COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Serum proteomics profiling identifies a preliminary signature for the diagnosis of early-stage lung cancer.

PURPOSE: Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival. EXPERIMENTAL DESIGN: The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach. RESULTS: We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors. CONCLUSIONS AND CLINICAL RELEVANCE: Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary "Real Life" Results.

The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists' 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.

Prevention of recurrent upper respiratory tract infections in a community of cloistered nuns using a new immunostimulating bacterial lysate. A randomized, double-blind clinical trial.

UNLABELLED: The aim of the present study was to evaluate the efficacy of the immunostimulating therapy with a new vaccine Ismigen in preventing recurrent infections of the upper respiratory tract in subjects belonging to a community of cloistered nuns. This product is a lysate obtained by mechanical lysis of bacteria (MLBL) usually responsible of respiratory tract infections. A randomized, double-blind, parallel groups, placebo controlled clinical trial was carried out in 47 nuns (age range 25-80 years) living in a cloistered religious community, suffering from recurrent infections of the upper respiratory tract. The 47 patients were allocated by randomization to two groups: Group A--The 24 patients (mean age SD: 48.12 +/- 14.25 years) of this group received one sublingual tablet of MLBL per day, for 10 consecutive days per month for 3 consecutive months (October, November and December 2001). Group B--The 23 patients (mean age +/- SD: 49.04 +/- 14.73 years) of this group received daily one sublingual tablet of taste masked placebo, for 10 consecutive days per month for 3 consecutive months (October, November and December 2001). At the end of the treatment period patients of both groups were followed up for further three months without any immunostimulating treatment. RESULTS: During and at the end of the treatment phase the number of respiratory infections (primary end-point) and their duration were statistically significantly lower in the MLBL group than in the placebo group. Moreover the administration of MLBL induced a marked reduction in the number of patients showing symptoms of infection in comparison to baseline and approximately 79 % of the patients showed an improvement of one or more of the evaluated symptoms. In the MLBL group a statistically significant increase of serum immunoglobulins (IgG +35%; IgM +86%; IgA +80 %) and salivary IgA (+110%) was found, in comparison to baseline; on the contrary no significant differences were observed in the placebo group. The beneficial effects of MLBL found in the treatment period were maintained also in the three-month follow-up. No adverse events associated with the treatment were found in both group. The results of this study demonstrate that MLBL is an efficacious and safe therapeutic option for the treatment and prevention of recurrent upper respiratory tract infections and that its use is recommended in subjects with a possible immune deficit.