Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort.

Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions' semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.

The accuracy of teleradiologists in diagnosing COVID-19 based on a French multicentric emergency cohort.

OBJECTIVES: To evaluate the accuracy of diagnoses of COVID-19 based on chest CT as well as inter-observer agreement between teleradiologists during on-call duty and senior radiologists in suspected COVID-19 patients. MATERIALS AND METHODS: From March 13, 2020, to April 14, 2020, consecutive suspected COVID-19 adult patients who underwent both an RT-PCR test and chest CT from 15 hospitals were included in this prospective study. Chest CTs were immediately interpreted by the on-call teleradiologist and were systematically blind reviewed by a senior radiologist. Readings were categorised using a five-point scale: (1) normal; (2) non-infectious findings; (3) infectious findings but not consistent with COVID-19 infection; (4) consistent with COVID-19 infection; and (5) typical appearance of COVID-19 infection. The diagnostic accuracy of chest CT and inter-observer agreement using the kappa coefficient were evaluated over the study period. RESULTS: In total, 513 patients were enrolled, of whom 244/513 (47.6%) tested positive for RT-PCR. First readings were scored 4 or 5 in 225/244 (92%) RT-PCR+ patients, and between 1 and 3 in 201/269 (74.7%) RT-PCR- patients. The data were highly consistent (weighted kappa = 0.87) and correlated with RT-PCR (p < 0.001, AUC1st-reading = 0.89, AUC2nd-reading = 0.93). The negative predictive value for scores of 4 or 5 was 0.91-0.92, and the PPV for a score of 5 was 0.89-0.96 at the first and second readings, respectively. Diagnostic accuracy was consistent over the study period, irrespective of a variable prevalence rate. CONCLUSION: Chest CT demonstrated high diagnostic accuracy with strong inter-observer agreement between on-call teleradiologists with varying degrees of experience and senior radiologists over the study period. KEY POINTS: • The accuracy of readings by on-call teleradiologists, relative to second readings by senior radiologists, demonstrated a sensitivity of 0.75-0.79, specificity of 0.92-0.97, NPV of 0.80-0.83, and PPV of 0.89-0.96, based on "typical appearance," as predictive of RT-PCR+. • Inter-observer agreement between the first reading in the emergency setting and the second reading by the senior emergency teleradiologist was excellent (weighted kappa = 0.87).

MRI of Reperfused Acute Myocardial Infarction Edema: ADC Quantification versus T1 and T2 Mapping.

Background After acute myocardial infarction (AMI), reperfusion injury is associated with microvascular lesions and myocardial edema. Purpose To evaluate the performance of apparent diffusion coefficient (ADC) quantification compared with T1 and T2 values in the detection of acute myocardial injury. Materials and Methods In this prospective study conducted from June 2016 to November 2018, participants without a history of heart failure or cardiomyopathy were enrolled after undergoing reperfusion for their first AMI. Quantitative T1 and T2 mapping were performed with a 1.5-T MRI scanner and compared with a fast free-breathing acquisition technique for ADC mapping (approximate duration, 3 minutes; five slices; spin-echo cardiac diffusion acquisition; b values, 0 and 200 sec/mm2; six diffusion-encoding directions; five repetitions). Quantitative ADC and unenhanced T1 and T2 values were compared in infarct, border, and remote regions by using Welch analysis of variance with Games-Howell post hoc test for pairwise comparisons. Results Thirty-four participants with AMI underwent MRI an average of 5 days ± 1.9 (standard deviation) after reperfusion. Mean ADC was markedly high in the infarcted regions (2.32 × 10-3 mm2/sec; 95% confidence interval [CI]: 2.28, 2.36) and moderately high in the border regions (1.91 ×10-3 mm2/sec; 95% CI: 1.89, 1.94; P < .001). In remote regions, mean ADC (1.62 ×10-3 mm2/sec; 95% CI: 1.59, 1.64) was comparable to that measured in vivo in healthy volunteers. Within the same regions of interest, although the measures showed similar trends in infarct and remote regions for T1 (mean, 1332 mec [95% CI: 1296, 1368] vs 1045 msec [95% CI: 1034, 1056]; P < .001) and T2 (72 msec [95% CI: 69, 75] vs 50 msec [95% CI: 49, 51]; P < .001), the magnitude of the differences among regions was greater when using ADC. Normalized signal differences between infarct and remote regions showed that diffusion-weighted MRI depicted edema 5.1 (P < .001) and 3.5 (P < .001) times greater than did T1 and T2 maps, respectively. Conclusion Multislice cardiac diffusion-weighted images could be acquired in those with acute myocardial injury. Quantitative apparent diffusion coefficient mapping showed greater differences among remote regions and lesions than did T1 or T2 mapping. © RSNA, 2020 See also the editorial by Lloyd and Farris in this issue.