the differences on organ dose measurements using tube current modulation (TCM) and fixed tube current protocols. METHODS: An experimental approach to evaluate organ doses in pediatric and adult anthropomorphic phantoms using thermoluminescent dosimeters (TLDs) was employed in this study. Several analyses were performed in order to establish the best way to achieve the main results in this investigation. The protocols used in this study were selected after an analysis of patient data collected from the Institute of Radiology of the School of Medicine of the University of São Paulo (InRad). The image quality was evaluated by a radiologist from this institution. Six chest adult protocols and four chest pediatric protocols were evaluated. Lung doses were evaluated for the adult phantom and lung and thyroid doses were evaluated for the pediatric phantom. The irradiations were performed using both a GE and a Philips CT scanner. Finally, organ doses measured with dosimeters were compared with Monte Carlo simulations performed with NCICT. RESULTS: After analyzing the data collected from all CT examinations performed during a period of 3 yr, the authors identified that adult and pediatric chest CT are among the most applied protocol in patients in that clinical institution, demonstrating the relevance on evaluating organ doses due to these examinations. With regards to the scan parameters adopted, the authors identified that using 80 kV instead of 120 kV for a pediatric chest routine CT, with TCM in both situations, can lead up to a 28.7% decrease on the absorbed dose. Moreover, in comparison to the standard adult protocol, which is performed with fixed mAs, TCM, and ultra low-dose protocols resulted in dose reductions of up to 35.0% and 90.0%, respectively. Finally, the percent differences found between experimental and Monte Carlo simulated organ doses were within a 20% interval. CONCLUSIONS: The results obtained in this study measured the impact on the absorbed dose in routine chest CT by changing several scan parameters while the image quality could be potentially preserved.

Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology.

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. © RSNA, 2018 Online supplemental material is available for this article.

Individual Positive End-expiratory Pressure Settings Optimize Intraoperative Mechanical Ventilation and Reduce Postoperative Atelectasis.

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Intraoperative lung-protective ventilation has been recommended to reduce postoperative pulmonary complications after abdominal surgery. Although the protective role of a more physiologic tidal volume has been established, the added protection afforded by positive end-expiratory pressure (PEEP) remains uncertain. The authors hypothesized that a low fixed PEEP might not fit all patients and that an individually titrated PEEP during anesthesia might improve lung function during and after surgery. METHODS: Forty patients were studied in the operating room (20 laparoscopic and 20 open-abdominal). They underwent elective abdominal surgery and were randomized to institutional PEEP (4 cm H2O) or electrical impedance tomography-guided PEEP (applied after recruitment maneuvers and targeted at minimizing lung collapse and hyperdistension, simultaneously). Patients were extubated without changing selected PEEP or fractional inspired oxygen tension while under anesthesia and submitted to chest computed tomography after extubation. Our primary goal was to individually identify the electrical impedance tomography-guided PEEP value producing the best compromise of lung collapse and hyperdistention. RESULTS: Electrical impedance tomography-guided PEEP varied markedly across individuals (median, 12 cm H2O; range, 6 to 16 cm H2O; 95% CI, 10-14). Compared with PEEP of 4 cm H2O, patients randomized to the electrical impedance tomography-guided strategy had less postoperative atelectasis (6.2 ± 4.1 vs. 10.8 ± 7.1% of lung tissue mass; P = 0.017) and lower intraoperative driving pressures (mean values during surgery of 8.0 ± 1.7 vs. 11.6 ± 3.8 cm H2O; P < 0.001). The electrical impedance tomography-guided PEEP arm had higher intraoperative oxygenation (435 ± 62 vs. 266 ± 76 mmHg for laparoscopic group; P < 0.001), while presenting equivalent hemodynamics (mean arterial pressure during surgery of 80 ± 14 vs. 78 ± 15 mmHg; P = 0.821). CONCLUSIONS: PEEP requirements vary widely among patients receiving protective tidal volumes during anesthesia for abdominal surgery. Individualized PEEP settings could reduce postoperative atelectasis (measured by computed tomography) while improving intraoperative oxygenation and driving pressures, causing minimum side effects.

Role of FDG PET/CT in the Eighth Edition of TNM Staging of Non-Small Cell Lung Cancer.

Lung cancer is the leading cause of cancer-related mortality in the United States, and accurate staging plays a vital role in determining prognosis and treatment. The recently revised eighth edition of the TNM staging system for lung cancer defines new T and M descriptors and updates stage groupings on the basis of substantial differences in survival. There are new T descriptors that are based on the findings at histopathologic examination, and T descriptors are reassigned on the basis of tumor size and extent. No changes were made to the N descriptors in the eighth edition of the TNM staging of lung cancer, because the four N categories that are based on the location of the diseased nodes can be used to consistently predict prognosis. The eighth edition includes a new M1b descriptor for patients with a single extrathoracic metastatic lesion in a single organ (M1b), because they have better survival and different treatment options, compared with those with multiple extrathoracic lesions (M1c). Examination with fluorine 18 fluorodeoxyglucose (FDG) PET/CT is the standard of care and is an integral part of the clinical staging of patients with lung cancer. To provide the treating physicians with accurate staging information, radiologists and nuclear medicine physicians should be aware of the updated classification system and should be cognizant of the site-specific strengths and limitations of FDG PET/CT. In this article, the eighth edition of the TNM staging system is reviewed, as well as the role of FDG PET/CT in the staging of non-small cell lung carcinoma. ©RSNA, 2018.

Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging to Identify Clear Cell Renal Cell Carcinoma in cT1a Renal Masses.

PURPOSE: The detection of small renal masses is increasing with the use of cross-sectional imaging, although many incidental lesions have negligible metastatic potential. Among malignant masses clear cell renal cell carcinoma is the most prevalent and aggressive subtype. A method to identify such histology would aid in risk stratification. Our goal was to evaluate a likelihood scale for multiparametric magnetic resonance imaging in the diagnosis of clear cell histology. MATERIALS AND METHODS: We retrospectively reviewed the records of patients with cT1a masses who underwent magnetic resonance imaging and partial or radical nephrectomy from December 2011 to July 2015. Seven radiologists with different levels of experience who were blinded to final pathology findings independently reviewed studies based on a predefined algorithm. They applied a clear cell likelihood score, including 1-definitely not, 2-probably not, 3-equivocal, 4-probably and 5-definitely. Binary classification was used to determine the accuracy of clear cell vs all other histologies. Interobserver agreement was calculated with the weighted κ statistic. RESULTS: A total of 110 patients with 121 masses were identified. Mean tumor size was 2.4 cm and 50% of the lesions were clear cell. Defining clear cell as scores of 4 or greater demonstrated 78% sensitivity and 80% specificity while scores of 3 or greater showed 95% sensitivity and 58% specificity. Interobserver agreement was moderate to good with a mean κ of 0.53. CONCLUSIONS: A clear cell likelihood score used with magnetic resonance imaging can reasonably identify clear cell histology in small renal masses and may decrease the number of diagnostic renal mass biopsies. Standardization of imaging protocols and reporting criteria is needed to improve interobserver reliability.

Pulmonary involvement in long-term mixed connective tissue disease: functional trends and image findings after 10 years.

OBJECTIVES: Interstitial lung disease (ILD) is highly prevalent in patients with mixed connective tissue disease (MCTD). However, little is known about the long-term progression of ILD in MCTD. The aims of this study were to describe pulmonary function test (PFT) and high-resolution computed tomography (HRCT) results in long-term MCTD patients, to measure changes in PFT and HRCT results over a 10-year period, and to ascertain correlations in functional and imaging data. METHODS: In this retrospective cohort study, comparison between baseline and follow-up PFT and HRCT data was performed for 39 unselected consecutive MCTD patients. RESULTS: At baseline, 51% of the patients had abnormal PFTs. Forced vital capacity (FVC) was slightly reduced at baseline (77% of predicted), but remained stable after 10 years. A relative decrease of 15% in the diffusion capacity for carbon monoxide (DLCO) was detected (from 84% to 71% of predicted, p<0.001). The median lower lobes ILD-HRCT score progressed from 7.5% at baseline to 11.2% at follow-up (p=0.02), and findings of traction bronchiolectasis and honeycombing increased (p<0.05). A moderate negative correlation was observed between functional parameters and quantification of image findings. CONCLUSIONS: Functional and radiologic alterations suggestive of ILD in long-term MCTD patients are prevalent, mild, and progressed slightly over time. The most sensitive parameters for detecting subtle progression of ILD in MCTD patients are trends in DLCO, quantification of lower-lobes disease by HRCT (lower-lobes %ILD-HRCT score), and qualitative analysis of HRCT imaging.

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.

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.

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.

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.

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.

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.

The RSNA Pulmonary Embolism CT Dataset.

Supplemental material is available for this article.

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.

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.

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

Routine screening CT for the identification of coronavirus disease 19 (COVID-19) pneumonia is currently not recommended by most radiology societies. However, the number of CT examinations performed in persons under investigation 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 health care providers, assisting management of patients during this pandemic. Published under a CC BY 4.0 license.