Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results

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. Key Points Question Can heterogeneous data from multiple centres be used to predict lung nodule malignancy risk? Findings Radiomics cancer prediction models must account for heterogeneity to maintain performance between different centres. The mixed-effects model achieved AUC 0.91 (cross-validation) for cancer prediction. Relevance statement The model provided net benefit for nodule investigation over Brock score and volume alone and could assist in decision-making following prospective validation.