Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France.

Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using chest CT for COVID-19, results of chest CT and reverse transcription polymerase chain reaction (RT-PCR) assays were compared together and with the final discharge diagnosis used as the reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2, 2020, and April 24, 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographic prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having COVID-19 pneumonia who underwent both initial chest CT and at least one RT-PCR test within 48 hours were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using the Mann-Whitney test, Student t test, χ2 test, and Pearson correlation coefficient. P < .05 indicated a significant difference. Results Twenty-six of 26 hospital radiology departments responded to the survey, with 7500 patients entered; 2652 did not have RT-PCR test results or had unknown or excess delay between the RT-PCR test and CT. After exclusions, 4824 patients (mean age, 64 years ± 19 [standard deviation], 2669 male) were included. With final diagnosis as the reference, 2564 of the 4824 patients had COVID-19 (53%). Sensitivity, specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis of COVID-19 were 2319 of 2564 (90%; 95% CI: 89, 91), 2056 of 2260 (91%; 95% CI: 91, 92), 2056 of 2300 (89%; 95% CI: 87, 90), and 2319 of 2524 (92%; 95% CI: 91, 93), respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of patients suspected of having coronavirus disease 2019 was successful. © RSNA, 2021 Online supplemental material is available for this article.

X-ray-free protocol for pectus deformities based on magnetic resonance imaging and a low-cost portable three-dimensional scanning device: a preliminary study.

OBJECTIVES: To compare a standard protocol using chest computed tomography (CT) to a non-irradiant protocol involving a low-cost portable 3D scanner and magnetic resonance imaging (MRI) for all pectus deformities based on the Haller index (HI). METHODS: From April 2019 to March 2020, all children treated for pectus excavatum or carinatum at our institution were evaluated by chest CT, 3D scanning (iPad with Structure Sensor and Captevia-Rodin4D) and MRI. The main objectives were to compare the HI determined by CT or MRI to a derived index evaluated with 3D scanning, the external Haller index (EHI). The secondary objectives were to assess the inter-rater variability and the concordance between CT and MRI for the HI and the correction index. RESULTS: Eleven patients were evaluated. We identified a strong correlation between the HI with MRI and the EHI (Pearson correlation coefficient = 0.900; P < 0.001), with a strong concordance between a radiologist and a non-radiologist using intra-class correlation for the HI with MRI (intra-class correlation coefficient = 0.995; [0.983; 0.999]) and the EHI (intra-class correlation coefficient = 0.978; [0.823; 0.995]). We also identified a marked correlation between the HI with CT and the EHI (Pearson coefficient = 0.855; P = 0.002), with a strong inter-rater concordance (intra-class correlation coefficient = 0.975; [0.901; 0.993]), a reliable concordance between CT and MRI for the HI and the correction index (Pearson coefficient = 0.886; P = 0.033). CONCLUSIONS: Non-irradiant pectus deformity assessment is possible in clinical practice, replacing CT with MRI and 3D scanning as a possible readily-accessible monitoring tool.

Two-Stage Bronchoscopic Endobronchial Valve Treatment Can Lead to Progressive Lung Volume Reduction and May Decrease Pneumothorax Risk.

Background: Since successful development of endobronchial valves (EBV) as treatment for severe emphysema, its main complication, pneumothorax, remains an important concern. Objective: We hypothesized that a two-step EBV implantation, during two distinct iterative procedures could lead to a more progressive target lobe volume reduction (TLVR) and thus ipsilateral lobe re-expansion, resulting in a significant decrease in the pneumothorax rate. Methods: This retrospective bi-center study carried out by Limoges and Toulouse University Hospitals included patients following the inclusion criteria established by the BLVR expert panel. All patients were treated by two distinct procedures: first, EBVs were placed in all but the most proximal segment or sub-segment. The remaining segment was treated subsequently. All patients had a complete evaluation before treatment, and 3 months after the second procedure. Results: Out of 58 patients included, only 4 pneumothoraxes (7%) occurred during the study. The other complications were pneumonia and severe COPD exacerbation (8.6% and 13.7% of patients, respectively). Significant improvement was found for FEV1 (+19.6 ± 25%), RV (-468 ± 960mL), 6MWD (30 ± 85m), BODE Index (-1.4 ± 1.8 point) and TLVR (50.6 ± 35.1%). Significant TLVR (MCID) was obtained in 74.1% of patients (43/58). Conclusion: This new approach using EBV could reduce the incidence of pneumothorax without increasing other complication rates. Clinical and physiological outcomes are similar to those reported in studies using the conventional single-step treatment.

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN). MATERIALS AND METHODS: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. RESULTS: The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. CONCLUSION: The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.

Three artificial intelligence data challenges based on CT and ultrasound.

PURPOSE: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers. MATERIALS AND METHODS: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data. RESULTS: Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications).