Chest Radiograph Interpretation: Online Teaching Resources for Non-Radiologists.

Chest radiographs provide vital information to clinicians. Medical professionals need to be proficient in interpreting chest radiographs to care for patients. This review examines online methods for teaching chest radiograph interpretation to non-radiologists. An online database search of PubMed and the Cochrane Databases of Systematic Reviews revealed 25 potential evidence sources. After using the similar articles tool on PubMed, eight evidence sources met the inclusion criteria. Three sources supported the use of online learning to increase students' confidence regarding chest radiograph interpretation. The evidence suggests that through self-directed online learning, students can learn skills to diagnose disease processes as well as to confirm the placement of invasive lines and tubes. Using online learning for teaching radiograph interpretation to non-radiologists is an evolving practice. A flexible schedule is needed when implementing the electronic learning process for busy students. Monitoring module completion and postlearning assessment of knowledge is important. Further research is warranted on electronic teaching of chest radiograph interpretation in nurse anesthesia programs. A list of potential online resources for teaching chest radiograph interpretation is presented.

An NHS Trust comparison of reporting radiographers and consultant radiologists ability to identify cancer on chest radiographs with confirmation through a Fast track CT thorax referral system.

INTRODUCTION: The National Institute for Health and Care Excellence (NICE) recommends that GPs initially refer patients with suspected lung cancer for a chest X-ray (CXR). The Radiology department has a 'fast track system' to identify those patients who may have lung cancer on CXR and are referred for a CT thorax with contrast to help determine a cancer diagnosis. This fast track system was put in place to ensure the NICE guidelines and NHS England's standards on a faster cancer diagnosis are being met. This audit studied the ability of radiologists and reporting radiographers to identify lung cancer on CXRs and the accuracy of the fast-track system. METHODS: 846 cases with lung alerts were analysed and 545 CXRs were audited. The CXRs were split into images reported by radiologists (168) and those reported by reporting radiographers (377). CT thorax results were collected through PACS and Cerner computer systems to identify if the 'fast track' system had yielded a "positive", "negative", or "other findings" result for lung cancer. RESULTS: 32.8% (179) of CXRs flagged for lung cancer were positive, 40.6% (221) were negative, and 26.6% (145) had other findings. Chi square statistical test showed no significant difference (p = 0.14) between the two reporting groups in their ability to identify lung cancer on CXRs. 27% (38) of CXRs flagged by radiologists and 35% (125) by reporting radiographers were positive for lung cancer. CONCLUSION: This clinical audit indicates, reporting radiographers and radiologists are not statistically significantly different regarding their ability to identify lung cancer on CXRs, when supported by the fast track system. The fast-track system had a 59.4 % accuracy rate, detected by the number of imaging of reports that identified a serious pathology. This concludes that the system is performing well, yet could still be improved. IMPLICATIONS FOR PRACTICE: This audit provides further evidence for the value of developing and deploying reporting radiographers for projection radiography reporting.

Impact of a structured reporting template on the quality of HRCT radiology reports for interstitial lung disease.

PURPOSE: This QI study compared the completeness of HRCT radiology reports before and after the implementation of a disease-specific structured reporting template for suspected cases of interstitial lung disease (ILD). MATERIALS AND METHODS: A pre-post study of radiology reports for HRCT of the thorax at a multicenter health system was performed. Data was collected in 6-month period intervals before (June 2019-November 2019) and after (January 2021-June 2021) the implementation of a disease-specific template. The use of the template was voluntary. The primary outcome measure was the completeness of HRCT reports graded based on the documentation of ten descriptors. The secondary outcome measure assessed which descriptor(s) improved after the intervention. RESULTS: 521 HRCT reports before and 557 HRCT reports after the intervention were reviewed. Of the 557 reports, 118 reports (21%) were created using the structured reporting template. The mean completeness score of the pre-intervention group was 9.20 (SD = 1.08) and the post-intervention group was 9.36 (SD = 1.03) with a difference of -0.155, 95% CI [-0.2822, -0.0285, p < 0.0001]. Within the post-intervention group, the mean completeness score of the unstructured reports was 9.25 (SD = 1.07) and the template reports was 9.93 (SD = 0.25) with a difference of -0.677, 95% CI [-0.7871, -0.5671, p < 0.0001]. After the intervention, the use of two descriptors improved significantly: presence of honeycombing from 78.3% to 85.1% (p < 0.0039) and technique from 90% to 96.6% (p < 0.0001). DISCUSSION: Shifting to disease-specific structured reporting for HRCT exams of suspected ILD is beneficial, as it improves the completeness of radiology reports. Further research on how to improve the voluntary uptake of a disease-specific template is needed to help increase the acceptance of structured reporting among radiologists.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

OBJECTIVES: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. METHODS: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630-0.665), 35.3% (CI, 24.7-47.8), and 94.2% (CI, 93.3-95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534-0.676 without AI and 0.571-0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96-10.27 s longer with AI assistance (all p values < 0.05). CONCLUSIONS: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. KEY POINTS: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice.

Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

PURPOSE: Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic abnormalities in a multicenter health screening cohort. METHODS AND MATERIALS: We retrospectively evaluated the radiology reports and Lunit results for CXR at several health screening centers in August 2020. Lunit was adopted as a clinical decision support system (CDSS) in routine clinical practice. Subsequently, radiologists completed their reports after reviewing the Lunit results. The DLA result was provided as a color map with an abnormality score (%) for thoracic lesions when the score was greater than the predefined cutoff value of 15%. Concordance was achieved when (a) the radiology reports were consistent with the DLA results ("accept"), (b) the radiology reports were partially consistent with the DLA results ("edit") or had additional lesions compared with the DLA results ("add"). There was discordance when the DLA results were rejected in the radiology report. In addition, we compared the reading times before and after Lunit was introduced. Finally, we evaluated systemic usability scale questionnaire for radiologists and physicians who had experienced Lunit. RESULTS: Among 3,113 participants (1,157 men; mean age, 49 years), thoracic abnormalities were found in 343 (11.0%) based on the CXR radiology reports and 621 (20.1%) based on the Lunit results. The concordance rate was 86.8% (accept: 85.3%, edit: 0.9%, and add: 0.6%), and the discordance rate was 13.2%. Except for 479 cases (7.5%) for whom reading time data were unavailable (n = 5) or unreliable (n = 474), the median reading time increased after the clinical integration of Lunit (median, 19s vs. 14s, P < 0.001). CONCLUSION: The real-world multicenter health screening cohort showed a high concordance of the chest X-ray report and the Lunit result under the clinical integration of the deep-learning solution. The reading time slight increased with the Lunit assistance.

Diagnostic accuracy of ultra-low-dose chest computed tomography in an emergency department.

BACKGROUND: This study examined whether ultra-low-dose chest computed tomography (ULD-CT) could improve detection of acute chest conditions. PURPOSE: To determine (i) whether diagnostic accuracy of ULD-CT is superior to supine chest X-ray (sCXR) for acute chest conditions and (ii) the feasibility of ULD-CT in an emergency department. MATERIAL AND METHODS: From 1 February to 31 July 2019, 91 non-traumatic patients from the Emergency Department were prospectively enrolled in the study if they received an sCXR. An ULD-CT and a non-contrast chest CT (NCCT) scan were then performed. Three radiologists assessed the sCXR and ULD-CT examinations for cardiogenic pulmonary edema, pneumonia, pneumothorax, and pleural effusion. Resources and effort were compared for sCXR and ULD-CT to evaluate feasibility. Diagnostic accuracy was calculated for sCXR and ULD-CT using NCCT as the reference standard. RESULTS: The mean effective dose of ULD-CT was 0.05±0.01 mSv. For pleural effusion and cardiogenic pulmonary edema, no difference in diagnostic accuracy between ULD-CT and sCXR was observed. For pneumonia and pneumothorax, sensitivities were 100% (95% confidence interval [CI] 69-100) and 50% (95% CI 7-93) for ULD-CT and 60% (95% CI 26-88) and 0% (95% CI 0-0) for sCXR, respectively. Median examination time was 10 min for ULD-CT vs. 5 min for sCXR (P<0.001). For ULD-CT 1-2 more staff members were needed compared to sCXR (P<0.001). ULD-CT was rated more challenging to perform than sCXR (P<0.001). CONCLUSION: ULD-CT seems equal or better in detecting acute chest conditions compared to sCXR. However, ULD-CT examinations demand more effort and resources.

Not so FAST-Chest ultrasound underdiagnoses traumatic pneumothorax.

BACKGROUND: Ultrasonography for trauma is a widely used tool in the initial evaluation of trauma patients with complete ultrasonography of trauma (CUST) demonstrating equivalence to computed tomography (CT) for detecting clinically significant abdominal hemorrhage. Initial reports demonstrated high sensitivity of CUST for the bedside diagnosis of pneumothorax. We hypothesized that the sensitivity of CUST would be greater than initial supine chest radiograph (CXR) for detecting pneumothorax. METHODS: A retrospective analysis of patients diagnosed with pneumothorax from 2018 through 2020 at a Level I trauma center was performed. Patients included had routine supine CXR and CUST performed prior to intervention as well as confirmatory CT imaging. All CUST were performed during the initial evaluation in the trauma bay by a registered sonographer. All imaging was evaluated by an attending radiologist. Subgroup analysis was performed after excluding occult pneumothorax. Immediate tube thoracostomy was defined as tube placement with confirmatory CXR within 8 hours of admission. RESULTS: There were 568 patients screened with a diagnosis of pneumothorax, identifying 362 patients with a confirmed pneumothorax in addition to CXR, CUST, and confirmatory CT imaging. The population was 83% male, had a mean age of 45 years, with 85% presenting due to blunt trauma. Sensitivity of CXR for detecting pneumothorax was 43%, while the sensitivity of CUST was 35%. After removal of occult pneumothorax (n = 171), CXR was 78% sensitive, while CUST was 65% sensitive (p < 0.01). In this subgroup, CUST had a false-negative rate of 36% (n = 62). Of those patients with a false-negative CUST, 50% (n = 31) underwent tube thoracostomy, with 85% requiring immediate placement. CONCLUSION: Complete ultrasonography of trauma performed on initial trauma evaluation had lower sensitivity than CXR for identification of pneumothorax including clinically significant pneumothorax requiring tube thoracostomy. Using CUST as the primary imaging modality in the initial evaluation of chest trauma should be considered with caution. LEVEL OF EVIDENCE: Diagnostic Test study, Level IV.

Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study.

BACKGROUND: Diagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists' diagnostic performance when used as a second reader. METHODS: CXRs of patients with FN (a body temperature ≥ 38.3 °C, or a sustained body temperature ≥ 38.0 °C for an hour; absolute neutrophil count < 500/mm3) obtained between January and December 2017 were consecutively included, from a single tertiary referral hospital. Reference standards for the diagnosis of pneumonia were defined by consensus of two thoracic radiologists after reviewing medical records and CXRs. A commercialized, deep learning-based CAD system was retrospectively applied to detect pulmonary infiltrates on CXRs. For comparing performance, five radiologists independently interpreted CXRs initially without the CAD results (radiologist-alone interpretation), followed by the interpretation with CAD. The sensitivities and specificities for detection of pneumonia were compared between radiologist-alone interpretation and interpretation with CAD. The standalone performance of the CAD was also evaluated, using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Moreover, sensitivity and specificity of standalone CAD were compared with those of radiologist-alone interpretation. RESULTS: Among 525 CXRs from 413 patients (52.3% men; median age 59 years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, P = 0.003) while their specificity remained similar (75.4% to 76.8%, P = 0.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, P < 0.001) and lower specificity (64.8% vs. 75.4%, P < 0.001) compared to radiologist-alone interpretation. CONCLUSIONS: In patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists' performance.

Diagnostic performance of chest radiography measurements for the assessment of cardiac chamber enlargement.

BACKGROUND: The cardiothoracic ratio (CTR) is commonly assessed on chest radiography for detection of cardiac chamber enlargement, but the traditional cutpoint of 0.5 has low specificity. We sought to evaluate the diagnostic accuracy of new measurement techniques for the detection of cardiac enlargement on chest radiographs. METHODS: We obtained retrospective cross-sectional data on consecutive patients who underwent both chest radiography and cardiac magnetic resonance imaging (MRI) within a 14-day interval between 2006 and 2016 at a large academic hospital network. We established the presence of cardiac chamber enlargement using cardiac MRI as the reference standard. We evaluated the diagnostic performance of different techniques for measuring heart size and CTR on frontal chest radiographs. RESULTS: Of 152 patients included, 81 (53%) were men and the mean age was 52 years. Maximum heart diameter had the highest area under the receiver operating characteristic curve for detection of cardiac enlargement (0.827, 95% confidence interval 0.760-0.894). In the subgroup of posteroanterior chest radiography studies (n = 101), a CTR cutpoint of 0.50 had only moderate sensitivity (72%) and specificity (72%). In men, a maximum heart diameter cutpoint of 15 cm had a sensitivity of 86% and a negative likelihood ratio of 0.24, and a cutpoint of 19 cm had a specificity of 100% and a positive likelihood ratio of infinity. In women, a maximum heart diameter cutpoint of 13 cm had a sensitivity of 91% and a negative likelihood ratio of 0.15, and a cutpoint of 17 cm had a specificity of 91% and a positive likelihood ratio of 3.5. INTERPRETATION: A traditional CTR cutpoint of 0.5 has limited diagnostic value. Simple heart diameter measurements have higher diagnostic performance measures than CTR.

Influence of the use of various imaging units and projections on the radiation dose received by children during chest digital radiography.

OBJECTIVE: To investigate the impact of the use of different imaging units and projections on radiation dose and image quality during chest digital radiography (DR) in 3- and 4-year-old children. METHODS: Two hundred forty 3- and 4-year-old participants requiring chest DR were included; they were divided into three groups: supine anterior-posterior projection (APP), standing APP and standing posterior-anterior projection (PAP). Each group included 40 participants who were evaluated using the same imaging unit. The dose area product (DAP) and the entrance surface dose (ESD) were recorded after each exposure. The visual grading analysis score (VGAS) was used to evaluate image quality, and the longitudinal distance (LD) from the apex of the right lung to the apex of the right diaphragm was used to evaluate the inspiration extent. RESULTS: DAP and ESD were significantly lower in the standing PAP and APP groups than in the supine APP group (P<0.05), but LD was significantly higher in the standing PAP and APP groups than in the supine APP group (P<0.05). Additionally, the pulmonary field area was significantly higher for the standing PAP group than for the standing and supine APP groups (P<0.05). The correlations between ESD, DAP, and VGAS were positive (P<0.001), showing that larger ESD and DAP correspond to higher VGAS. The correlations between ESD, DAP, and body mass index (BMI) were also positive (P<0.05), indicating that higher BMI corresponds to larger ESD and DAP. Finally, no differences in DAP, ESD, VGAS, LD, pulmonary field area, or BMI were noted between males and females (P>0.05). CONCLUSION: The radiation dose to superficial organs may be lower with standing PAP than with standing APP during chest DR. Standing PAP should be selected for chest DR in 3- and 4-year-old children, as it may decrease the required radiation dose.

The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT.

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

Screening performance of the chest X-ray in adult blunt trauma evaluation: Is it effective and what does it miss?

BACKGROUND: Although chest x-ray (CXR) is often used as a screening tool for thoracic injury in adult blunt trauma assessment, its screening performance is unclear. Using chest CT as the referent standard, we sought to determine the screening performance of CXR for injury. METHODS: We analyzed data from the NEXUS Chest CT study, in which we prospectively enrolled blunt trauma patients older than 14 years who received chest imaging as part of their evaluation at nine level I trauma centers. For this analysis, we included patients who had both CXR and chest CT. We used CT as the referent standard and categorized injuries as clinically major or minor according to an a priori expert panel classification. RESULTS: Of 11,477 patients enrolled, 4501 had both CXR and chest CT; 1496 (33.2%) were found to have injury, of which 256 (17%) were classified as major injury. CXR missed injuries in 818 patients (54.7%), of which 63 (7.7%) were classified as major injuries. For injuries of major clinical significance, CXR had a sensitivity of 75.4% (95% confidence interval [CI] 69.6-80.4%), specificity of 86.2% (95% CI 85.1-87.2%), negative predictive value of 98.3 (95%CI 97.9-98.6%), and positive predictive value of 24.7 (95%CI 22.9-26.7%). For any injury CXR had a sensitivity of 45.3% (95% CI 42.8-47.9%), specificity of 96.6% (95% CI 95.9-97.2%), negative predictive value of 78% (95% CI 77.2-78.8%), and positive predictive value of 86.9% (95% CI 84.5-89.0%). The most common missed major injuries were pneumothorax (30/185; 16.2%), spinal fractures (19/39; 48.7%), and hemothorax (8/70; 11.4%). The most common missed minor injuries were rib fractures (381/836; 45.6%), pulmonary contusion (203/462; 43.9%), and sternal fractures (153/229; 66.8%). CONCLUSIONS: When used alone, without other trauma screening criteria, CXR has poor screening performance for blunt thoracic injury.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

ABSTRACT: The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

Vertebral heart score to evaluate cardiac size in thoracic radiographs of 124 healthy rats (Rattus norvegicus).

Dilated cardiomyopathy is a relatively common disease in pet rats (Rattus norvegicus); however, there is a lack of radiographic references for the normal cardiac size in this species. The aim of this prospective, anatomical and reference interval study was to establish quantitative radiographic reference range measurements for the vertebral heart score (VHS) in rats. Right lateral (RL), ventrodorsal (VD), and dorsoventral (DV) radiographs of clinically healthy rats (n = 124) were evaluated. Measurements were performed by 2 expert readers who were unaware of signalment data. The mean values and references intervals of VHS were 7.7 and 7.0-8.5 for the RL, 7.5 and 6.6-8.6 for the VD, and 7.9 and 6.9-9.0 for the DV, with VHS values greater in males than in females. The measurements reported in this study can be used by the clinician as an objective tool to evaluate cardiac size in rats, in order to improve the diagnosis and treatment of cardiac diseases.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.

[Spatial and temporal distribution and predictive value of chest CT scoring in patients with COVID-19].

Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.

Family medicine residents' skill levels in emergency chest X-ray interpretation.

BACKGROUND: Family medicine physicians may encounter a wide variety of conditions, including acute and urgent cases. Considering the limited access to diagnostic investigations in primary care practice, chest X-ray remains the imaging modality of choice. The current study assessed the competency of family medicine residents in the interpretation of chest X-rays for emergency conditions and to compare it with that of diagnostic radiology residents, general practitioners, and medical interns. METHODS: An online survey was distributed to 600 physicians, including family medicine residents, medical interns, general practitioners, and diagnostic radiology residents. The study included some background information such as gender, years in practice, training type, interest in pulmonary medicine and diagnostic radiology, and having adequate training on the interpretation of chest X-rays. The survey had 10 chest X-ray cases with brief clinical information. Participants were asked to choose the most likely diagnosis and to rate their degree of confidence in the interpretation of the chest X-ray for each case. RESULTS: The survey was completed by 205 physicians (response rate = 34.2%). The overall diagnostic accuracy was 63.1% with a significant difference between family medicine and radiology residents (58.0% vs. 90.5%; P < 0.001). The COVID-19 pneumonia (85.4%) and pneumoperitoneum (80.5%) cases had the highest diagnostic accuracy scores. There was a significant correlation between the diagnostic confidence and accuracy (rs = 0.39; P < 0.001). Multivariable regression analysis revealed that being diagnostic radiology residents (odds ratio [OR]: 13.0; 95% confidence interval [CI]: 2.5-67.7) and having higher diagnostic confidence (OR: 2.2; 95% CI: 1.3-3.8) were the only independent predictors of achieving high diagnostic accuracy. CONCLUSION: The competency of family medicine residents in the interpretation of chest X-ray for emergency conditions was far from optimal. The introduction of radiology training courses on emergency conditions seems imperative. Alternatively, the use of tele-radiology in primary healthcare centers should be considered.

Usefulness of staging chest-CT in patients with operable breast cancer.

OBJECTIVE: The aim of this study was to investigate the usefulness of staging chest-CT in terms of diagnostic yield and false-referral rate in patients with operable breast cancer. MATERIALS AND METHODS: This study was approved by the institutional review border. In this retrospective study, we reviewed patients who underwent staging chest-CT between January 2014 and June 2016. Reference standard was defined as a combination of pathology and radiologic tumor changes in accordance with primary tumor or metastatic lesions and stability during the 12-month follow-up period. We calculated diagnostic yield and false-referral rates stratified by pathologic stage. The important ancillary findings of staging chest-CT were also recorded. RESULTS: A total of 1,342 patients were included in this study. Of these, four patients (0.3%; 4/1342) had true pulmonary metastasis. Diagnostic yields of stage I, II, III disease were 0.0% (0/521), 0.3% (2/693), and 1.6% (2/128), respectively. The overall false-referral rate was 4.6% (62/1342); false-referral rates of stage I, II, and III disease were 5.0% (26/521), 3.8% (26/693), and 7.8% (10/128), respectively. No occult thoracic metastasis occurred within 12 months of staging chest-CT. Nineteen patients showed significant ancillary findings besides lung metastasis, including primary lung cancer (n = 9). The overall diagnostic yield of ancillary findings was 1.7% (23 of 1342). CONCLUSIONS: The incidence of pulmonary metastasis was near zero for pathologic stages I/II and slightly higher (although still low; 1.6%). for stage III. Considering its low diagnostic yield and substantial false-referral rates, staging chest-CT might not be useful in patients with operable breast cancer.

Portable Chest Radiography as an Exclusionary Test for Adverse Clinical Outcomes During the COVID-19 Pandemic.

BACKGROUND: Chest radiography (CXR) often is performed in the acute setting to help understand the extent of respiratory disease in patients with COVID-19, but a clearly defined role for negative chest radiograph results in assessing patients has not been described. RESEARCH QUESTION: Is portable CXR an effective exclusionary test for future adverse clinical outcomes in patients suspected of having COVID-19? STUDY DESIGN AND METHODS: Charts of consecutive patients suspected of having COVID-19 at five EDs in New York City between March 19, 2020, and April 23, 2020, were reviewed. Patients were categorized based on absence of findings on initial CXR. The primary outcomes were hospital admission, mechanical ventilation, ARDS, and mortality. RESULTS: Three thousand two hundred forty-five adult patients, 474 (14.6%) with negative initial CXR results, were reviewed. Among all patients, negative initial CXR results were associated with a low probability of future adverse clinical outcomes, with negative likelihood ratios of 0.27 (95% CI, 0.23-0.31) for hospital admission, 0.24 (95% CI, 0.16-0.37) for mechanical ventilation, 0.19 (95% CI, 0.09-0.40) for ARDS, and 0.38 (95% CI, 0.29-0.51) for mortality. Among the subset of 955 patients younger than 65 years and with a duration of symptoms of at least 5 days, no patients with negative CXR results died, and the negative likelihood ratios were 0.17 (95% CI, 0.12-0.25) for hospital admission, 0.09 (95% CI, 0.02-0.36) for mechanical ventilation, and 0.09 (95% CI, 0.01-0.64) for ARDS. INTERPRETATION: Initial CXR in adult patients suspected of having COVID-19 is a strong exclusionary test for hospital admission, mechanical ventilation, ARDS, and mortality. The value of CXR as an exclusionary test for adverse clinical outcomes is highest among young adults, patients with few comorbidities, and those with a prolonged duration of symptoms.

Proof of Concept: Phantom Study to Ensure Quality and Safety of Portable Chest Radiography Through Glass During the COVID-19 Pandemic.

BACKGROUND: Chest radiography is often used to detect lung involvement in patients with suspected pneumonia. Chest radiography through glass walls of an isolation room is a technique that could be immensely useful in the current COVID-19 pandemic. PURPOSE: The purpose of this study was to ensure quality and radiation safety while acquiring portable chest radiographs through the glass doors of isolation rooms using an adult anthropomorphic thorax phantom. MATERIALS AND METHODS: Sixteen chest radiographs were acquired utilizing different exposure factors without glass, through the smart glass, and through regular glass. Images were scored independently by 2 radiologists for quantum mottle and sharpness of anatomical structures using a 5-point Likert scale. Statistically significant differences in Likert scale scores and entrance surface dose (ESD) between images acquired without glass and through the smart and regular glass were tested. Interreader reliability was also evaluated. RESULTS: Compared with conventional radiography, equal or higher mean image quality scores (mottle and anatomical structures) were observed with the smart glass using 100 kVp at 12 mAs and 20 mAs and 125 kVp at 6.3 mAs (100 kVp at 2 mAs and 125 kVp at 3.2 mAs were used for conventional radiography observations). There was no statistically significant difference in the Likert scale scores for image quality and the entrance surface dose for radiographs acquired without glass, through the smart glass, and through regular glass. Backscatter from the smart glass was minimal at a distance of 3 m and was recorded as zero at a distance of 4 m from the x-ray tube outside an isolation room. CONCLUSIONS: Good-quality portable chest radiographs can be obtained safely through the smart glass doors of the isolation room. However, this technique does result in minor backscatter radiation. Modifications in the exposure factors (such as increasing milliampere seconds) may be required to optimize image quality while using this technique.