The Impact of Tocilizumab on Radiological Changes Assessed by Quantitative Chest CT in Severe COVID-19 Patients.

(1) Background: We aimed to analyze the characteristics associated with the in-hospital mortality, describe the early CT changes expressed quantitatively after tocilizumab (TOC), and assess TOC timing according to the oxygen demands. (2) Methods: We retrospectively studied 101 adult patients with severe COVID-19, who received TOC and dexamethasone. The lung involvement was assessed quantitatively using native CT examination before and 7-10 days after TOC administration. (3) Results: The in-hospital mortality was 17.8%. Logistic regression analysis found that interstitial lesions above 50% were associated with death (p = 0.01). The other variables assessed were age (p = 0.1), the presence of comorbidities (p = 0.9), the oxygen flow rate at TOC administration (p = 0.2), FiO2 (p = 0.4), lymphocyte count (p = 0.3), and D-dimers level (p = 0.2). Survivors had a statistically significant improvement at 7-10 days after TOC of interstitial (39.5 vs. 31.6%, p < 0.001), mixt (4.3 vs. 2.3%, p = 0.001) and consolidating (1.7 vs. 1.1%, p = 0.001) lesions. When TOC was administered at a FiO2 ≤ 57.5% (oxygen flow rate ≤ 13 L/min), the associated mortality was significantly lower (4.3% vs. 29.1%, p < 0.05). (4) Conclusions: Quantitative imaging provides valuable information regarding the extent of lung damage which can be used to anticipate the in-hospital mortality. The timing of TOC administration is important and FiO2 could be used as a clinical predictor.

COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis.

Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (

Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients.

INTRODUCTION: COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. MATERIALS AND METHODS: From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. RESULTS: Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 ± 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 ± 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 ± 0.26L vs 0.43 ± 0.23L, p = 0.0005) and fibrotic alterations (0.05 ± 0.03 L vs 0.04 ± 0.03 L, p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p < 0.001), respectively. CONCLUSIONS: Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.

Quantitative CT Extent of Lung Damage in COVID-19 Pneumonia Is an Independent Risk Factor for Inpatient Mortality in a Population of Cancer Patients: A Prospective Study.

Background: CT lung extent has emerged as a potential risk factor of COVID-19 pneumonia severity with mainly semiquantitative assessment, and outcome was not assessed in the specific oncology setting. The main goal was to evaluate the prognostic role of quantitative assessment of the extent of lung damage for early mortality of patients with COVID-19 pneumonia in cancer patients. Methods: We prospectively included consecutive cancer patients with recent onset of COVID-19 pneumonia assessed by chest CT between March 15, 2020, and April 20, 2020, and followed until May 1, 2020. Demographic, clinical, laboratory test data and imaging findings were recorded. Quantitative chest CT assessment of COVID-19 pneumonia was based on the density distribution of lung lesions using a freely available software recently released (Myrian XP-Lung). The association between extent of lung damage and overall survival was studied by univariate and multivariate Cox analysis. The Uno C-index was used to assess the discriminatory value of the quantitative CT extent of lung damage. Results: Seventy cancer patients with chest CT evidence of COVID-19 were included. After a median follow-up of 25 days, 17 patients (24%) had died. The median quantitative chest CT extent of COVID-19 was 20% (IQR = 14-35, range = 3-59) for non-survivors vs. 10% (IQR = 6-15, range = 2-55) for survivors (p = 0.002). The extent of COVID-19 pneumonia was correlated with inpatient management (p = 0.003) and oxygen therapy requirements (p < 0.001). Independent factors associated with death were performance status (PS) ≥2 (HR = 3.9, 95% CI = [1.1-13.8] p = 0.04) and extent of COVID-19 pneumonia ≥30% (HR = 12.0, 95% CI = [2.2-64.4] p = 0.004). No differences were found regarding the histology of cancer, cancer stage, metastases sites, or type of oncologic treatment between the survivor and non-survivor groups. The cross-validated Uno C-index of the model including PS and extent of COVID-19 pneumonia was 0.83, 95% CI = [0.73-0.93]. Conclusions: The quantitative chest CT extent of COVID-19 pneumonia was a strong independent prognostic factor of early inpatient mortality in a population of cancer patients.