Skip to main content
Published in: European Radiology 1/2024

Open Access 08-08-2023 | Chronic Obstructive Lung Disease | Computed Tomography

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction

Authors: Mats Lidén, Antoine Spahr, Ola Hjelmgren, Simone Bendazzoli, Josefin Sundh, Magnus Sköld, Göran Bergström, Chunliang Wang, Per Thunberg

Published in: European Radiology | Issue 1/2024

Login to get access



Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists’ visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method.

Materials and methods

Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists’ visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson’s correlation coefficients and receiver operating characteristics (ROC) analysis.


SWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r =  − 0.69 vs. r =  − 0.49/r =  − 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r =  − 0.77 vs. r =  − 0.69/r =  − 0.65, p = 0.03/p = 0.002), and in the random sample (r =  − 0.39 vs. r =  − 0.26/r =  − 0.25, p = 0.001/p = 0.007).


The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists’ visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists’ visual scores.

Clinical relevance statement

The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores.

Key Points

A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images.
SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950).
SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.
Available only for authorised users
Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction
Mats Lidén
Antoine Spahr
Ola Hjelmgren
Simone Bendazzoli
Josefin Sundh
Magnus Sköld
Göran Bergström
Chunliang Wang
Per Thunberg
Publication date
Springer Berlin Heidelberg
Published in
European Radiology / Issue 1/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084

Other articles of this Issue 1/2024

European Radiology 1/2024 Go to the issue