proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decisionmaking, from oncology and respiratory medicine to pharmacological and genotyping studies.

Word count: 2973All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Key points:Question How do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID- patients at hospital admission perform?Findings This model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244.Meaning The findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Background Asthmatics and COPD patients have more severe outcomes with viral infections than people without obstructive disease. Objective To evaluate if obstructive diseases are risk factors for ICU stay and death due to COVID19. Methods We collected data from the electronic medical record from 596 adult patients hospitalized in University hospital of Liege between 18 th of March and 17th of April 2020 for SARS-CoV2 infection. We classified patients in three groups according to the underlying respiratory disease, present prior to COVID19 pandemics. Results Among patients requiring hospitalization for COVID19, asthma and COPD accounted for 9.6% and 7.7% respectively. The proportions of asthmatics, COPD and patients without obstructive airway disease hospitalized in ICU were 17.5%, 19.6% and 14% respectively. One third of COPD patients died during hospitalization while only 7.0% of asthmatics and 13.6% of patients without airway obstruction died due to SARS-CoV2. The multivariate analysis showed that asthma, COPD, ICS treatment and OCS treatment were not independent risk factors for ICU admission or death. Male gender (OR:1.9; 95%CI: 1.1 to 3.2) and obesity (OR:8.5; 95%CI: 5.1 to 14.1) were predictors of ICU admission while male gender (OR1.9; 95%CI: 1.1-3.2), older age (OR:1.9; 95%CI: 1.6-2.3), cardiopathy (OR: 1.8; 95%CI: 1.1-3.1) and immunosuppressive diseases (OR: 3.6; 95%CI: 1.5-8.4) were independent predictors of death. Conclusion Asthma and COPD are not risk factors for ICU admission and death related to SARS-CoV2 infection.

Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.

Retrospective studies showed a relationship between vitamin D status and COVID-19 severity and mortality, with an inverse relation between SARS-CoV-2 positivity and circulating calcifediol levels. The objective of this pilot study was to investigate the effect of vitamin D supplementation on the length of hospital stay and clinical improvement in patients with vitamin D deficiency hospitalized with COVID-19. The study was randomized, double blind and placebo controlled. A total of 50 subjects were enrolled and received, in addition to the best available COVID therapy, either vitamin D (25,000 IU per day over 4 consecutive days, followed by 25,000 IU per week up to 6 weeks) or placebo. The length of hospital stay decreased significantly in the vitamin D group compared to the placebo group (4 days vs. 8 days; p = 0.003). At Day 7, a significantly lower percentage of patients were still hospitalized in the vitamin D group compared to the placebo group (19% vs. 54%; p = 0.0161), and none of the patients treated with vitamin D were hospitalized after 21 days compared to 14% of the patients treated with placebo. Vitamin D significantly reduced the duration of supplemental oxygen among the patients who needed it (4 days vs. 7 days in the placebo group; p = 0.012) and significantly improved the clinical recovery of the patients, as assessed by the WHO scale (p = 0.0048). In conclusion, this study demonstrated that the clinical outcome of COVID-19 patients requiring hospitalization was improved by administration of vitamin D.

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Background The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits.Objectives To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. MethodsIn this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liège, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity.Results 1381 patients were included in this study. The average age was 64.4±15.8 and 63.8±14.4 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Introduction Patients with interstitial lung diseases (ILD) can be suspected to be at risk of experiencing a rapid flare-up due to COVID-19. However, no specific data are currently available for these patients. Methods We retrospectively analyzed a cohort of 401 patients with ILD and determined the proportion of patients hospitalized for proven severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and specific symptoms of COVID-19. Results We found that 1% of patients (n = 4) were hospitalized (1 in ICU) for COVID-19. In total, 310 of the 401 patients answered the phone call. Only 33 patients (0.08%) experienced specific symptoms of SARS-CoV-2 infection. Conclusion Our study did not demonstrate any increased occurrence of severe COVID-19 in ILD patients compared to the global population. Based on our findings, we could not make any conclusion on the incidence rate of SARS-CoV-2 infection in patients with ILDs, or on the overall outcome of immunocompromised patients affected by COVID-19.