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 RT-PCR were compared together and with the final discharge diagnosis used as reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2 and April 24 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographical prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having a COVID-19 pneumonia who underwent within 48 hours both initial chest CT and at least one RT-PCR testing 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 Mann-Whitney Test, Student’s t-test, Chi-square test and Pearson’s correlation. A p value <.05 determined statistical significance. Results Twenty-six of 26 hospital radiology departments responded to the survey with 7500 patients entered; 2652 did not have RT-PCR results or had unknown or excess delay between RT-PCR and CT. After exclusions, 4824 patients (mean age 64, ± 19 yrs, 2669 males) were included. Using final diagnosis as the reference, 2564 of the 4824 patients were positive for COVID-19 (53%). Sensitivity, specificity, NPV and PPV of chest CT for diagnosing COVID-19 were 2319/2564 (90%, 95% confidence interval [CI]: 89, 91), 2056/2260 (91%, 95%CI: 91, 92%), 2056/2300 (89%, 95%CI; 87, 90%) and 2319/2524 (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 suspected COVID-19 patients was successful.

Purpose To determine the impact of the COVID-19 on the CT activities in French radiological centers during the epidemic peak. Materials and methods A cross-sectional prospective CT scan survey was conducted between March 16 and April 12, 2020, in accordance with the local IRB. Seven hundred nine radiology centers were invited to participate in a weekly online survey. Numbers of CT examinations related to COVID-19 including at least chest (CT covid) and whole chest CT scan activities (CT chest) were recorded each week. A sub-analysis on French departments was performed during the 4 weeks of the study. The impact of the number of RT-PCRs (reverse transcriptase polymerase chain reactions) on the CT workflow was tested using two-sample t test and Pearson's test. Results Five hundred seventy-seven structures finally registered (78%) with mean response numbers of 336 ± 18.9 (323; 351). Mean CT chest activity per radiologic structure ranged from 75.8 ± 133 (0-1444) on week 12 to 99.3 ± 138.6 (0-1147) on week 13. Mean ratio of CT covid on CT chest varied from 0.36 to 0.59 on week 12 and week 14 respectively. There was a significant relationship between the number of RT-PCR performed and the number of CT covid (r = 0.73, p = 3.10 −16) but no link with the number of positive RT-PCR results. Conclusion In case of local high density COVID-19, CT workflow is strongly modified and redirected to the management of these specific patients. Key Points • Over the 4-week survey period, 117,686 chest CT (CT total) were performed among the responding centers, including 61,784 (52%) CT performed for COVID-19 (CT covid). • Across the country, the ratio CT covid /CT total varied from 0.36 to 0.59 and depended significantly on the local epidemic density (p = 0.003). • In clinical practice, in a context of growing epidemic, in France, chest CT was used as a surrogate to RT-PCR for patient triage.

Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.

Biotin is thought to improve functional impairment in progressive multiple sclerosis (MS) by upregulating bioenergetic metabolism. We enrolled 19 patients suffering from progressive MS (5 primary and 14 secondary Progressive‐MS). Using cerebral multinuclear magnetic resonance spectroscopy (MMRS) and clinical evaluation before and after 6 months of biotin cure, we showed significant modifications of: PME/PDE, ATP, and lactate resonances; an improvement of EDSS Neuroscore. Our results are consistent with metabolic pathways concerned with biotin action and could suggest the usefulness of MMRS for monitoring.