Project description:BackgroundThe role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.MethodsLungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.ResultsDespite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.ConclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.Key pointsWe conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Project description:2 normal lung parenchyma SAGE libraries, generated from 2 pools of 4 individuals each Discussed in the study: IDENTIFICATION OF NOVEL LUNG GENES IN BRONCHIAL EPITHELIUM BY SERIAL ANALYSIS OF GENE EXPRESSION Kim M. Lonergan1, Raj Chari1, Ronald J. deLeeuw1, Ashleen Shadeo1, Bryan Chi1, Ming-Sound Tsao2, Steven Jones3, Marco Marra3, Victor Ling1, Raymond Ng1,4, Calum MacAulay5, Stephen Lam5 and Wan L. Lam1 From the 1Department of Cancer Genetics & Developmental Biology, 5Department of Cancer Imaging, 3Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, BC, Canada, 2Ontario Cancer Institute / Princess Margaret Hospital, Toronto, ON, Canada, 4the Department of Computer Science, University of British Columbia, Vancouver, BC, Canada A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases including cancer. We demonstrate here that minute epithelial specimens obtained by bronchial brushings afford reliable profiling by serial analysis of gene expression (SAGE) leading to lung gene discovery. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based upon a vast data set of nearly two million sequence tags from 21 SAGE libraries generated from individuals with a history of smoking. Cluster and linear regression analysis demonstrate the repeatability and reproducibility of bronchial SAGE libraries, and suggest that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. This distinction is highlighted by the abundant expression of signature genes that reflect tissue-specific and region-specific functions. Through our analysis we have identified novel bronchial-enriched genes and a novel transcript variant for surfactant, pulmonary-associated protein B in lung parenchyma. Conspicuously, gene expression associated with ciliogenesis is evident in bronchial epithelium. Additionally, it is noted that a large number of unmapped tags awaits further investigation. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, constituting one of the largest human SAGE studies reported to date. Keywords: lung parenchyma 2 normal lung parenchyma SAGE libraries, generated from 2 pools of 4 individuals each

Project description:2 normal lung parenchyma SAGE libraries, generated from 2 pools of 4 individuals each Discussed in the study: IDENTIFICATION OF NOVEL LUNG GENES IN BRONCHIAL EPITHELIUM BY SERIAL ANALYSIS OF GENE EXPRESSION Kim M. Lonergan1, Raj Chari1, Ronald J. deLeeuw1, Ashleen Shadeo1, Bryan Chi1, Ming-Sound Tsao2, Steven Jones3, Marco Marra3, Victor Ling1, Raymond Ng1,4, Calum MacAulay5, Stephen Lam5 and Wan L. Lam1 From the 1Department of Cancer Genetics & Developmental Biology, 5Department of Cancer Imaging, 3Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, BC, Canada, 2Ontario Cancer Institute / Princess Margaret Hospital, Toronto, ON, Canada, 4the Department of Computer Science, University of British Columbia, Vancouver, BC, Canada A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases including cancer. We demonstrate here that minute epithelial specimens obtained by bronchial brushings afford reliable profiling by serial analysis of gene expression (SAGE) leading to lung gene discovery. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based upon a vast data set of nearly two million sequence tags from 21 SAGE libraries generated from individuals with a history of smoking. Cluster and linear regression analysis demonstrate the repeatability and reproducibility of bronchial SAGE libraries, and suggest that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. This distinction is highlighted by the abundant expression of signature genes that reflect tissue-specific and region-specific functions. Through our analysis we have identified novel bronchial-enriched genes and a novel transcript variant for surfactant, pulmonary-associated protein B in lung parenchyma. Conspicuously, gene expression associated with ciliogenesis is evident in bronchial epithelium. Additionally, it is noted that a large number of unmapped tags awaits further investigation. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, constituting one of the largest human SAGE studies reported to date. Keywords: lung parenchyma

Dataset Information

Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.

Publications

Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.

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