Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Objective
We developed and validated radiomics models for lung cancer prediction in indeterminate pulmonary nodules that retain performance in heterogenous data from multiple centres.
Materials and methods
The retrospective Lung Imaging Biobank for Radiomics and AI (LIBRA) study recruited 1,026 patients (2,071 computed tomography scans) with 5-to-30-mm lung nodules from seven UK hospitals (01/07/2020–30/09/2022). Benign and malignant nodules were segmented for feature extraction using PyRadiomics. Principal component analysis reduced feature dimensionality to twenty components. Logistic regression models using ComBat correction, fixed effects or random effects to mitigate for disparate data across study centres were compared. Models were fit to the whole dataset and evaluated using 10-fold cross-validation. Internal-external validation assessed between-centre heterogeneity. Models were benchmarked against volume, Brock score and scrambled voxels. Calibration was assessed using calibration curves. Decision-curve analysis was used to assess net benefit.
Results
Between-centre discrimination variability was observed with an area under the curve (AUC) ranging 0.75–0.91. The mixed-effects model provided better discrimination over ComBat or fixed effects alone (AUC 0.91 (95% confidence interval [CI]: 0.90–0.93) versus 0.75 (95% CI: 0.72–0.78) and 0.88 (95% CI: 0.86–0.90), respectively (p < 0.0001 and 0.003, respectively) in cross-validation. Radiomics features retained significance in multivariable models including established clinical features. The final model was superior to models developed using volume alone, Brock score, and scrambled-voxels. The model was well calibrated (intercept -0.036, slope 0.89 with net benefit compared to volume or Brock score alone.
Conclusion
Radiomics models show between-centre heterogeneity, with resilience best achieved with mixed-effects models. The final model had high performance with net benefit over established risk models.
We developed and validated radiomics models for lung cancer prediction in indeterminate pulmonary nodules that retain performance in heterogenous data from multiple centres.
Materials and methods
The retrospective Lung Imaging Biobank for Radiomics and AI (LIBRA) study recruited 1,026 patients (2,071 computed tomography scans) with 5-to-30-mm lung nodules from seven UK hospitals (01/07/2020–30/09/2022). Benign and malignant nodules were segmented for feature extraction using PyRadiomics. Principal component analysis reduced feature dimensionality to twenty components. Logistic regression models using ComBat correction, fixed effects or random effects to mitigate for disparate data across study centres were compared. Models were fit to the whole dataset and evaluated using 10-fold cross-validation. Internal-external validation assessed between-centre heterogeneity. Models were benchmarked against volume, Brock score and scrambled voxels. Calibration was assessed using calibration curves. Decision-curve analysis was used to assess net benefit.
Results
Between-centre discrimination variability was observed with an area under the curve (AUC) ranging 0.75–0.91. The mixed-effects model provided better discrimination over ComBat or fixed effects alone (AUC 0.91 (95% confidence interval [CI]: 0.90–0.93) versus 0.75 (95% CI: 0.72–0.78) and 0.88 (95% CI: 0.86–0.90), respectively (p < 0.0001 and 0.003, respectively) in cross-validation. Radiomics features retained significance in multivariable models including established clinical features. The final model was superior to models developed using volume alone, Brock score, and scrambled-voxels. The model was well calibrated (intercept -0.036, slope 0.89 with net benefit compared to volume or Brock score alone.
Conclusion
Radiomics models show between-centre heterogeneity, with resilience best achieved with mixed-effects models. The final model had high performance with net benefit over established risk models.
Date Issued
2026-12-01
Date Acceptance
2026-07-15
Citation
European Radiology Experimental, 2026, 10 (1)
ISSN
2509-9280
Publisher
SpringerOpen
Journal / Book Title
European Radiology Experimental
Volume
10
Issue
1
Copyright Statement
© The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1186/s41747-026-00791-2
Subjects
Early detection of cancer
Lung neoplasms
Machine learning
Radiomics
Tomography
Publication Status
Published
Article Number
ARTN 149
Date Publish Online
2026-10-05