Mid-term Follow-Up chest CT findings in recovered COVID-19 patients with residual symptoms.

OBJECTIVES: More than a year has passed since the initial outbreak of SARS-CoV-2, which caused many hospitalizations worldwide due to COVID-19 pneumonia and its complications. However, there is still a lack of information detailing short- and long-term outcomes of previously hospitalized patients. The purpose of this study is to analyze the most frequent lung CT findings in recovered COVID-19 patients at mid-term follow-ups. METHODS: A total of 407 consecutive COVID-19 patients who were admitted to the XXXX and discharged between February 27, 2020, and June 26, 2020 were recruited into this study. Out of these patients, a subset of 108 patients who presented with residual asthenia and dyspnea at discharge, altered spirometric data, positive lung ultrasound and positive chest X-ray was subsequently selected, and was scheduled to undergo a mid-term chest computer tomography study, which was evaluated for specific lung alterations and morphological patterns. RESULTS: The most frequently observed lung CT alterations, in order of frequency, were ground glass opacities (81%), linear opacities (74%), bronchiolectases (64,81%), and reticular opacities (63,88%). The most common morphological pattern was the nonspecific interstitial pneumonia pattern (63,88%). Features consistent with pulmonary fibrosis were observed in 32 patients (29,62%). CONCLUSIONS: Our work showed that recovered COVID-19 patients that were hospitalized and that exhibited residual symptoms after discharge had a slow radiological recovery with persistent residual lung alterations. ADVANCES IN KNOWLEDGE: This slow recovery process should be kept in mind when determining the follow-up phases in order to improve the long-term management of patients affected by COVID-19.

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

OBJECTIVES: While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR. METHODS: A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases. RESULTS: RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001). CONCLUSION: An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR. KEY POINTS: • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: a survey of Italian radiologists.

PURPOSE: Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. MATERIAL AND METHODS: The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). RESULTS: The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. CONCLUSION: This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.

Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms.

OBJECTIVE: To analyze the most frequent radiographic features of COVID-19 pneumonia and assess the effectiveness of chest X-ray (CXR) in detecting pulmonary alterations. MATERIALS AND METHODS: CXR of 240 symptomatic patients (70% male, mean age 65 ± 16 years), with SARS-CoV-2 infection confirmed by RT-PCR, was retrospectively evaluated. Patients were clustered in four groups based on the number of days between symptom onset and CXR: group A (0-2 days), 49 patients; group B (3-5), 75 patients; group C (6-9), 85 patients; and group D (> 9), 31 patients. Alteration's type (reticular/ground-glass opacity (GGO)/consolidation) and distribution (bilateral/unilateral, upper/middle/lower fields, peripheral/central) were noted. Statistical significance was tested using chi-square test. RESULTS: Among 240 patients who underwent CXR, 180 (75%) showed alterations (group A, 63.3%; group B, 72%; group C, 81.2%; group D, 83.9%). GGO was observed in 124/180 patients (68.8%), reticular alteration in 113/180 (62.7%), and consolidation in 71/180 (39.4%). Consolidation was significantly less frequent (p < 0.01). Distribution among groups was as follows: reticular alteration (group A, 70.9%; group B, 72.2%; group C, 57.9%; group D, 46.1%), GGO (group A, 67.7%; group B, 62.9%; group C, 71%; group D, 76.9%), and consolidation (group A, 35.5%; group B, 31.4%; group C, 47.8%; group D, 38.5%). Alterations were bilateral in 73.3%. Upper, middle, and lower fields were involved in 36.7%, 79.4%, and 87.8%, respectively. Lesions were peripheral in 49.4%, central in 11.1%, or both in 39.4%. Upper fields and central zones were significantly less involved (p < 0.01). CONCLUSIONS: The most frequent lesions in COVID-19 patients were GGO (intermediate/late phase) and reticular alteration (early phase) while consolidation gradually increased over time. The most frequent distribution was bilateral, peripheral, and with middle/lower predominance. Overall rate of negative CXR was 25%, which progressively decreased over time. KEY POINTS: • The predominant lung changes were GGO and reticular alteration, while consolidation was less frequent. • The typical distribution pattern was bilateral, peripheral, or both peripheral and central and involved predominantly the lower and middle fields. • Chest radiography showed lung abnormalities in 75% of patients with confirmed SARS-CoV-2 infection, range varied from 63.3 to 83.9%, respectively, at 0-2 days and > 9 days from the onset of symptoms.