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Updated: Aug 21, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Automated identification of interstitial lung abnormalities in lung screening using quantitative CT
Niamh Logan1, Sujal R Desai1,2, Emily C Bartlett1,2
1Royal Brompton and Harefield Hospitals, London, UK.
Objectives:
Interstitial lung abnormalities (ILAs) are an important incidental finding in lung screening. Quantitative CT (qCT) offers a promising approach to standardising ILA assessment; however, its adoption into routine clinical practice remains limited, largely due to the need for further clinical validation.
Setting:
We evaluated the performance of e-Lung (Brainomix), a commercially available qCT tool in assessing ILAs for participants attending a lung cancer screening programme.
Participants:
All participants invited to attend the West London lung cancer screening pilot programme between 2018 and 2020.
Primary Outcome Measures:
To define the optimal qCT biomarker thresholds that identified ILA as defined by expert thoracic radiologists.
Results:
e-Lung qCT biomarkers had an area under the curve of between 0.82 and 0.88, and between 0.84 and 0.87 for visually quantified ILA extent thresholds of at least 5% and >10%, respectively.
Conclusion:
e-Lung is a qCT tool with potential utility in the automated identification of ILA in participants undergoing lung screening.
