Lobar analysis of classical and deep learning radiomic features in chronic airflow limitation
5 affiliations
- 1Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- 2Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
- 3Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden
- 4Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
- 5Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
Abstract
Background: Quantitative chest computed tomography (CT) enables detailed assessment of structural lung abnormalities in chronic airflow limitation (CAL). We evaluated whether lobe-based classical radiomics and frozen deep learning transfer features from inspiratory and expiratory CT improved discrimination of CAL compared with handcrafted quantitative measures.
Methods: In this population-based study, 2,579 participants underwent inspiratory and expiratory CT and post-bronchodilator spirometry. CAL, defined as post-bronchodilator FEV₁/FVC < 0.70, was the primary modeling endpoint and was present in 430 participants; 2,149 participants without CAL served as controls. Lung lobes were segmented using nnU-Net. Classical radiomic features were extracted with PyRadiomics, and deep learning transfer features were obtained from frozen 3D U-Net and SegResNet encoders pretrained on the non-lung AbdomenAtlas dataset. A previously developed handcrafted feature set was included as a reference. Elastic-net regularized logistic regression with 10-fold cross-validation was used for classification, and performance was assessed by the area under the receiver operating characteristic curve (AUC).
Results: Models based on expiratory features outperformed inspiratory-only models (AUC improvement 0.03–0.08). The best performance was achieved by classical radiomics combining inspiratory and expiratory features (AUC 0.87; positive predictive value 49%; negative predictive value 95%), exceeding the handcrafted model (AUC 0.81; PPV 34%; NPV 94%). Frozen transfer features from the non-lung-pretrained encoders showed slightly lower discrimination than classical radiomics, and combining feature types did not improve performance.
Conclusion: Lobe-based classical radiomics from combined inspiratory and expiratory CT improved cross-validated discrimination of spirometry-defined CAL in this population-based cohort, with expiratory imaging contributing substantially to optimal performance.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12880-026-03020-9.
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BibTeX
@article{backlin2026lobar,
title = {{Lobar analysis of classical and deep learning radiomic features in chronic airflow limitation}},
author = {Emelie Bäcklin and Birgitta Janerot-Sjoberg and Örjan Smedby and Magnus Sköld and Eva Breznik},
journal = {BMC Medical Imaging},
year = {2026},
month = oct,
doi = {10.1186/s12880-026-03020-9},
url = {https://doi.org/10.1186/s12880-026-03020-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Lobar analysis of classical and deep learning radiomic features in chronic airflow limitation
AU - Emelie Bäcklin
AU - Birgitta Janerot-Sjoberg
AU - Örjan Smedby
AU - Magnus Sköld
AU - Eva Breznik
PY - 2026
DA - 2026/10/31
JO - BMC Medical Imaging
DO - 10.1186/s12880-026-03020-9
UR - https://doi.org/10.1186/s12880-026-03020-9
AB - Background: Quantitative chest computed tomography (CT) enables detailed assessment of structural lung abnormalities in chronic airflow limitation (CAL). We evaluated whether lobe-based classical radiomics and frozen deep learning transfer features from inspiratory and expiratory CT improved discrimination of CAL compared with handcrafted quantitative measures. Methods: In this population-based study, 2,579 participants underwent inspiratory and expiratory CT and post-bronchodilator spirometry. CAL, defined as post-bronchodilator FEV₁/FVC < 0.70, was the primary modeling endpoint and was present in 430 participants; 2,149 participants without CAL served as controls. Lung lobes were segmented using nnU-Net. Classical radiomic features were extracted with PyRadiomics, and deep learning transfer features were obtained from frozen 3D U-Net and SegResNet encoders pretrained on the non-lung AbdomenAtlas dataset. A previously developed handcrafted feature set was included as a reference. Elastic-net regularized logistic regression with 10-fold cross-validation was used for classification, and performance was assessed by the area under the receiver operating characteristic curve (AUC). Results: Models based on expiratory features outperformed inspiratory-only models (AUC improvement 0.03–0.08). The best performance was achieved by classical radiomics combining inspiratory and expiratory features (AUC 0.87; positive predictive value 49%; negative predictive value 95%), exceeding the handcrafted model (AUC 0.81; PPV 34%; NPV 94%). Frozen transfer features from the non-lung-pretrained encoders showed slightly lower discrimination than classical radiomics, and combining feature types did not improve performance. Conclusion: Lobe-based classical radiomics from combined inspiratory and expiratory CT improved cross-validated discrimination of spirometry-defined CAL in this population-based cohort, with expiratory imaging contributing substantially to optimal performance. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12880-026-03020-9.
SN - 1471-2342
ER -