Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool.

PURPOSE: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. MATERIALS AND METHODS: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. RESULTS: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8-21 days (after hospital admission) was an "advanced period" with the most severe lung disease involvement. After the extent of involvement started to decrease-particularly after 21 days-the absorption was more obvious. CONCLUSIONS: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.

Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software.

PURPOSE: To compare different commercial software in the quantification of Pneumonia Lesions in COVID-19 infection and to stratify the patients based on the disease severity using on chest computed tomography (CT) images. MATERIALS AND METHODS: We retrospectively examined 162 patients with confirmed COVID-19 infection by reverse transcriptase-polymerase chain reaction (RT-PCR) test. All cases were evaluated separately by radiologists (visually) and by using three computer software programs: (1) Thoracic VCAR software, GE Healthcare, United States; (2) Myrian, Intrasense, France; (3) InferRead, InferVision Europe, Wiesbaden, Germany. The degree of lesions was visually scored by the radiologist using a score on 5 levels (none, mild, moderate, severe, and critic). The parameters obtained using the computer tools included healthy residual lung parenchyma, ground-glass opacity area, and consolidation volume. Intraclass coefficient (ICC), Spearman correlation analysis, and non-parametric tests were performed. RESULTS: Thoracic VCAR software was not able to perform volumes segmentation in 26/162 (16.0%) cases, Myrian software in 12/162 (7.4%) patients while InferRead software in 61/162 (37.7%) patients. A great variability (ICC ranged for 0.17 to 0.51) was detected among the quantitative measurements of the residual healthy lung parenchyma volume, GGO, and consolidations volumes calculated by different computer tools. The overall radiological severity score was moderately correlated with the residual healthy lung parenchyma volume obtained by ThoracicVCAR or Myrian software, with the GGO area obtained by the ThoracicVCAR tool and with consolidation volume obtained by Myrian software. Quantified volumes by InferRead software had a low correlation with the overall radiological severity score. CONCLUSIONS: Computer-aided pneumonia quantification could be an easy and feasible way to stratify COVID-19 cases according to severity; however, a great variability among quantitative measurements provided by computer tools should be considered.

Unsupervised quantification of tissue immunofluorescence in animal models of multiple sclerosis - Instructions for use.

BACKGROUND: In the analysis of animal models of CNS diseases such as experimental autoimmune encephalomyelitis (EAE), immunostaining and histopathology are important readouts. However, the complex morphological features of a tissue staining are often reduced to a single measure which relies on tedious manual planimetry. Furthermore, the measure itself and co-variables such as the region being analysed are chosen in a human decision-making process, which introduces bias. NEW METHOD: First aim of the present study is to provide an open-source workflow for the high-throughput, unsupervised quantification of different stainings in the spinal cord. We evaluate different EAE models, spinal cord regions and different time points of disease. By applying random forest classification, we compare different measures. RESULTS: Exemplified for glial reactivity, we show that measures and variables interact and that their values are non-normally distributed, hampering the common use of parametric tests. Furthermore, we demonstrate that one-dimensional measures are insufficient descriptors for immunofluorescence data in EAE and thus need to be considered as partly invalid. COMPARISON WITH EXISTING METHODS: We show in a systematic analysis of EAE studies that currently published immunohistological outcomes are highly incompatible regarding methodology and statistics. Furthermore, they lack the report of important information necessary for reproducibility and do not use unsupervised automatic analysis. CONCLUSIONS: Our results discover relevant caveats in the currently used methods of immunofluorescence analysis. The provided step-by-step instructions and open-source code are intended to serve as a framework for sensitive, unbiased immunofluorescence analysis of tissue sections in translational research.