Reporting of Magnetic Resonance Enterography in Inflammatory Bowel Disease: Results of an Italian Survey.

Background/Objectives: Inflammatory bowel diseases (IBDs) are chronic disorders that require close monitoring with imaging techniques such as magnetic resonance enterography (MRE). Standardization of radiological reports is crucial for the optimal management of IBD. We surveyed Italian radiologists regarding their experiences with MRE examinations and reporting for IBD. Methods: All members of the Italian Society of Medical and Interventional Radiology (SIRM) were invited to complete an anonymous questionnaire in April 2023. Comparison tests between variables were assessed using the χ2 test or Fisher exact test according to the least frequency group. Significance level was set for p-value < 0.05. Results: A total of 253 radiologists responded to the survey. Around 70% of the respondents declared personal clinical experience with IBD. Great agreement with the items included and described for both disease activity (i.e., intestinal wall thickness, presence of mucosal ulcers, presence of edema, mucous enhancement) and complications was reported. One-third of the respondents regularly used a structured MRE report. Centers with a high number of IBD patients per year (>1000) mostly used 3 T scanners or both 1.5 T and 3 T scanners (p < 0.001). The incorporation of scores of disease activity was associated with university and high-volume hospitals (p < 0.001). Conclusions: This survey highlighted the current routine practice and experience of MRE reports of IBD patients among Italian radiologists. We found deficiencies in the use of radiological scores in MRE reports and attendance at IBD multidisciplinary meetings.

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

The gut-brain axis: Correlation of choroid plexus volume and permeability with inflammatory biomarkers in Crohn's disease.

BACKGROUND: The dysregulation of the gut-brain axis in chronic inflammatory bowel diseases can cause neuro-psychological disturbances, but the underlying mechanisms are still not fully understood. The choroid plexus (CP) maintains brain homeostasis and nourishment through the secretion and clearance of cerebrospinal fluid. Recent research has demonstrated the existence of a CP vascular barrier in mice which is modulated during intestinal inflammation. This study investigates possible correlations between CP modifications and inflammatory activity in patients with Crohn's disease (CD). METHODS: In this prospective study, 17 patients with CD underwent concomitant abdominal and brain 3 T MRI. The volume and permeability of CP were compared with levels of C-reactive protein (CRP), fecal calprotectin (FC), sMARIA and SES-CD scores. RESULTS: The CP volume was negatively correlated with CRP levels (R = -0.643, p-value = 0.024) and FC (R = -0.571, p-value = 0.050). DCE metrics normalized by CP volume were positively correlated with CRP (K-trans: R = 0.587, p-value = 0.045; Vp: R = 0.706, p-value = 0.010; T1: R = 0.699, p-value = 0.011), and FC (Vp: R = 0.606, p-value = 0.037). CONCLUSIONS: Inflammatory activity in patients with CD is associated with changes in CP volume and permeability, thus supporting the hypothesis that intestinal inflammation could affect the brain through the modulation of CP vascular barrier also in humans.

Quantification of cauda equina nerve root dispersion through radiomics features in weight-bearing MRI in normal subjects and spinal canal stenosis patients.

OBJECTIVE: To quantify the distribution of cauda equina nerve roots in supine and upright positions using manual measurements and radiomics features both in normal subjects and in lumbar spinal canal stenosis (LSCS) patients. METHODS: We retrospectively recruited patients who underwent weight-bearing MRI in supine and upright positions for back pain. 3D T2-weighted isotropic acquisition (3D-HYCE) sequences were used to develop a 3D convolutional neural network for identification and segmentation of lumbar vertebrae. Para-axial reformatted images perpendicular to the spinal canal and parallel to each vertebral endplate were automatically extracted. From each level, we computed the maximum antero-posterior (AP) and latero-lateral (LL) dispersion of nerve roots; further, radiomics features were extracted to quantify standardized metrics of nerve root distribution. RESULTS: We included 16 patients with LSCS and 20 normal subjects. In normal subjects, nerve root AP dispersion significantly increased from supine to upright position (p < 0.001, L2-L5 levels), and radiomics features showed an increase in non-uniformity. In LSCS subjects, in the upright position AP dispersion of nerve roots and entropy-related features increased caudally to the stenosis level (p < 0.001) and decreased cranially (p < 0.001). Moreover, entropy-related radiomics features negatively correlated with pre-operative Pain Numerical Rating Scale. Comparison between normal subjects and LSCS patients showed a difference in AP dispersion and increase of variance cranially to the stenosis level (p < 0.001) in the upright position. CONCLUSIONS: Nerve root distribution inside the dural sac changed between supine and upright positions, and radiomics features were able to quantify the differences between normal and LSCS subjects. CLINICAL RELEVANCE STATEMENT: The distribution of cauda equina nerve roots and the redundant nerve root sign significantly varies between supine and upright positions in normal subjects and spinal canal stenosis patients, respectively. Radiomics features quantify nerve root dispersion and correlates with pain severity. KEY POINTS: • Weight-bearing MRI depicts spatial distribution of the cauda equina in both supine and upright positions in normal subjects and spinal stenosis patients. • Radiomics features can quantify the effects of spinal stenosis on the dispersion of the cauda equina in the dural sac. • In the orthostatic position, dispersion of nerve roots is different in lumbar spinal stenosis patients compared to that in normal subjects; entropy-related features negatively correlated with pre-operative Pain Numerical Rating Scale.

Association Between Duration of SARS-CoV-2 Positivity and Long COVID.

In an observational study, we analyzed 1293 healthcare workers previously infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), of which 34.1% developed postacute sequelae of SARS-CoV-2 infection (also known as long COVID). Using a multivariate logistic regression model, we demonstrate that the likelihood of developing long COVID in infected individuals rises with the increasing of duration of infection and that 3 doses of the BNT162b2 vaccine are protective, even during the Omicron wave.

CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex.

PURPOSE: Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. MATERIALS AND METHODS: We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. RESULTS: The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years). CONCLUSION: Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.

Efficacy of Six Different SARS-CoV-2 Vaccines during a Six-Month Follow-Up and Five COVID-19 Waves in Brazil and Mexico.

Comparisons among the different vaccines against SARS-CoV-2 are important to understand which type of vaccine provides more protection. This study aimed to evaluate the real-life efficacy through symptomatic infection and the humoral response of six different vaccines against SARS-CoV-2-BNT162b2, mRNA-1273, ChAdOx1-S, CoronaVac, Ad26.COV2, and Ad5-nCoV. This multicentric observational longitudinal study involved hospitals from Mexico and Brazil in which volunteers who received complete vaccination schemes were followed for 210 days after the last dose. SARS-CoV-2 Spike 1-2 IgG levels were taken before receiving the first vaccine, 21 days after each dose, and the last sample at six months (+/-1 month) after the last dose. A total of 1132 individuals exposed to five COVID-19 waves were included. All vaccines induced humoral responses, and mRNA vaccines had the highest antibody levels during follow-up. At six months, there was a decline in the SARS-CoV-2 Spike 1-2 IgG antibody titers of 69.5% and 36.4% in subjects with negative and positive history of infection respectively. Infection before vaccination and after complete vaccination scheme correlated with higher antibody titers. The predictors of infection were vaccination with CoronaVac compared to BNT162b2 and ChAdOx1-S. In the presence of comorbidities such as diabetes, rheumatoid arthritis, or dyslipidemia, CoronaVac lowered the risk of infection.

Editorial comment: Radiogenomics of glioblastoma: shifting the focus from tumor cells to immune microenvironment.

KEY POINTS: • The ICI score derived from gene expression profile of immune cells infiltrating GBM correlates with overall survival and is an effective prognostic biomarker.• In this study, the authors developed a radiomics-based machine learning model able to identify gene expression profiles of GBM intratumoral stromal and immune cells and predict the ICI score on the preoperative MRI scans with high accuracy.• Radiogenomics could potentially be applied in primary brain tumors to noninvasively assess the specific tumor immune characteristics, predict patients' prognosis and identify those patients with higher probability to respond to immunotherapy.

Antibody Titer Correlates with Omicron Infection in Vaccinated Healthcare Workers.

The advent of vaccines against SARS-CoV-2 has drastically reduced the level of hospitalization with severe COVID-19 disease in infected individuals. However, the diffusion of variants of concern still challenge the protection conferred by vaccines raised against the wild-type form of the virus. Here, we have characterized the antibody response to the BNT162b2 (Comirnaty) mRNA vaccine in patients infected with the Omicron variant. We analyzed a population of 4354 vaccinated healthcare workers (HCW) from 7 different hospitals in Italy and monitored infection with SARS-CoV-2 Omicron. We correlated infection with the antibody response after vaccination. We found that a lower level of IgG, younger age, and the presence of allergies correlate with increased infection during the Omicron wave, and that infections correlate with wild-type spike protein antibody titers below 350 BAU/mL. These results support the necessity of a fourth booster dose, particularly for individuals with lower levels of antibodies.

Analysis of immunization time, amplitude, and adverse events of seven different vaccines against SARS-CoV-2 across four different countries.

Background: Scarce information exists in relation to the comparison of seroconversion and adverse events following immunization (AEFI) with different SARS-CoV-2 vaccines. Our aim was to correlate the magnitude of the antibody response to vaccination with previous clinical conditions and AEFI. Methods: A multicentric comparative study where SARS-CoV-2 spike 1-2 IgG antibodies IgG titers were measured at baseline, 21-28 days after the first and second dose (when applicable) of the following vaccines: BNT162b2 mRNA, mRNA-1273, Gam-COVID-Vac, Coronavac, ChAdOx1-S, Ad5-nCoV and Ad26.COV2. Mixed model and Poisson generalized linear models were performed. Results: We recruited 1867 individuals [52 (SD 16.8) years old, 52% men]. All vaccines enhanced anti-S1 and anti-S2 IgG antibodies over time (p<0.01). The highest increase after the first and second dose was observed in mRNA-1273 (p<0.001). There was an effect of previous SARS-CoV-2 infection; and an interaction of age with previous SARS-CoV-2 infection, Gam-COVID-Vac and ChAdOx1-S (p<0.01). There was a negative correlation of Severe or Systemic AEFI (AEs) of naïve SARS-CoV-2 subjects with age and sex (p<0.001); a positive interaction between the delta of antibodies with Gam-COVID-Vac (p=0.002). Coronavac, Gam-COVID-Vac and ChAdOx1-S had less AEs compared to BNT162b (p<0.01). mRNA-1273 had the highest number of AEFIs. The delta of the antibodies showed an association with AEFIs in previously infected individuals (p<0.001). Conclusions: The magnitude of seroconversion is predicted by age, vaccine type and SARS-CoV-2 exposure. AEs are correlated with age, sex, and vaccine type. The delta of the antibody response only correlates with AEs in patients previously exposed to SARS-CoV-2. Registration number: ClinicalTrials.gov, identifier NCT05228912.

A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019.

BACKGROUND AND AIMS: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. METHODS: Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. RESULTS: We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. CONCLUSION: This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

Bayesian supervised machine learning classification of neural networks with pathological perturbations.

Objective.Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of humanin vitroneural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform.Approach.We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbedin vitroneural networks.Main Results.We found a high degree of separability between the classes using DM point process features (p-value <0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (43 ± 16 ms versus 67 ± 31 ms,µIGfeature distribution).Significance.Our approach has been successful in extracting temporal features related to the neurons' behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation.

One dose of SARS-CoV-2 vaccine exponentially increases antibodies in individuals who have recovered from symptomatic COVID-19.

BACKGROUNDThe COVID-19 vaccines currently in use require 2 doses to achieve optimal protection. Currently, there is no indication as to whether individuals who have been exposed to SARS-CoV-2 should be vaccinated, or whether they should receive 1 or 2 vaccine doses.METHODSWe tested the antibody response developed after administration of the Pfizer/BioNTech vaccine in 124 health care professionals, of whom 57 had a previous history of SARS-CoV-2 exposure with or without symptoms.RESULTSPostvaccine antibodies in SARS-CoV-2-exposed individuals increased exponentially within 5 to 18 days after the first dose compared to naive subjects (P < 0.0001). In a multivariate linear regression (LR) model we showed that the antibody response depended on the IgG prevaccine titer and on the exposure to SARS-CoV-2. In symptomatic SARS-CoV-2-exposed individuals, IgG reached a plateau after the second dose, and those who voluntarily refrained from receiving the second dose (n = 7) retained their antibody response. Gastrointestinal symptoms, muscle pain, and fever markedly positively correlated with increased IgG responses. By contrast, all asymptomatic/paucisymptomatic and unexposed individuals showed an important increase after the second dose.CONCLUSIONOne vaccine dose is sufficient in symptomatic SARS-CoV-2-exposed subjects to reach a high titer of antibodies, suggesting no need for a second dose, particularly in light of current vaccine shortage.TRIAL REGISTRATIONClinicalTrials.gov NCT04387929.FUNDINGDolce & Gabbana and the Italian Ministry of Health (Ricerca corrente).

Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.

OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU. METHODS: A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates. RESULTS: The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all. CONCLUSIONS: The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients.

BACKGROUND: Persistence of antibodies to SARS-CoV-2 viral infection may depend on several factors and may be related to the severity of disease or to the different symptoms. METHODS: We evaluated the antibody response to SARS-CoV-2 in personnel from 9 healthcare facilities and an international medical school and its association with individuals' characteristics and COVID-19 symptoms in an observational cohort study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. RESULTS: Here we show in individuals positive for IgG (≥12 AU/mL) at the beginning of the study an increase [p = 0.0002] in antibody response in paucisymptomatic or symptomatic subjects, particularly with loss of taste or smell (anosmia/dysgeusia: OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis in the first three months. The antibody response persists for at least 8-10 months. CONCLUSIONS: SARS-CoV-2 infection induces a long lasting antibody response that increases in the first months, particularly in individuals with anosmia/dysgeusia. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb.