Computed Tomography Coronary Angiography on a Detector-Based Spectral Computed Tomography Platform: Evaluation of Patients With Coronary Artery Disease Reporting and Data System Score of 3 and Higher.

PURPOSE: Computed tomography (CT) coronary angiography performed on a detector-based spectral scanner helps more closely approximate severity of stenosis with nuclear medicine and cardiac catheterization tests compared with single-energy CT (SECT) in patients with an original CAD-RADS score of 3 and higher. METHODS: This retrospective trial was conducted between January 2017 and December 2019 and included 52 patients with a CAD-RADS score of 3 and higher. Two reading sessions were performed 6 weeks apart. The first reading session was performed using only conventional images and the second reading session was performed using spectral results. Detector-based spectral CT CAD-RADS scores were compared with cardiac stress test and/or cardiac catheterization results for final characterization of stenosis in 41 segments from 32 patients. The mean CAD-RADS score was calculated for both the conventional images and spectral images. RESULTS: The CAD-RADS score for SECT and the score for spectral CT for the 41 segments were compared. Available associated stress test and/or cardiac catheterization results were also compared with CAD-RADS scores. In 51% (21/41), a diagnosis concordant with best practices results was achieved with the help of spectral CT results. A mean CAD-RADS score of 3.56 was obtained using spectral results, compared with 3.93 using conventional images. A 2-tailed paired t test determined the difference to be significant with a P value of 0.007. CONCLUSIONS: Computed tomography coronary angiography is feasible on a detector-based spectral CT scanner and can improve diagnostic confidence over SECT angiography in patients with an original CAD-RADS score of 3 and higher.

Coronavirus Disease (COVID-19): The Value of Chest Radiography for Patients Greater Than Age 50 Years at an Earlier Timepoint of Symptoms Compared With Younger Patients.

Background: A relative paucity of data exists regarding chest radiography (CXR) in diagnosis of coronavirus disease (COVID-19) compared to computed tomography. We address the use of a strict pattern of CXR findings for COVID-19 diagnosis, specifically during early onset of symptoms with respect to patient age. Methods: We performed a retrospective study of patients under investigation for COVID-19 who presented to the emergency department during the COVID-19 outbreak of 2020 and had CXR within 1 week of symptoms. Only reverse transcription polymerase chain reaction (RT-PCR)-positive patients were included. Two board-certified radiologists, blinded to RT-PCR results, assessed 60 CXRs in consensus and assigned 1 of 3 patterns: characteristic, atypical, or negative. Atypical patterns were subdivided into more suspicious or less suspicious for COVID-19. Results: Sixty patients were included: 30 patients aged 52 to 88 years and 30 patients aged 19 to 48 years. Ninety-three percent of the older group demonstrated an abnormal CXR and were more likely to have characteristic and atypical-more suspicious findings in the first week after symptom onset than the younger group. The relationship between age and CXR findings was statistically significant (χ2 [2, n=60]=15.70; P=0.00039). The relationship between negative and characteristic COVID-19 CXR findings between the 2 age cohorts was statistically significant with Fisher exact test resulting in a P value of 0.001. Conclusion: COVID-19 positive patients >50 years show earlier, characteristic patterns of statistically significant CXR changes than younger patients, suggesting that CXR is useful in the early diagnosis of infection. CXR can be useful in early diagnosis of COVID-19 in patients older than 50 years.

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective.

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χ McNema r ' s statistic 2 = 163 . 2 and a p-value of 2.23 × 10-37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

Diagnosis in a snap: a pilot study using Snapchat in radiologic didactics.

PURPOSE: To evaluate Snapchat, an image-based social media platform, as a tool for emergency radiologic didactics comparing image interpretation on mobile devices with conventional analysis on a classroom screen. MATERIALS AND METHODS: Seven radiology residents (4 juniors, 3 seniors;4 males, 3 females; 28.4 years old, ± 1.7 years) were shown 5 emergent radiologic cases using Snapchat and 5 cases of similar content and duration on a classroom projector over 4 weeks. All images depicted diagnoses requiring immediate communication to ordering physicians. Performance was scored 0-2 (0 = complete miss, 1 = major finding, but missed the diagnosis, 2 = correct diagnosis) by two attending radiologists in consensus. RESULTS: All residents performed better on Snapchat each week. In weeks 1-4, juniors scored 21/40 (52.5%), 23/40 (57.5%), 19/40 (47.5%), and 18/40 (45%) points using Snapchat compared with 13/40 (32.5%), 23/40 (57.5%), 14/40 (35%), and 13/40 (32.5%), respectively, each week by projector, while seniors scored 19/30 (63.3%), 21/30 (70%), 27/30 (90%), and 21/30 (70%) on Snapchat versus 16/30 (53.3%), 19/30 (63.3%), 20/30 (66.7%), and 20/30 (66.7%) on projector. Four-week totals showed juniors scoring 81/160 (50.6%) on Snapchat and 63/160 (39.4%) by projector compared with seniors scoring 88/120 (73.3%) and 75/120 (62.5%), respectively. Performance on Snapchat was statistically, significantly better than via projector during weeks 1 and 3 (p values 0.0019 and 0.0031). CONCLUSION: Radiology residents interpreting emergency cases via Snapchat showed higher accuracy compared with using a traditional classroom screen. This pilot study suggests that Snapchat may have a role in the digital radiologic classroom's evolution.

A Characteristic Chest Radiographic Pattern in the Setting of the COVID-19 Pandemic.

PURPOSE: To determine the utility of chest radiography in aiding clinical diagnosis of coronavirus disease 2019 (COVID-19) utilizing reverse-transcription polymerase chain reaction (RT-PCR) as the standard of comparison. MATERIALS AND METHODS: A retrospective study was performed of persons under investigation for COVID-19 presenting to this institution during the exponential growth phase of the COVID-19 outbreak in New Orleans (March 13-25, 2020). Three hundred seventy-six in-hospital chest radiographic examinations for 366 individual patients were reviewed along with concurrent RT-PCR tests. Two experienced radiologists categorized each chest radiograph as characteristic, nonspecific, or negative in appearance for COVID-19, utilizing well-documented COVID-19 imaging patterns. Chest radiograph categorization was compared against RT-PCR results to determine the utility of chest radiography in diagnosing COVID-19. RESULTS: Of the 366 patients, the study consisted of 178 male (49%) and 188 female (51%) patients with a mean age of 52.7 years (range, 17 to 98 years). Of the 376 chest radiographic examinations, 37 (10%) exhibited the characteristic COVID-19 appearance; 215 (57%) exhibited the nonspecific appearance; and 124 (33%) were considered negative for a pulmonary abnormality. Of the 376 RT-PCR tests evaluated, 200 (53%) were positive and 176 (47%) were negative. RT-PCR tests took an average of 2.5 days ± 0.7 to provide results. Sensitivity and specificity for correctly identifying COVID-19 with a characteristic chest radiographic pattern was 15.5% (31/200) and 96.6% (170/176), with a positive predictive value and negative predictive value of 83.8% (31/37) and 50.1% (170/339), respectively. CONCLUSION: The presence of patchy and/or confluent, bandlike ground-glass opacity or consolidation in a peripheral and mid to lower lung zone distribution on a chest radiograph obtained in the setting of pandemic COVID-19 was highly suggestive of severe acute respiratory syndrome coronavirus 2 infection and should be used in conjunction with clinical judgment to make a diagnosis.© RSNA, 2020.